building-the-ai-bank-of-the-future.pdf

Global Banking Practice

Building the AI bank

of the future

May 2021

Global Banking Practice

Building the AI bank of

the future

To thrive in the AI-powered digital age, banks will need an AI-and-analytics

capability stack that delivers intelligent, personalized solutions and

distinctive experiences at scale in real time.

May 2021

Contents

AI bank of the future: Can banks meet the

AI challenge?

Artificial intelligence technologies are increasingly

integral to the world we live in, and banks need

to deploy these technologies at scale to remain

relevant. Success requires a holistic transformation

spanning multiple layers of the organization.

Reimagining customer engagement for the

AI bank of the future

Banks can meet rising customer expectations by

applying AI to offer intelligent propositions and smart

servicing that can seamlessly embed in partner

ecosystems.

AI-powered decision making for the bank of

the future

Banks are already strengthening customer relationships

and lowering costs by using artificial intelligence to

guide customer engagement. Success requires that

capability stacks include the right decisioning elements.

Beyond digital transformations: Modernizing

core technology for the AI bank of the future

For artificial intelligence to deliver value across the

organization, banks need core technology that is scalable,

resilient, and adaptable. Building that requires changes in

six key areas.

Platform operating model for the AI bank of

the future

Technology alone cannot define a successful AI bank;

the AI bank of the future also needs an operating

model that brings together the right talent, culture, and

organizational design.

Banking is at a pivotal moment. Technology

disruption and consumer shifts are laying the basis

for a new S-curve for banking business models,

and the COVID19 pandemic has accelerated

these trends. Building upon this momentum,

the advancement of artificial-intelligence (AI)

technologies within financial services offers banks

the potential to increase revenue at lower cost by

engaging and serving customers in radically new

ways, using a new business model we call “the AI

bank of the future.” The articles collected here

outline key milestones on a path we believe can lead

banks to deeper customer relationships, expanded

market share, and stronger financial performance.

The opportunity for a new business model comes as

banks face daunting challenges on multiple fronts.

In capital markets, many banks trade at a 50 percent

discount to book, and approximately three-quarters

of banks globally earn returns on equity that do not

cover their cost of equity.¹ Traditional banks also

face diverse competitive threats from neobanks and

nonbank challengers. Leading financial institutions

are already leveraging AI for split-second loan

approvals, biometric authentication, and virtual

assistants, to name just a few examples. Fintech

and other digital-commerce innovators are steadily

disintermediating banks from crucial aspects of

customer relationships, and large tech companies

are incorporating payments and, in some cases,

lending capabilities to attract more users with

an ever-broader range of services. Further, as

customers conduct a growing share of their daily

transactions through digital channels, they are

becoming accustomed to the ease, speed, and

personalized service offered by digital natives, and

their expectations of banks are rising.

To compete and thrive in this challenging

environment, traditional banks will need to build a

new value proposition founded upon leading-edge

AI-and-analytics capabilities. They must become

“AI first” in their strategy and operations. Many bank

leaders recognize that the economies of scale

afforded to organizations that efficiently deploy AI

technologies will compel incumbents to strengthen

customer engagement each day with distinctive

experiences and superior value propositions. This

value begins with intelligent, highly personalized

offers and extends to smart services, streamlined

omnichannel journeys, and seamless embedding

of trusted bank functionality within partner

ecosystems. From the customer’s point of view,

these are key features of an AI bank.

The building blocks of an AI bank

Our goal in this compendium is to give banking

leaders an end-to-end view of an AI bank’s full stack

capabilities and examine how these capabilities

cut across four layers: engagement, AI-powered

decision making, core technology and data

infrastructure, and a platform-based operating

model.

In our first article, “AI-bank of the future: Can banks

meet the challenge?” we take a closer look at the

trends and challenges leading banks to take an

AI-first approach as they define their core value

proposition. We continue by considering a day in the

life of a retail consumer and small-business owner

transacting with an AI bank. Then we summarize the

requirements for each layer of the AI-and-analytics

capability stack.

The second article, “Reimagining customer

engagement for the AI bank of the future,” examines

the capabilities that enable a bank to provide

customers with intelligent offers, personalized

solutions, and smart servicing within omnichannel

journeys across bank-owned platforms and partner

ecosystems.

In our third article, “AI-powered decision making for

the bank of the future,” we examine how machine-

learning models can significantly enhance customer

“A test of resilience: Banking through the crisis, and beyond,” Global Banking Annual Review, December 2020, McKinsey.com.

Introduction

2 Building the AI bank of the future

experiences and bank productivity, and we outline

the steps banks can follow to build the architecture

required to generate real-time analytical insights and

translate them into messages addressing precise

customer needs.

The fourth article, “Beyond digital transformations:

Modernizing core technology for the AI bank of

the future,” discusses the key elements required

for the backbone of the capability stack, including

automated cloud provisioning and an API and

streaming architecture to enable continuous,

secure data exchange between the centralized data

infrastructure and the decisioning and engagement

layers.

As we discuss in our final article, “Platform operating

model for the AI bank of the future,” deploying these

AI-and-analytics capabilities efficiently at scale

requires cross-functional business-technology

platforms comprising agile teams and new

technology talent.

Starting the journey

To get started on the transformation, bank leaders

should formulate the organization’s strategic goals

for the AI-enabled digital age and evaluate how AI

technologies can support these goals.

Once bank leaders have established their AI-first

vision, they will need to chart a road map detailing

the discrete steps for modernizing enterprise

technology and streamlining the end-to-end stack.

Joint business-technology owners of customer-

facing solutions should assess the potential of

emerging technologies to meet precise customer

needs and prioritize technology initiatives with the

greatest potential impact on customer experience

and value for the bank. We also recommend that

banks consider leveraging partnerships for non-

differentiating capabilities while devoting capital

resources to in-house development of capabilities

that set the bank apart from the competition.

Building the AI bank of the future will allow

institutions to innovate faster, compete with digital

natives in building deeper customer relationships

at scale, and achieve sustainable increases in

profits and valuations in this new age. We hope

the following articles will help banks establish their

vision and craft a road map for the journey.

Renny Thomas

Senior Partner

McKinsey & Company

3Building the AI bank of the future

Global Banking & Securities

AI bank of the future: Can

banks meet the AI challenge?

Artificial intelligence technologies are increasingly integral to the world we

live in, and banks need to deploy these technologies at scale to remain

relevant. Success requires a holistic transformation spanning multiple layers

of the organization.

September 2020

by Suparna Biswas, Brant Carson, Violet Chung, Shwaitang Singh, and Renny Thomas

In 2016, AlphaGo, a machine, defeated 18-time

world champion Lee Sedol at the game of

Go, a complex board game requiring intuition,

imagination, and strategic thinking—abilities

long considered distinctly human. Since then,

artificial intelligence (AI) technologies have

advanced even further,¹ and their transformative

impact is increasingly evident across

industries. AI-powered machines are tailoring

recommendations of digital content to individual

tastes and preferences, designing clothing

lines for fashion retailers, and even beginning to

surpass experienced doctors in detecting signs of

cancer. For global banking, McKinsey estimates

that AI technologies could potentially deliver up to

$1 trillion of additional value each year.²

Many banks, however, have struggled to move

from experimentation around select use cases to

scaling AI technologies across the organization.

Reasons include the lack of a clear strategy for AI,

an inflexible and investment-starved technology

core, fragmented data assets, and outmoded

operating models that hamper collaboration

between business and technology teams. What

is more, several trends in digital engagement

have accelerated during the COVID19 pandemic,

and big-tech companies are looking to enter

financial services as the next adjacency. To

compete successfully and thrive, incumbent

banks must become “AI-first” institutions,

adopting AI technologies as the foundation for

new value propositions and distinctive customer

experiences.

In this article, we propose answers to four

questions that can help leaders articulate a clear

vision and develop a road map for becoming an

AI-first bank:

1. Why must banks become AI first?

2. What might the AI bank of the future look like?

3. What obstacles prevent banks from deploying

AI capabilities at scale?

4. How can banks transform to become AI first?

1. Why must banks become AI first?

Over several decades, banks have continually

adapted the latest technology innovations to

redefine how customers interact with them. Banks

introduced ATMs in the 1960s and electronic,

card-based payments in the ’70s. The 2000s saw

broad adoption of 24/7 online banking, followed

by the spread of mobile-based “banking on the go”

in the 2010s.

Few would disagree that we’re now in the

AI-powered digital age, facilitated by falling costs

for data storage and processing, increasing

access and connectivity for all, and rapid

advances in AI technologies. These technologies

can lead to higher automation and, when deployed

after controlling for risks, can often improve upon

human decision making in terms of both speed

and accuracy. The potential for value creation

is one of the largest across industries, as AI can

potentially unlock $1 trillion of incremental value

for banks, annually (Exhibit 1).

Across more than 25 use cases,³ AI technologies

can help boost revenues through increased

personalization of services to customers (and

employees); lower costs through efficiencies

generated by higher automation, reduced errors

rates, and better resource utilization; and uncover

new and previously unrealized opportunities

based on an improved ability to process and

generate insights from vast troves of data.

More broadly, disruptive AI technologies can

dramatically improve banks’ ability to achieve

four key outcomes: higher profits, at-scale

personalization, distinctive omnichannel

AI can be defined as the ability of a machine to perform cognitive functions associated with human minds (e.g., perceiving, reasoning, learning, and

problem solving). It includes various capabilities, such as machine learning, facial recognition, computer vision, smart robotics, virtual agents, and

autonomous vehicles. See “Global AI Survey: AI proves its worth, but few scale impact,” November 2019, McKinsey.com.

“The executive’s AI playbook,” McKinsey.com.

For an interactive view, visit: www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/the-executives-ai-

playbook?page=industries/banking/

5 AI bank of the future: Can banks meet the AI challenge?

experiences, and rapid innovation cycles. Banks

that fail to make AI central to their core strategy

and operations—what we refer to as becoming

“AI-first”—will risk being overtaken by competition

and deserted by their customers. This risk is

further accentuated by four current trends:

— Rising customer expectations as adoption

of digital banking increases. In the first few

months of the COVID19 pandemic, use of

online and mobile banking channels across

countries has increased by an estimated 20

to 50 percent and is expected to continue at

this higher level once the pandemic subsides.

Across diverse global markets, between 15 and

45 percent of consumers expect to cut back

on branch visits following the end of the crisis.

As consumers increase their use of digital

banking services, they grow to expect more,

particularly when compared to the standards

they are accustomed to from leading consumer-

internet companies. Meanwhile, these digital

experience leaders continuously raise the bar

on personalization, to the point where they

sometimes anticipate customer needs before

the customer is aware of them, and offer highly-

tailored services at the right time, through the

right channel.

— Leading financial institutions’ use of advanced

AI technologies is steadily increasing. Nearly

60 percent of financial-services sector

respondents in McKinsey’s Global AI Survey

report that their companies have embedded

Exhibit 1

Potential annual value of AI and analytics for global banking could reach as high as

$1 trillion.

Total potential annual value, $ billion

Source: "The executive's AI playbook," McKinsey.com. (See "Banking," under "Value & Assess.")

Potential annual value of AI and analytics for global banking could reach as

high as $1 trillion.

1,022.4 (15.4% of sales)

Traditional AI

and analytics

Advanced AI

660.9

361.5

% of value driven by advanced AI, by function

100

Marketing and sales: 624.8

363.8 261.1

Risk: 372.9

288.6 84.3

HR: 14.2

8.6 5.7

Finance and IT: 8.0

0.0 8.0

Other operations: $2.4 B

0.0 2.4

John Euart, Nuno Ferreira, Jonathan Gordon, Ajay Gupta, Atakan Hilal, Olivia White, “A global view of financial life during COVID19—an update,”

July 2020, McKinsey.com.

Arif Cam, Michael Chui, Bryce Hall, “Global AI Survey: AI proves its worth, but few scale impact,” November 2019, McKinsey.com.

6AI bank of the future: Can banks meet the AI challenge?

at least one AI capability. The most commonly

used AI technologies are: robotic process

automation (36 percent) for structured

operational tasks; virtual assistants or

conversational interfaces (32 percent ) for

customer service divisions; and machine

learning techniques (25 percent) to detect

fraud and support underwriting and risk

management. While for many financial services

firms, the use of AI is episodic and focused on

specific use cases, an increasing number of

banking leaders are taking a comprehensive

approach to deploying advanced AI, and

embedding it across the full lifecycle, from the

front- to the back-office (Exhibit 2).

— Digital ecosystems are disintermediating

traditional financial services. By enabling

access to a diverse set of services through

a common access point, digital ecosystems

have transformed the way consumers discover,

evaluate, and purchase goods and services.

For example, WeChat users in China can use

the same app not only to exchange messages,

but also to book a cab, order food, schedule

a massage, play games, send money to a

contact, and access a personal line of credit.

Similarly, across countries, nonbanking

businesses and “super apps” are embedding

financial services and products in their

journeys, delivering compelling experiences

for customers, and disrupting traditional

methods for discovering banking products and

services. As a result, banks will need to rethink

how they participate in digital ecosystems,

and use AI to harness the full power of data

available from these new sources.

— Technology giants are entering financial

services as the next adjacency to their

core business models. Globally, leading

technology giants have built extraordinary

market advantages: a large and engaged

customer network; troves of data, enabling a

robust and increasingly precise understanding

of individual customers; natural strengths

in developing and scaling innovative

technologies (including AI); and access to

Exhibit 2

Banks are expanding their use of AI technologies to improve customer

experiences and back-oce processes.

Web <year>

Exhibit <x> of <y>

Banks are expanding their use of AI technologies to improve customer

experiences and back-oce processes.

Front oce

Back oce

Smile-to-pay facial scanning

to initiate transaction

Micro-expression analysis

with virtual loan ocers

Biometrics (voice, video,

print) to authenticate and

authorize

Machine learning to detect

fraud patterns,

cybersecurity attacks

Conversational bots for

basic servicing requests

Humanoid robots in branches

to serve customers

Machine vision and natural-

language processing to scan

and process documents

Real-time transaction

analysis for risk monitoring

7 AI bank of the future: Can banks meet the AI challenge?

low-cost capital. In the past, tech giants have

aggressively entered into adjacent businesses

in search of new revenue streams and to

keep customers engaged with a fresh stream

of offerings. Big-tech players have already

gained a foothold in financial services in select

domains (especially in payments and, in some

cases, lending and insurance), and they may

soon look to press their advantages to deepen

their presence and build greater scale.

2. What might the AI bank of the

future look like?

To meet customers’ rising expectations and

beat competitive threats in the AI-powered

digital era, the AI-first bank will offer propositions

and experiences that are intelligent (that

is, recommending actions, anticipating and

automating key decisions or tasks), personalized

(that is, relevant and timely, and based on a

detailed understanding of customers’ past

behavior and context), and truly omnichannel

(seamlessly spanning the physical and online

contexts across multiple devices, and delivering

a consistent experience) and that blend banking

capabilities with relevant products and services

beyond banking. Exhibit 3 illustrates how such a

bank could engage a retail customer throughout

the day. Exhibit 4 shows an example of the banking

experience of a small-business owner or the

treasurer of a medium-size enterprise.

Exhibit 3

How AI transforms banking for a retail customer.

Name: Anya

Age: 28 years

Occupation: Working professional

Personalized Omnichannel Banking and beyond bankingIntelligent

Seamless

integration with

nonbanking apps

Bank app

recognizes Anya's

spending patterns

and suggests

coee at nearby

cafes

Facial recognition

for frictionless

payment

Anya uses smile-

to-pay to

initiate payment

Analytics-

backed

personalized oers

Anya gets 2% o

on health

insurance

premiums based

on her gym

activity and

sleep habits

Personalized

money-management

solutions

App oers money-

management and

savings solutions,

prioritizes card

payments

Aggregated

overview of daily

activities

Anya receives

end-of-day

overview of her

activities, with

augmented reality,

and reminders to

pay bills

Savings and

investment recom-

mendations

Anya receives

integrated portfolio

view and a set of

actions with the

potential to

augment returns

8AI bank of the future: Can banks meet the AI challenge?

Internally, the AI-first institution will be optimized

for operational efficiency through extreme

automation of manual tasks (a “zero-ops” mindset)

and the replacement or augmentation of human

decisions by advanced diagnostic engines in

diverse areas of bank operations. These gains

in operational performance will flow from broad

application of traditional and leading-edge AI

technologies, such as machine learning and

facial recognition, to analyze large and complex

reserves of customer data in (near) real time.

The AI-first bank of the future will also enjoy

the speed and agility that today characterize

digital-native companies. It will innovate

rapidly, launching new features in days or

weeks instead of months. It will collaborate

extensively with partners to deliver new

value propositions integrated seamlessly

across journeys, technology platforms, and

data sets.

Exhibit 4

How AI transforms banking for a small- or medium-size-enterprise customer.

Name: Dany

Age: 36 years

Occupation: Treasurer of a small manufacturing unit

Personalized Omnichannel Banking and beyond bankingIntelligent

Customized

lending solutions

Bank is integrated

with client

business

management

systems

Dany gets loan

oer based on

company projected

cash ows

Micro-expression

analysis to review loan

applications

Dany answers

short questionnaire;

app scans his facial

movements

Firm is credited

with funds after

application

approval

Seamless

inventory and receiv-

ables management

App suggests

items to reorder,

gives visual reports

on receivables

management

Dany receives

customized

solutions for

invoice discounting,

factoring, etc.

SME platform to

source suppliers

and buyers

Dany is assisted

in sourcing and

selecting the

right vendors

and partners

Beyond-

banking support

services

Dany gets prelled

tax documents to

review and

approve; les with

a single click

Serviced by an AI-

powered virtual

adviser

An AI-powered

virtual adviser

resolves queries

Dany seeks

professional advice

on a lending oer

9 AI bank of the future: Can banks meet the AI challenge?

3. What obstacles prevent banks from

deploying AI capabilities at scale?

Incumbent banks face two sets of objectives,

which on first glance appear to be at odds. On

the one hand, banks need to achieve the speed,

agility, and flexibility innate to a fintech. On the

other, they must continue managing the scale,

security standards, and regulatory requirements

of a traditional financial-services enterprise.

Despite billions of dollars spent on change-

the-bank technology initiatives each year, few

banks have succeeded in diffusing and scaling

AI technologies throughout the organization.

Among the obstacles hampering banks’ efforts,

the most common is the lack of a clear strategy

for AI. Two additional challenges for many

banks are, first, a weak core technology and data

backbone and, second, an outmoded operating

model and talent strategy.

Built for stability, banks’ core technology

systems have performed well, particularly in

supporting traditional payments and lending

operations. However, banks must resolve

several weaknesses inherent to legacy systems

before they can deploy AI technologies at scale

(Exhibit 5). First and foremost, these systems

often lack the capacity and flexibility required

to support the variable computing requirements,

data-processing needs, and real-time analysis

that closed-loop AI applications require. Core

systems are also difficult to change, and their

maintenance requires significant resources.

What is more, many banks’ data reserves are

fragmented across multiple silos (separate

business and technology teams), and analytics

efforts are focused narrowly on stand-alone use

cases. Without a centralized data backbone, it is

practically impossible to analyze the relevant data

and generate an intelligent recommendation or

offer at the right moment. If data constitute the

bank’s fundamental raw material, the data must be

governed and made available securely in a manner

that enables analysis of data from internal and

external sources at scale for millions of customers,

in (near) real time, at the “point of decision” across

the organization. Lastly, for various analytics and

advanced-AI models to scale, organizations need

a robust set of tools and standardized processes

to build, test, deploy, and monitor models, in a

repeatable and “industrial” way.

Banks’ traditional operating models further

impede their efforts to meet the need for

continuous innovation. Most traditional banks

are organized around distinct business lines,

with centralized technology and analytics

teams structured as cost centers. Business

owners define goals unilaterally, and alignment

with the enterprise’s technology and analytics

strategy (where it exists) is often weak or

inadequate. Siloed working teams and “waterfall”

implementation processes invariably lead

to delays, cost overruns, and suboptimal

performance. Additionally, organizations lack

a test-and-learn mindset and robust feedback

loops that promote rapid experimentation and

iterative improvement. Often unsatisfied with the

performance of past projects and experiments,

business executives tend to rely on third-party

technology providers for critical functionalities,

starving capabilities and talent that should ideally

be developed in-house to ensure competitive

differentiation.

Michael Chui, Sankalp Malhotra, “AI adoption advances, but foundational barriers remain,” November 2018, McKinsey.com.

“Closed loop” refers to the fact that the models’ intelligence is applied to incoming data in near real time, which in turn refines the content presented

to the user in near real time.

10AI bank of the future: Can banks meet the AI challenge?

Exhibit 5

Investments in core tech are critical to meet increasing demands for

scalability, exibility, and speed.

Application programming interface.

Investments in core tech are critical to meet increasing demands for

scalability, exibility, and speed.

Cloud

Data API1

Challenges How cloud computing can help

Core/legacy systems can’t scale suciently

(eg, 150+ transactions/second)

Signicant time, eort, and team sizes

required to maintain infrastructure

Long time required to provision environments

for development and testing (eg, 40+ days in

some cases)

Enables higher scalability, resilience of services and

platforms through virtualization of infrastructure

Reduces IT overhead, enables automation of several

infrastructure-management tasks, and allows development

teams to “self-serve”

Enables faster time to market; dramatically reduces time by

providing managed services (e., setting up new environments

in minutes vs days)

High error rates; poor refresh rates; lack of

golden source of truth

Hard to access in a timely fashion for various

use cases

Data trapped in silos across multiple units and

hard to integrate with external sources

Ensures high degree of accuracy and single source of truth

in a cost-eective manner

Enables timely and role-appropriate access for various use

cases (eg, regulatory, business intelligence at scale, advanced

analytics and machine learning, exploratory)

Enables a 360-degree view across the organization to enable

generation of deeper insights by decision-making algorithms

and models

Challenges How best-in-class data management can help

Longer time to market, limited reusability of

code and software across internal teams

Hard to partner or collaborate with external

partners; long time to integrate

Suboptimal user experience—hard to stitch

data and services across multiple functional

siloes for an integrated proposition

Challenges

How APIs can help

Promote reusability and accelerate development by enabling

access to granular services (internal and external)

Reduce complexity and enable faster collaboration with

external partners

Enhance customer experience by enabling timely access to

data and services across dierent teams; faster time to market

due to limited coordination, cross-team testing

11 AI bank of the future: Can banks meet the AI challenge?

4. How can banks transform to

become AI-first?

To overcome the challenges that limit

organization-wide deployment of AI

technologies, banks must take a holistic

approach. To become AI-first, banks must invest

in transforming capabilities across all four layers

of the integrated capability stack (Exhibit 6): the

engagement layer, the AI-powered decisioning

layer, the core technology and data layer, and the

operating model.

As we will explain, when these interdependent

layers work in unison, they enable a bank to

provide customers with distinctive omnichannel

experiences, support at-scale personalization,

and drive the rapid innovation cycles critical

to remaining competitive in today’s world.

Each layer has a unique role to play—under-

investment in a single layer creates a weak link

that can cripple the entire enterprise.

The following paragraphs explore some of the

changes banks will need to undertake in each

layer of this capability stack.

Layer 1: Reimagining the customer

engagement layer

Increasingly, customers expect their bank to be

present in their end-use journeys, know their

context and needs no matter where they interact

with the bank, and to enable a frictionless

experience. Numerous banking activities

(e.g., payments, certain types of lending) are

becoming invisible, as journeys often begin and

end on interfaces beyond the bank’s proprietary

platforms. For the bank to be ubiquitous in

customers’ lives, solving latent and emerging

needs while delivering intuitive omnichannel

experiences, banks will need to reimagine how

they engage with customers and undertake

several key shifts.

First, banks will need to move beyond highly

standardized products to create integrated

propositions that target “jobs to be done.” This

requires embedding personalization decisions

(what to offer, when to offer, which channel

to offer) in the core customer journeys and

designing value propositions that go beyond the

core banking product and include intelligence

that automates decisions and activities on

behalf of the customer. Further, banks should

strive to integrate relevant non-banking

products and services that, together with the

core banking product, comprehensively address

the customer end need. An illustration of the

“jobs-to-be-done” approach can be seen in the

way fintech Tally helps customers grapple with

the challenge of managing multiple credit cards.

The fintech’s customers can solve several pain

points—including decisions about which card to

pay first (tailored to the forecast of their monthly

income and expenses), when to pay, and how

much to pay (minimum balance versus retiring

principal)—a complex set of tasks that are often

not done well by customers themselves.

The second necessary shift is to embed

customer journeys seamlessly in partner

ecosystems and platforms, so that banks

engage customers at the point of end use and

in the process take advantage of partners’

data and channel platform to increase higher

engagement and usage. ICICI Bank in India

embedded basic banking services on WhatsApp

(a popular messaging platform in India) and

scaled up to one million users within three

months of launch. In a world where consumers

and businesses rely increasingly on digital

ecosystems, banks should decide on the

posture they would like to adopt across multiple

ecosystems—that is, to build, orchestrate, or

partner—and adapt the capabilities of their

engagement layer accordingly.

Clayton M. Christensen, Taddy Hall, Karen Dillon and David S. Duncan, “Know your customers ‘jobs to be done,” Harvard Business Review,

September 2016, hbr.org.

“ICICI Bank crosses 1 million users on WhatsApp platform,” Live Mint, July 7, 2020, livemint.com.

12AI bank of the future: Can banks meet the AI challenge?

Exhibit 6

To become an AI-rst institution, a bank must streamline its capability stack for

value creation.

To become an AI-rst institution, a bank must streamline its capability stack

for value creation.

Reimagined

engagement

AI-powered

decision

making

Core

technology

and data

Operating

model

AI bank of the future

Protability

Personalization

at scale

Omnichannel

experience

Speed and

innovation

Intelligent products,

tools, experiences

for customers and

employees

Within-bank channels and

journeys (eg, web, apps,

mobile, smart devices,

branches, Internet of Things)

Beyond-bank channels

and journeys (eg,

ecosystems, partners,

distributors)

Smart service and

operations

Digital marketing

Customer

acquisition

Credit

decision

making

Monitoring

and

collections

Retention

and cross-

selling,

upselling

Servicing

and

engagement

Natural-

language

process-

ing

Voice-

script

analysis

Virtual

agents,

bots

Computer

vision

Facial

recog-

nition

Block-

chain

Robotics

Behav-

ioral

analytics

Advanced

analytics

AI capabilities

Core technology

and data

A. Tech-forward strategy (in-house build of dierential capabilities

vs buying oerings; in-house talent plan)

B. Data

manage-

ment for

AI world

Platform operating

model

A. Autonomous business + tech teams

B. Agile way

of working

C. Remote

collaboration

D. Modern talent

strategy (hiring,

reskilling)

E. Culture and

capabilities

Value capture

C. Modern

API archi-

tecture

D. Intelligent

infrastructure

(AI operations

command,

hybrid cloud

setup, etc)

E. Hollow-

ing the

core (core

moderniza-

tion)

F. Cyber-

security

and

control

tiers

13 AI bank of the future: Can banks meet the AI challenge?

Third, banks will need to redesign overall

customer experiences and specific journeys for

omnichannel interaction. This involves allowing

customers to move across multiple modes (e.g.,

web, mobile app, branch, call center, smart

devices) seamlessly within a single journey

and retaining and continuously updating the

latest context of interaction. Leading consumer

internet companies with offline-to-online

business models have reshaped customer

expectations on this dimension. Some banks

are pushing ahead in the design of omnichannel

journeys, but most will need to catch up.

Reimagining the engagement layer of the

AI bank will require a clear strategy on how

to engage customers through channels

owned by non-bank partners. Banks will

need to adopt a design-thinking lens as they

build experiences within and beyond the

bank’s platform, engineering engagement

interfaces for flexibility to enable tailoring and

personalization for customers, reengineering

back-end processes, and ensuring that data-

capture funnels (e.g., clickstream) are granularly

embedded in the bank’s engagement layer. All

of this aims to provide a granular understanding

of journeys and enable continuous

improvement.

Layer 2: Building the AI-powered decision-

making layer

Delivering personalized messages and

decisions to millions of users and thousands

of employees, in (near) real time across the full

spectrum of engagement channels, will require

the bank to develop an at-scale AI-powered

decision-making layer. Across domains within

the bank, AI techniques can either fully replace

or augment human judgment to produce

significantly better outcomes (e.g., higher

accuracy and speed), enhanced experience

for customers (e.g., more personalized

interaction and offerings), actionable insights

for employees (e.g., which customer to contact

first with next-best-action recommendations),

and stronger risk management (e.g., earlier

detection of likelihood of default and

fraudulent activities).

To establish a robust AI-powered decision

layer, banks will need to shift from attempting

to develop specific use cases and point

solutions to an enterprise-wide road map for

deploying advanced-analytics (AA)/machine-

learning (ML) models across entire business

domains. As an illustration, in the domain of

unsecured consumer lending alone, more

than 20 decisions across the life cycle can be

automated.¹¹ To enable at-scale development

of decision models, banks need to make the

development process repeatable and thus

capable of delivering solutions effectively and

on-time. In addition to strong collaboration

between business teams and analytics

talent, this requires robust tools for model

development, efficient processes (e.g., for

re-using code across projects), and diffusion

of knowledge (e.g., repositories) across teams.

Beyond the at-scale development of decision

models across domains, the road map should

also include plans to embed AI in business-

as-usual process. Often underestimated,

this effort requires rewiring the business

processes in which these AA/AI models will be

embedded; making AI decisioning “explainable”

to end-users; and a change-management plan

that addresses employee mindset shifts and

skills gaps. To foster continuous improvement

beyond the first deployment, banks also

need to establish infrastructure (e.g., data

measurement) and processes (e.g., periodic

reviews of performance, risk management of AI

models) for feedback loops to flourish.

Additionally, banks will need to augment

homegrown AI models, with fast-evolving

capabilities (e.g., natural-language processing,

computer-vision techniques, AI agents

and bots, augmented or virtual reality) in

their core business processes. Many of

these leading-edge capabilities have the

Jennifer Kilian, Hugo Sarrazin, and Hyo Yeon, “Building a design-driven culture,” September 2015, McKinsey.com.

Renny Thomas, Vinayak HV, Raphael Bick, and Shwaitang Singh, “Ten lessons for building a winning retail and small-business digital lending

franchise,” November 2019, McKinsey.com.

14AI bank of the future: Can banks meet the AI challenge?

potential to bring a paradigm shift in customer

experience and/or operational efficiency. While

many banks may lack both the talent and the

requisite investment appetite to develop these

technologies themselves, they need at minimum

to be able to procure and integrate these

emerging capabilities from specialist providers

at rapid speed through an architecture enabled

by an application programming interface (API),

promote continuous experimentation with these

technologies in sandbox environments to test and

refine applications and evaluate potential risks,

and subsequently decide which technologies to

deploy at scale.

To deliver these decisions and capabilities and to

engage customers across the full life cycle, from

acquisition to upsell and cross-sell to retention

and win-back, banks will need to establish

enterprise-wide digital marketing machinery. This

machinery is critical for translating decisions and

insights generated in the decision-making layer

into a set of coordinated interventions delivered

through the bank’s engagement layer. This

machinery has several critical elements, which

include:

— Data-ingestion pipelines that capture a range

of data from multiple sources both within the

bank (e.g., clickstream data from apps) and

beyond (e.g., third-party partnerships with

telco providers)

— Data platforms that aggregate, develop, and

maintain a 360-degree view of customers and

enable AA/ML models to run and execute in

near real time

— Campaign platforms that track past actions

and coordinate forward-looking interventions

across the range of channels in the

engagement layer

Layer 3: Strengthening the core technology and

data infrastructure

Deploying AI capabilities across the organization

requires a scalable, resilient, and adaptable set

of core-technology components. A weak core-

technology backbone, starved of the investments

needed for modernization, can dramatically

reduce the effectiveness of the decision-making

and engagement layers.

The core-technology-and-data layer has six key

elements (Exhibit 7):

— Tech-forward strategy. Banks should have

a unified technology strategy that is tightly

aligned to business strategy and outlines

strategic choices on which elements, skill

sets, and talent the bank will keep in-house

and those it will source through partnerships

or vendor relationships. In addition, the

tech strategy needs to articulate how each

component of the target architecture will both

support the bank’s vision to be an AI-first

institution and interact with each layer of the

capability stack.

— Data management for the AI-enabled world.

The bank’s data management must ensure

data liquidity—that is, the ability to access,

ingest, and manipulate the data that serve as

the foundation for all insights and decisions

generated in the decision-making layer.

Data liquidity increases with the removal of

functional silos and allows multiple divisions

to operate off the same data, with increased

coordination. The data value chain begins with

seamless sourcing of data from all relevant

internal systems and external platforms. This

includes ingesting data into a lake, cleaning

and labeling the data required for diverse use

cases (e.g., regulatory reporting, business

intelligence at scale, AA/ML diagnostics),

segregating incoming data (from both existing

and prospective customers) to be made

available for immediate analysis from data to

be cleaned and labeled for future analysis.

Furthermore, as banks design and build their

centralized data-management infrastructure,

they should develop additional controls and

monitoring tools to ensure data security,

privacy, and regulatory compliance—for

example, timely and role-appropriate access

across the organization for various use cases.

15 AI bank of the future: Can banks meet the AI challenge?

— Modern API architecture. APIs are the

connective tissue enabling controlled access

to services, products, and data, both within

the bank and beyond. Within the bank, APIs

reduce the need for silos, increase reusability

of technology assets, and promote flexibility

in the technology architecture. Beyond the

bank, APIs accelerate the ability to partner

externally, unlock new business opportunities,

and enhance customer experiences. While

APIs can unlock significant value, it is critical to

start by defining where they are to be used and

establish centralized governance to support

their development and curation.¹²

— Intelligent infrastructure. As companies

in diverse industries increase the share of

workload handled on public and private

cloud infrastructure, there is ample evidence

that cloud-based platforms allow for the

higher scalability and resilience crucial to an

AI-first strategy.

Additionally, cloud-based

infrastructure reduces costs for IT maintenance

and enables self-serve models for development

teams, which enable rapid innovation cycles by

providing managed services (e.g., setting up new

environments in minutes instead of days).

Exhibit 7

The core-technology-and-data layer accommodates increasing use of the cloud

and reduction of legacy technology.

Application programming interface.

The core-technology-and-data layer accommodates increasing use of the

cloud and reduction of legacy technology.

Capabilities Our perspective

Tech-forward strategy

Data management for AI world

Modern API1 architecture

Intelligent infrastructure

Hollowing the core

Cybersecurity and control tiers

Build dierentiating capabilities in-house by augmenting the internal skill base;

carefully weigh options to buy, build, or compose modular architecture through

best-of-breed solutions

Upgrade data management and underlying architecture to support machine-learning

use cases at scale by leveraging cloud, streaming data, and real-time analytics

Leverage modern cloud-native tooling to enable a scalable API platform supporting

complex orchestrations while creating experience-enhancing integrations across

the ecosystem

Implement infrastructure as code across on-premises and cloud environments;

increase platform resiliency by adopting AIOps to support deep diagnostics, auto-

recoverability, and auto-scale

Distribute transaction processing across the enterprise stack; selectively identify

components that can be externalized to drive broader reuse, standardization, and

eciency

Implement robust cybersecurity in the hybrid infrastructure; secure data and

applications through zero-trust design principles and centralized command-and-

control centers

¹² Renny Thomas, Vinayak HV, Raphael Bick, and Shwaitang Singh, “Ten lessons for building a winning retail and small-business digital lending

franchise,” November 2019, McKinsey.com.

¹³ Arul Elumalai and Roger Roberts, “Unlocking business acceleration in a hybrid cloud world,” August 2019, McKinsey.com.

16AI bank of the future: Can banks meet the AI challenge?

Layer 4: Transitioning to the platform operating

model

The AI-first bank of the future will need a new

operating model for the organization, so it can

achieve the requisite agility and speed and

unleash value across the other layers. While

most banks are transitioning their technology

platforms and assets to become more modular

and flexible, working teams within the bank

continue to operate in functional silos under

suboptimal collaboration models and often lack

alignment of goals and priorities.

The platform operating model envisions cross-

functional business-and-technology teams

organized as a series of platforms within the bank.

Each platform team controls their own assets

(e.g., technology solutions, data, infrastructure),

budgets, key performance indicators, and

talent. In return, the team delivers a family of

products or services either to end customers of

the bank or to other platforms within the bank.

In the target state, the bank could end up with

three archetypes of platform teams. Business

platforms are customer- or partner-facing teams

dedicated to achieving business outcomes in

areas such as consumer lending, corporate

lending, and transaction banking. Enterprise

platforms deliver specialized capabilities and/

or shared services to establish standardization

throughout the organization in areas such as

collections, payment utilities, human resources,

and finance. And enabling platforms enable the

enterprise and business platforms to deliver

cross-cutting technical functionalities such as

cybersecurity and cloud architecture.

By integrating business and technology in

jointly owned platforms run by cross-functional

teams, banks can break up organizational silos,

increasing agility and speed and improving the

alignment of goals and priorities across the

enterprise.

The journey to becoming an AI-first bank entails

transforming capabilities across all four layers

of the capability stack. Ignoring challenges or

underinvesting in any layer will ripple through all,

resulting in a sub-optimal stack that is incapable

of delivering enterprise goals.

A practical way to get started is to evaluate

how the bank’s strategic goals (e.g., growth,

profitability, customer engagement, innovation)

can be materially enabled by the range of AI

technologies—and dovetailing AI goals with the

strategic goals of the bank. Once this alignment

is in place, bank leaders should conduct a

comprehensive diagnostic of the bank’s starting

position across the four layers, to identify areas

that need key shifts, additional investments

and new talent. They can then translate these

insights into a transformation roadmap that spans

business, technology, and analytics teams.

Equally important is the design of an execution

approach that is tailored to the organization. To

ensure sustainability of change, we recommend

a two-track approach that balances short-term

projects that deliver business value every quarter

with an iterative build of long-term institutional

capabilities. Furthermore, depending on their

market position, size, and aspirations, banks need

not build all capabilities themselves. They might

elect to keep differentiating core capabilities

in-house and acquire non-differentiating

capabilities from technology vendors and

partners, including AI specialists.

For many banks, ensuring adoption of AI

technologies across the enterprise is no longer

a choice, but a strategic imperative. Envisioning

and building the bank’s capabilities holistically

across the four layers will be critical to success.

Suparna Biswas is a partner, Shwaitang Singh is an associate partner, and Renny Thomas is a senior partner, all in McKinsey’s

Mumbai office. Brant Carson is a partner in the Sydney office, and Violet Chung is a partner in the Hong Kong office.

The authors would like to thank Milan Mitra, Anushi Shah, Arihant Kothari, and Yihong Wu for their contributions to this article.

17 AI bank of the future: Can banks meet the AI challenge?

Global Banking & Securities

Reimagining customer

engagement for the AI bank

of the future

Banks can meet rising customer expectations by applying AI to offer

intelligent propositions and smart servicing that can seamlessly embed

in partner ecosystems.

October 2020

by Violet Chung, Malcolm Gomes, Sailee Rane, Shwaitang Singh, and Renny Thomas

From instantaneous translation to

conversational interfaces, artificial-intelligence

(AI) technologies are making ever more evident

impacts on our lives. This is particularly true in

the financial-services sector, where challengers

are already launching disruptive AI-powered

innovations. To remain competitive, incumbent

banks must become “AI first” in vision and

execution, and as discussed in the previous

article, this means transforming the full

capability stack, including the engagement layer,

AI-powered decision making, core technology

and data infrastructure, and operating model.

If fully integrated, these capabilities can

strengthen engagement significantly, supporting

customers’ financial activities across diverse

online and physical contexts with intelligent,

highly personalized solutions delivered through

an interface that is intuitive, seamless, and fast.

These are the baseline expectations for an

AI bank.

In this article, we examine how banks can take

an AI-first approach to reimagining customer

engagement. We focus on three elements with

potential to give the bank a decisive competitive

edge:

1. The value of re-imagined customer

engagement: By reimagining customer

engagement, banks can unlock new value

through better efficiency, expanded market

access, and greater customer lifetime value.

2. Key elements of the re-imagined engagement

layer: The combination of intelligent propositions,

seamless embedding within partner ecosystems,

and smart servicing and experiences underpins

an overall experience that sets the AI bank apart

from traditional incumbents.

3. Integrated supporting capabilities: As banks

rethink and rebuild their engagement capabilities,

they need to leverage critical enablers, each

of which cuts across all four layers of the

capability stack.

The value of reimagined customer

engagement

In recent years, many financial institutions

have devoted significant capital to digital-and-

analytics transformations, aiming to improve

customer journeys across mobile and web

channels. Despite these big investments, most

banks still lag well behind consumer-tech

companies in their efforts to engage customers

with superior service and experiences.

The prevailing models for bank customer

acquisition and service delivery are beset by

missed cues: incumbents often fail to recognize

and decipher the signals customers leave

behind in their digital journeys.

Across sectors, however, leaders in delivering

positive experiences are not just making

their journeys easy to access and use but

also personalizing core journeys to match

an individual’s present context, direction of

movement, and aspiration.

Creating a superior experience can generate

significant value. A McKinsey survey of US

retail banking customers found that at the

banks with the highest degree of reported

customer satisfaction, deposits grew 84

percent faster than at the banks with the lowest

satisfaction ratings (Exhibit 1).

Superior experiences are not only a proven

foundation for growth but also a crucial means

of countering threats from new attackers. In

particular, three trends make it imperative for

banks to improve customer engagement:

1. Rising customer expectations. Accustomed

to the service standards set by consumer

internet companies, today’s customers

have come to expect the same degree of

consistency, convenience, and personalization

from their financial-services institutions. For

example, Netflix has been able to raise the

bar in customer experience by doing well

on three crucial attributes: consistency of

19 Reimagining customer engagement for the AI bank of the future

experience across channels (mobile app, laptop,

TV), convenient access to a vast reserve of

content with a single click, and recommendations

finely tailored to each profile within a single

account. Improving websites and online portals

for a seamless experience is one of the top three

areas where customers desire support from

banks.¹ Innovation leaders are already executing

transactions and loan approvals and resolving

service inquiries in near real time.

2. Disintermediation. Nonbank providers are

disintermediating banks from the most valuable

services, leaving less profitable links in the value

chain to traditional banks. Big-tech companies are

providing access to financial products within their

nonbanking ecosystems. Messaging app WeChat

allows users in China to make a payment within

the chat window. Google has partnered with eight

US banks to offer cobranded accounts that will be

mobile first and focus on creating an intuitive user

experience and new ways to manage money with

financial insights and budgeting tools.²

Beyond access, nonbank innovators are also

disintermediating parts of the value chain that

were once considered core capabilities of financial

institutions, including underwriting. Indian agtech

company Cropin uses advanced analytics and

machine learning to analyze historical data on

Exhibit 1

US retail banks with high customer satisfaction typically grow deposits faster.

Percentage of respondents that selected a 9 or 10 on a 10-point customer satisfaction scale. Question: “We would like to understand your experience with

[product] with (Bank). Overall, how satised or dissatised are you with [product] with [Bank]?” Banks were ranked based on average satisfaction scores

and then divided into quartiles.

Customer satisfaction score.

Source: McKinsey 2018 Retail Banking Customer Experience Benchmark Survey

US retail banks with high customer satisfaction typically grow deposits faster.

Real dierences in customer satisfaction1

CSAT (Percent of customers rating 9 or 10)

Leaders in customer satisfaction grow faster

Deposit CAGR (2014-17)

Top

quartile

3rd

quartile

2nd

quartile

Bottom

quartile

Top

quartile

CSAT

Bottom

quartile

CSAT

-26 pp

5.9

3.2

+84%

John Euart, Nuno Ferreira, Jonathan Gordon, Ajay Gupta, Atakan Hilal, and Olivia White, “Financial life during the COVID19 pandemic—an update,”

July 2020, McKinsey.com.

“Google to offer co-branded cards with 8 US banks” August 3, 2020, Finextra.com.

20Reimagining customer engagement for the AI bank of the future

crop performance, weather patterns, land usage,

and more to develop underwriting models that

predict a customer’s creditworthiness much more

accurately than traditional risk models.

3. Increasingly human-like formats.

Conversational interfaces are becoming the

new standard for customer engagement. With

approximately one third of adult Americans

owning a smart speaker,³ voice commands are

gaining traction, and adoption of both voice and

video interfaces will likely expand as in-person

interactions continue to decline. Several banks

have already launched voice-activated assistants,

including Bank of America with Erica and ICICI

bank in India with iPal.

If reimagined customer engagement is properly

aligned with the other layers of the AI-and-

analytics capability stack, it can strengthen

a bank’s competitive position and financial

performance by increasing efficiency, access

and scale, and customer lifetime value (Exhibit 2).

Key elements of the AI-first

engagement layer

For banks, successfully integrating core

personalization elements across the range

of touchpoints with customers will be critical

to deliver a superior experience and better

outcomes. The reimagined engagement layer

should provide the AI bank with a deeper and

Exhibit 2

With an AI-rst approach to customer engagement, banks have the

opportunity to reap gains in crucial areas.

Net promoter score.

Turn around time.

Source: McKinsey analysis

With an AI-rst approach to customer engagement, banks have the

opportunity to reap gains in crucial areas.

Increased access

and scale

Reduced cost of acquisition

(more cross-sell, partner

platform-led growth)

Lower cost to serve (less or

“zero” operations)

Lower risk (better data, early

warnings, proactive nudging)

Stronger

activation and usage of

existing products

Higher engagement (eg, monthly

usage), satisfaction (eg, NPS,

lower TAT) and reduced churn

Higher cross-sell of new products

Key

metrics

impacted

Higher

eciency

Higher

customer

lifetime value

Access to newer, previously untapped

customer segments

Higher speed to reach critical scale

Bret Kinsella, “Nearly 90 million U.S. adults have smart speakers, adoption now exceeds one-third of consumers,” April 28, 2020, voicebot.ai.

21 Reimagining customer engagement for the AI bank of the future

Exhibit 3

A reimagined engagement layer uses AI and advanced analytics and comprises

3 key elements.

The reimagined engagement layer uses AI and advanced analytics and

comprises 3 key elements.

Needs

Anticipate

customer needs

Behaviors

Products purchased online,

parts of purchase journey

that are digital, preferred

platforms

Preferences

Preferred channels,

best time to

contact, etc

Context

Life stage, upcoming

events, sources of

income, occupation, etc

Intelligent

propositions

that can

anticipate

and address

customers

needs and

preferences

Smart

servicing

facilitated

by fast,

simple, and

intuitive

interactions

Seamless

embedding

within

partner

ecosystems

Reimagined

engagement layer

Understanding customers

Embedding analytical

outcomes within journeys

more accurate understanding of each customer’s

context, behavior, needs, and preferences. This

understanding, in turn, enables the bank to

craft an intelligent, personalized offering. To

support this, banks need to analyze customer

data in real time and embed analytical outcomes

within customer journeys for fast execution

of customer transaction requests and service

queries, enabling instant fulfilment. These

two objectives should guide the design of the

engagement layer, which comprises three pillars:

Intelligent propositions, seamless embedding

within partner ecosystems, and smart service

and experiences (Exhibit 3).

Intelligent propositions

To craft and deliver intelligent propositions,

banks must take an entirely new approach to

innovation. First and foremost, they need to

free themselves from a product-centric view,

where they develop new products and features

and “push” them to customers through product

bundles and discounted pricing. Instead, they

should adopt a customer-centric view, which

starts with understanding customer needs.

Achieving this close alignment between bank

capabilities and customer needs requires time

and capital to develop a realistic, evidence-

based understanding of actual customers’

22Reimagining customer engagement for the AI bank of the future

time-critical needs. The capability to gauge

customers’ expressed needs and anticipate

latent needs in real time requires that AI and

analytics capabilities be integrated with diverse

core systems and delivery platforms across the

enterprise.

Customer propositions can no longer be static

and one-size-fits-all—they should be intelligent

and tailored, and go beyond banking to address

customer needs that may involve both banking

and non-banking products and services.

Across diverse markets, recent innovations

in messaging and financial-management

tools are already helping customers simplify

banking activities and improve their financial

position—for example, with fee-reduction

recommendations, budgeting tools, savings and

liquidity management, and planning tools to help

customers achieve their life goals.

— Fee reduction recommendations. Rapid

analysis of transaction history enables banks

to inform individual customers about their

potential to reduce fees. The mobile app

Empower highlights duplicate services and

high bills and suggests possible actions, such

as reducing the number of subscriptions or

negotiating for more competitive mobile-

phone fees, and recommends options for

reducing bank fees. (E.g., “You can potentially

reduce your telephone bill by 30 percent. We

can negotiate with your service provider on

your behalf and get you a better plan.”)

— Budgeting tools. Budgeting tools can help

customers improve financial discipline. Acorns,

for example, allows people to set budgets and

sends them alerts to help them stay on track

(“You have spent 75 percent of your dining limit

this week”). It also delivers reminders based on

past transactions (“You paid your credit card

bill on the 10th last month. Would you like to

pay now?”). Wally and Spendee automatically

allocate expenses to different categories

and show the proportion of monthly expense in

a particular category (e.g., dining out or fuel) in

comparison with the previous month’s spending.

— Planning for life goals. Finally, by integrating

systems across the enterprise, banks can analyze

relevant data to generate a comprehensive

view of a customer’s total inflows and outflows

and offer advice for balancing daily and annual

spending with wealth-building goals. Wealthfront,

a digital wealth-management tool, proposes an

investment plan to customers based on their

answers to a few questions. The process allows

customers to define their goals in practical terms,

such as learning how much to invest to buy a

home in five years, take a year off to travel next

year, or retire at 40. Chinese wealth-management

fintech Snowball offers a cross-platform app with

a Twitter-like feature that enables investors to

exchange investment ideas.

— Debt simplification: Some fintech companies

are helping customers who grapple with the

challenge of managing multiple credit cards. For

example, Fintech Tally helps solve a number of

pain points, and decisions such as which card

to pay first (based on a forecast of their monthly

income and expenses), when to pay, and how

much to pay (minimum balance vs. retiring

principal), while optimizing their credit scores.

Embedding in partner ecosystems

As banks design and offer intelligent propositions

they need to make them accessible not only on their

own platforms but also in other ecosystems that

their customers are part of. McKinsey research has

identified 12 distinct ecosystems that have begun

to form around end-to-end customer needs within

distinct service domains. We estimate that these

integrated networks will generate approximately

$60 trillion in global annual revenues by 2025.

Just a few years ago, the most prominent examples

were tech giants such as Alibaba, Baidu, and

WeChat in China, and Amazon, Facebook, and

Google in the United States. In the past two

Venkat Atluri, Miklós Dietz, and Nicolaus Henke, “Competing in a world of sectors without borders,” July, 2017, McKinsey.com.

23 Reimagining customer engagement for the AI bank of the future

years, however, both traditional companies and

tech start-ups have contributed to significant

expansion of ecosystem activity globally. Well-

established banks have led the formation of

digital ecosystems, often in one of five areas: B2C

commerce, housing, B2B services, transportation,

and wealth and protection. Examples include

RBC’s Ownr, a digital solution for entrepreneurs

launching a business, and DBS’s digital

marketplace for automobiles, electricity, housing,

and travel.

Ecosystem strategies. Financial institutions can

leverage their own and/or partner ecosystems to

create value in diverse ways, including increased

access, higher efficiencies, and stronger

offerings:

— Increased access and scale. By embedding

their services within ecosystems, banks have

the potential to access customer segments

beyond their traditional footprint and to scale

new solutions rapidly. For example, BBVA’s

Valora, a real estate and mortgage advisory

platform, is an important channel for customer

acquisition.

— Higher efficiencies. Participation in one or

several ecosystems typically leads to lower

customer acquisition costs, lower cost to serve,

and better credit risk management. In China,

for example, co-lending ecosystem partners

rely on advanced diagnostic models to analyze

ecosystem data to monitor potential changes

in borrowers’ risk profiles and to manage early-

stage collection in case of default.

— New value propositions. Deniz Bank has

launched Deniz Den, a platform for agricultural

consulting and financial services, supporting

farmers with timely information about

agricultural best practices and advice on small-

business finance and investments.

— More convenience. In India, SBI has launched

YONO, designed as a one-stop solution to

meet a broad range of a retail customers’

banking and nonbanking needs. It has more

than 100 merchants embedded in the online

marketplace, enabling customers to complete

diverse tasks, such as ordering groceries and

booking tickets, through a single app.

How to move forward. The gradual shift of

commercial activity toward digital ecosystems

has far-reaching implications for practically

every sector of the economy, and each financial-

services organization should build a detailed

strategy for competing in these new contexts.

At present, however, only a few banks have

successfully tapped the potential of ecosystems

to create value. To avoid common pitfalls

and maximize the value of their ecosystem

partnerships, banks need a clear ecosystem

strategy, end-to-end integration of internal

capabilities, and ways of working that are

compatible with technology partners’ methods.

Banks need a clear understanding of their

strengths, local context, and current customers,

which they should use to select an ecosystem

strategy that fits the organization’s ambition and

market position. These are top priorities for the

board and should not be left entirely to the chief

digital officer.

End-to-end integration of internal capabilities

is necessary to support real-time analytics and

messaging. From the collection and processing

of customer data to accurate customer-profile

analysis, banks must upgrade their technology

architecture and analytical capabilities. Further,

as discussed in the following section, they

should establish a consolidated, enterprise-wide

platform for managing customer data. They

should also establish robust links with partner

ecosystems to support instantaneous data

exchange.

Organizational culture and processes also

matter. The bank should work in a way that

matches the way technology partners work.

This typically entails changes in organizational

mindset and culture. One approach is to organize

Joydeep Sengupta, Vinayak HV, Violet Chung, et al., “The ecosystem playbook: Winning in a world of ecosystems,” April 2019, McKinsey.com.

24Reimagining customer engagement for the AI bank of the future

a team of top talent from multiple departments

that speak the language of the tech partners,

work at a compatible speed, and are empowered

to make and implement decisions swiftly.

Another key area is performance measurement.

Traditionally, a bank’s key performance indicators

(KPIs) focus on growth and profitability. The

core KPI for internet companies, by contrast, is

user experience. If partners are not aligned in

evaluating progress toward agreed-upon goals,

tension can arise and diminish the impact of the

collaboration.

Smart servicing and experiences

The third pillar of the reimagined engagement

layer is smart servicing facilitated by fast,

simple, and intuitive interactions with customers.

Banks that leverage AI and analytics to deliver

smart servicing and superior experiences stand

to increase customer satisfaction and loyalty.

Research shows that the stronger the experience

and the more satisfied the customer, the more likely

it is that the bank will generate higher revenue:

a more satisfied customer typically accounts

for approximately 2.4 times more revenue than

a neutral customer. What is more, we have

seen that companies scoring high on a scale of

customer satisfaction tend to generate higher total

shareholder returns than lower-scoring companies

do (Exhibit 4).

Along with the significant impact of customers’

overall experience, customers’ expectations

also influence their level of satisfaction—and, by

Exhibit 4

Companies with higher customer satisfaction tend to generate higher returns.

Change in total returns to shareholders (TRS) for companies with high, moderate, and low net

promoter scores (NPS)1

To create this chart we gathered data on TRS for ~150 publicly traded companies. Using 2017 Temkin NPS data, we grouped the companies into low, moderate,

and high NPS groups, and summarized the dierence in annualized percent growth in total returns for each group from 2008–18.

Source: McKinsey analysis; TRS data from DataStream 2008-2018; Temkin Group “October 2017 Net Promoter Score Benchmark Study”

Companies with higher customer satisfaction tend to generate higher returns.

Annualized growth in total shareholder returns, %

450

400

350

300

250

200

150

100

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

NPS performance groupings

High

3966

Moderate

2938

Low

–9–+28

Peter Kriss, “The Value of Customer Experience—Quantified,” August 1, 2014, HBR.org.

25 Reimagining customer engagement for the AI bank of the future

extension, may affect the company’s value. Given

the rising trend in customers’ expectations for

online, offline, and hybrid journeys, disruptive

companies in diverse markets are creating

customer-centric interactions and journeys

that are fast, simple, and intuitive. Guided by a

relentless commitment to customer satisfaction,

Amazon has achieved a high level of customer

loyalty through value, convenience, and reliability

in online shopping. Uber has set a high bar for

speed, safety, and amicable service supported

by frictionless end-to-end customer journeys.

Netflix has created a highly differentiated

experience by analyzing the viewing choices of

hundreds of millions of subscribers to create

highly personalized recommendations from its

stock of diverse content.

The challenge for banks is to examine each

crucial element in the design of differentiating

customer experiences. First among these is the

ability to open a service request on the device

of choice anytime, anywhere. Second, each

interaction should build on previous history

and continue without interruption or repeated

steps when the customer shifts from one

device to another. The service interface should

also be capable of recognizing the customer’s

context and adjust messaging accordingly. A

third crucial element is speed: For example, a

customer requesting a higher credit limit through

a chatbot should receive a response within

seconds, supported by real-time analysis of the

customer’s risk profile. If the request cannot

be met at once, the time frame for fulfilling the

request should be stated clearly.

Fourth, chatbots, voice assistants, and live

video consultations make it possible to dispense

with long, detailed forms and questionnaires.

Insurance provider Lemonade offers a chat-

based application form that follows a carefully

designed conversation to generate an insurance

quote. Likewise, self-serve journeys can offer

prompt access to assistance through chatbots,

with the ability to shift instantaneously and

seamlessly to a live video chat with a service

representative or adviser as soon as the request

exceeds machine capabilities.

Finally, it is crucial to personalize journeys in just

the right way. For example, customers appreciate

recommendations that they would not have

thought of themselves. They often do not want

more examples of what they have already bought.

They need to be given the recommendations at

the right time, when they are in “shopping mode.”

For example, sending a customer a reminder

for repeating an order for flowers based on a

purchase made on a special date last year, like an

anniversary, may work very well. At the same time,

organizations must be careful not to be “creepy”

and offer instead recommendations that are highly

relevant without crossing lines.

Reimagined engagement requires

integrated capabilities

To successfully design and implement their

engagement layer to become AI-first, banks need

to develop five capabilities:

1. Adopt a holistic, data-driven approach to

understanding how customers engage with the

bank. Best-in-class players achieve this in three

major steps:

— Implement a real-time, enterprise-wide data

infrastructure that captures virtually all data

points for a given customer’s relationship with

the bank’s various divisions and supports

a unified customer view encompassing

all channels, journeys, and products. (The

traditional siloed analyses undertaken by any

one of various teams have little relevance in an

AI-first organization.)

— Consolidate data on a central platform: To

ensure that these enterprise data sets are

utilized effectively and widely across teams,

AI-first banks aggregate the data captured from

multiple internal and external sources into a

central customer data platform.

Julien Boudet, Brian Gregg, Jane Wong, and Gustavo Schuler, “What shoppers really want from personalized marketing,” October 2017,

McKinsey.com.

26Reimagining customer engagement for the AI bank of the future

— Automate governance and controls to ensure

business-and-technology teams have ready

access to appropriate data sets, with the

necessary controls for security and permission

where needed. It is also important to ensure

that the appropriate data are available for

decisioning, at the right time and in the right

form, to the various AA/ML models used

by internal teams (from customer service to

product management) to support intelligent,

highly personalized interactions with

customers.

2. Embed next-generation talent within traditional

teams. Creating superior customer experiences

in the digital era requires a new set of skills and

capabilities centered on design, data science,

and product management. An individual product

manager, for example, may focus primarily on

technical solutions, customer experiences, or

maximizing business performance, but in an

AI-first environment, all product managers will

need a foundation in diverse areas, including

customer experience, advanced analytics and

machine learning, market analysis, business

strategy, as well as leadership and capability

development. Design leaders require a similar

foundation as well as deep expertise in extracting

user insights to guide business strategy and

innovation. The data, analytics, and AI skills

required to build an AI-bank are foreign to most

traditional financial services institutions, and

organizations should craft a detailed strategy

for attracting them. This plan should define

which capabilities can and should be developed

in-house (to ensure competitive distinction) and

which can be acquired through partnerships with

technology specialists.

Furthermore, our experience suggests that it’s

not enough to staff the teams with new talent.

What really differentiates experience leaders

is how they integrate new talent in traditional

team structures and unlock the full potential of

these capabilities, in the context of business

problems. Several organizations have built an

internal talent pool of data scientists and engineers.

However, most treat data as an operational function

and leverage data-and-analytics talent primarily

to generate and automate reports required by

traditional business teams. A few leaders treat data

management as a strategic function, and embed

data scientists/engineers within agile product

and customer service teams, each focused on a

discrete journey or use case, such as small business

lending, home financing, or digital wealth advisory

for the mass affluent. These organizations have

been recognized as leaders in creating superior

experiences that give them a competitive edge,

measured in customer satisfaction and value

creation.

3. Institute formal top-down mechanisms to

support coordination across traditional product and

channel silos. While financial services institutions

take various measures to align working teams with

groups focused on serving a specific customer

segment, these measures typically take a long time

to yield results (and often fail). The product and

channel silos through which banks have traditionally

sought to address the needs of diverse market

segments can be very complex, and this complexity

makes it difficult to break out of the product-centric

mindset and assume a genuinely customer-centric

view throughout the organization.

In our experience, bottom-up efforts to organize

teams around customer segments often fall short

of expectations if they are not complemented by a

top-down approach consisting of cross-department

senior management teams. While these teams are

empowered to act (that is, they have resources and

budgets, along with autonomy in deciding how to

deploy these to meet strategic goals), they also take

an integrated view of various siloed efforts across

the organization and prioritize a limited number of

high-impact cross-cutting initiatives that require

central coordination (as opposed to spreading the

organization’s resources thin on several smaller

initiatives). Finally, they develop and track progress

against a coordinated plan executed through the

traditional team structure.

Chandra Gnanasambandam, Martin Harrysson, Shivam Srivastava, and Yu Wu, “Product managers for the digital world,” May 2017, McKinsey.com.

 Melissa Dalrymple, Sam Pickover, and Benedict Sheppard, “Are you asking enough from your design leaders,” February 2020, McKinsey.com.

27 Reimagining customer engagement for the AI bank of the future

4. Institutionalized capabilities to strike new

partnerships at-scale with a heterogenous set of

non-financial services institutions. Partnerships

are becoming increasingly critical for financial

services players to extend their boundaries

beyond traditional channels, acquire more

customers, and create deeper engagement. Most

institutions understand the importance of having

a clear strategic rationale (including a “win-win”

value creation thesis for partners), and a strong

governance model to oversee the partnership. It

is also important to establish teams responsible

both for setting up partnerships and for adapting

the technology infrastructure to support the

efficient and speedy launch of the partnership.

— Setting-up dedicated teams that are focused

on establishing partnerships. These teams

constantly scan the market for potential

partners and assess their relevance to the

institution’s growth strategy. They engage

effectively with a broad range of non-

bank partners—beginning with a review of

differences in culture and technology—and

gauge the flexibility required to align with the

partners’ ways of working (e.g., profile and

seniority of people participating in discussions,

decision-making styles, responsiveness to

requests, adherence to timelines) to enable

faster, smoother, and more productive

collaboration.

— Making the technology infrastructure

partnership-friendly hinges to a significant

degree on API contracts identifying the

functionalities that must be developed to meet

the partner’s requirements. Another crucial

step is altering the technology infrastructure

to facilitate fast integration with partner

capabilities. This includes creating sand-box

environments to enable rapid experimentation

and proof-of-concept trials, as well as

modern data-sharing and storage options

compatible with the partner’s data-stack.

5. Deep integration with the remaining

layers of the AI bank—that is, the AI-enabled

decisioning layer and the core-tech and data

layer. The journey to become an AI bank

entails transforming capabilities across all four

layers of the capability stack: engagement,

AI-powered decisioning, core technology and

data infrastructure, and operating model. The

layers should work in unison, and investment in

each layer should be made in tandem with the

others. Underinvesting in any layer will create a

ripple effect that hinders the ability of the stack

as a whole to deliver enterprise goals.

As traditional banks observe the rapid

advancement of AI technologies and the

success of digital innovators in creating

compelling customer experiences, many

recognize the need to reimagine how they

engage their customers. By adopting an

AI-first approach in their vision and planning,

innovative banks are building the capabilities

that will enable them not just to deliver

intelligent services but also to design intuitive,

highly personalized journeys spanning diverse

ecosystems, from banking to housing to

retail commerce, B2B services, and more. To

realize this vision requires new talent, a robust

mechanism for managing partnerships, and a

progressive transformation of the capability

stack. Throughout this expansive undertaking,

leaders must stay attuned to customer

perspectives and be clear about how the AI

bank will create value for each customer.

Renny Thomas is a senior partner, Shwaitang Singh is an associate partner, and Sailee Rane is a consultant, all in McKinsey’s

Mumbai office. Malcom Gomes is a partner in the Bengaluru office, and Violet Chung is a partner in the Hong Kong office.

The authors would like to thank Xiang Ji, Jinita Shroff, Amit Gupta, Vineet Rawat, and Yihong Wu for their contributions to this article.

28Reimagining customer engagement for the AI bank of the future

Global Banking & Securities

AI-powered decision

making for the bank of

the future

Banks are already strengthening customer relationships and

lowering costs by using artificial intelligence to guide customer

engagement. Success requires that capability stacks include the

right decisioning elements.

March 2021

by Akshat Agarwal, Charu Singhal, and Renny Thomas

The ongoing transition to digital channels creates

an opportunity for banks to serve more customers,

expand market share, and increase revenue at lower

cost. Crucially, banks that pursue this opportunity

also can access the bigger, richer data sets required

to fuel advanced-analytics (AA) and machine-

learning (ML) decision engines. Deployed at scale,

these decision-making capabilities powered

by artificial intelligence (AI) can give the bank a

decisive competitive edge by generating significant

incremental value for customers, partners, and

the bank. Banks that aim to compete in global

and regional markets increasingly influenced

by digital ecosystems will need a well-rounded

AI-and-analytics capability stack comprising four

main layers: reimagined engagement, AI-powered

decision making, core technology and data

infrastructure, and leading-edge operating model.

The layers of the AI-bank capability stack are

interdependent and must work in unison to deliver

value, as discussed in the first article. In our

second article, we examined how AI-first banks

are reimagining customer engagement to provide

superior experiences across diverse bank platforms

and partner ecosystems. Here, we focus on the AA/

ML decisioning capabilities required to understand

and respond to customers’ fast-evolving needs with

precision, speed, and efficiency. Banks that leverage

machine-learning models to determine in (near) real

time the best way to engage with each customer

have potential to increase value in four ways:

— Stronger customer acquisition. Banks gain an

edge by creating superior customer experiences

with end-to-end automation and using advanced

analytics to craft highly personalized messages

at each step of the customer-acquisition journey.

— Higher customer lifetime value. Banks can

increase the lifetime value of customers

by engaging with them continuously and

intelligently to strengthen each relationship

across diverse products and services.

— Lower operating costs. Banks can lower costs

by automating as fully as possible document

processing, review, and decision making,

particularly in acquisition and servicing.

— Lower credit risk. To lower credit risks, banks

can adopt more sophisticated screening of

prospective customers and early detection of

behaviors that signal higher risk of default and

fraud.

As banks think about how to design and build a

highly flexible and fully automated decisioning

layer of the AI-bank capability stack, they can

benefit from organizing their efforts around four

interdependent elements: (1) leveraging AA/ML

models for automated, personalized decisions

across the customer life cycle; (2) building and

deploying AA/ML models at scale; (3) augmenting

AA/ML models with what we call “edge” capabilities¹

to reduce costs, streamline customer journeys, and

enhance the overall experience; and (4) building

an enterprise-wide digital-marketing engine to

translate insights generated in the decision-making

layer into a set of coordinated messages delivered

through the bank’s engagement layer.

Automated, personalized decisions

across the customer life cycle

If financial institutions begin by prioritizing the use

cases where AA/ML models can add the most value,

they can automate more than 20 decisions in diverse

customer journeys. Within the lending life cycle, for

example, leading banks are relying increasingly

on AI and analytics capabilities to add value in five

main areas: customer acquisition, credit decisioning,

monitoring and collections, deepening relationships,

and smart servicing (Exhibit 1, next page).

Customer acquisition

The use of advanced analytics is crucial to the

design of journeys for new customers, who may

follow a variety of paths to open a new card account,

Edge capabilities refer to next-generation AI-powered technologies that can provide financial institutions an edge over the competition.

Natural language processing (NLP), voice-script analysis, virtual agents, computer vision, facial recognition, blockchain, robotics, and

behavioral analytics are some of the technologies that we classify as “edge capabilities.” These capabilities can be instrumental in improving

customer experience and loyalty across multiple dimensions (engagement channels, intelligent advisory, faster processing), personalizing

offers with highly accurate underwriting, and improving operational efficiency across the value chain (from customer servicing to monitoring,

record management, and more).

30 AI-powered decision making for the bank of the future

apply for a mortgage, or research new investment

opportunities. Some may head directly to the bank’s

website, mobile app, branch kiosk, or ATM. Others

may arrive indirectly through a partner’s website or by

clicking on an ad. Many banks already use analytical

tools to understand each new customer’s path to the

bank, so they get an accurate view of the customer’s

context and direction of movement, which enables

them to deliver highly personalized offers directly

on the landing page. Following local regulations

governing the use and protection of customer data,

banks can understand individuals’ needs more

precisely by analyzing how customers enter the

website (search, keywords, advertisements), their

browsing history (cookies, site history), and social-

media data to form an initial profile of each customer,

including financial position and provisional credit

scoring. Based on real-time analysis of a customer’s

digital footprint, banks can display a landing page

tailored to their profile and preferences.

These tools can also help banks tailor follow-up

messages and offers for each customer. Replacing

much of the mass messaging that used to flow to

thousands or tens of thousands of customers in a

subsegment, advanced analytics can help prioritize

customers for continued engagement. The bank can

select customers according to their responsiveness

to prior messaging—also known as their “propensity

to buy”—and can identify the best channel for

each type of message, according to the time of

day. And for the “last mile” of the customer journey,

AI-first institutions are using advanced analytics

to generate intelligent, highly relevant messages

Exhibit 1

Banks should prioritize using advanced analytics (AA) and machine learning (ML)

in decisions across the customer life cycle.

VAR is value at risk.

2AUM is assets under management.

Banks should prioritize using advanced analytics (AA) and machine learning

(ML) in decisions across the customer life cycle.

Customer life cycle

Customer

acquisition

Hyperpersonalized

oers

Customer retargeting

Propensity-to-buy

scoring

Channel mapping

Monthly customer-

acquisition run rate

Credit decisioning

Credit qualication

Limit assessment

Pricing optimization

Fraud prevention

Credit-approval

turnaround time, %

of applications

approved

Monitoring and

collections

Early-warning

signals

Probability of

default/self-cure

VAR-based customer

segmentation1

Agent–customer

mapping

Average days past

due, nonperforming

assets

Deepening

relationships

Intelligent oers (eg,

next product to buy)

Churn reduction

Channel propensity

Fatigue rule engine

Deposit/AUM

attrition rate,2

products 6 per

customer

Smart servicing

Servicing personas

Dynamic customer

routing (channel, agent)

Real-time recom-

mendation engine

AI-enabled agent

review and training

Net promoter score,

cost of servicing

Key metrics

31AI-powered decision making for the bank of the future

and provide smart servicing via assisted channels to

create a superior experience, which has been shown to

contribute to higher rates of conversion.²

Credit decisioning

Setting themselves apart from traditional banks,

whose customers may wait anywhere from a day to a

week for credit approval, AI-first banks have designed

streamlined lending journeys, using extensive

automation and near-real-time analysis of customer

data to generate prompt credit decisions for retailers,

small and medium-size enterprises (SMEs), and

corporate clients. They do this by sifting through a

variety of structured and unstructured data collected

from conventional sources (such as bank transaction

history, credit reports, and tax returns) and new ones

(including location data, telecom usage data, utility

bills, and more). Access to these nontraditional data

sources depends on open banking and other data

sharing guidelines as well the availability of officially

approved APIs and data aggregators in the local

market. Further, while accessing and leveraging

personal data of customers, banks must secure data

and protect customer privacy in accordance with

local regulations (e.g., the General Data Protection

Regulation in the EU and the California Consumer

Privacy Act in the US).

By using powerful AA/ML models to analyze these

broad and diverse data sets in near real time, banks

can qualify new customers for credit services,

determine loan limits and pricing, and reduce the risk

of fraud.

— Credit qualification. Lenders seeking to determine

if a customer qualifies for a particular type of

loan have for many years used rule-based or

logistic-regression models to analyze credit

bureau reports. This approach, which relies on

a narrow set of criteria, fails to serve a large

segment of consumers and SMEs lacking a formal

credit history, so these potential customers turn

to nonbank sources of credit. In recent years,

however, leading banks and fintech lenders

have developed complex models for analyzing

structured and unstructured data, examining

hundreds of data points collected from social

media, browsing history, telecommunications

usage data, and more. This decisioning process

is automated from end to end, so it can be

completed nearly instantaneously, enabling

the bank to predict the likelihood of default for

individuals in a vast and potentially profitable

segment of unbanked and underbanked

consumers and SMEs. As banks build and refine

their qualification model, they can proceed

gradually, testing and improving the model—for

example, by using auto-approvals for customers

up to a certain threshold with significantly lower

default risk and using manual verification to

review those estimated to have a higher default

risk and then gradually shifting more cases to

automated decisioning.

— Limit assessment. Leading banks are also using

AA/ML models to automate the process for

determining the maximum amount a customer

may borrow. These loan-approval systems, by

leveraging optical character recognition (OCR)

to extract data from conventional data sources

such as bank statements, tax returns, and

utilities invoices, can quickly assess a customer’s

disposable income and capacity to make regular

loan payments. The proliferation of digital

interactions also provides vast and diverse data

sets to fuel complex machine-learning models.

By building data sets that draw upon both

conventional and new sources of data, banks

can generate a highly accurate prediction of

a customer’s capacity to pay. Just a few data

sources that may be available for analysis (with

the customer’s permission) are emails, SMS, and

e-commerce expenditures.

— Pricing. Banks generally have offered highly

standardized rates on loans, with sales

representatives and relationship managers

having some discretion to adjust rates within

certain thresholds. However, fierce competition

on loan pricing, particularly for borrowers with a

strong risk score, places banks using traditional

approaches at a considerable disadvantage

against AI-and-analytics leaders. Fortified with

highly accurate machine-learning models for risk

scoring and loan pricing, AI-first banks have been

able to offer competitive rates while keeping their

Erik Lindecrantz, Madeleine Tjon Pian Gi, and Stefano Zerbi, “Personalizing the customer experience: Driving differentiation in retail,” April

2020, McKinsey.com.

32 AI-powered decision making for the bank of the future

risk costs low. Some are also using their decisioning

capabilities to quantify the customer’s propensity

to buy according to the customer’s use of different

types of financial products. Some even leverage

natural-language processing (NLP) to analyze

unstructured transcripts of interactions with sales

and service representatives and, in some cases,

collections personnel. By basing the offered rate

on both creditworthiness and propensity to buy, the

bank can optimize the balance of total asset volume,

risk, and interest income within a lending portfolio.

— Fraud management. As competition for credit

relationships becomes concentrated in digital

channels, the automated processing of loan

applications and use of AA/ML models to expedite

credit approval and disbursement of funds not

only positions the bank to acquire new customers

and increase market share, but also opens new

opportunities for fraud. The costliest instances of

fraud typically fall into one of five categories: identity

theft, employee fraud, third-party or partner fraud

(e.g., fraud by sales agents), customer fraud, and

payment fraud (including money laundering and

sanctions violations). Banks should continuously

update their fraud detection and prevention

models, as we discuss later with regard to edge

capabilities. Ping An, for example, uses an image-

analytics model to recognize 54 involuntary micro-

expressions that occur before the brain has a

chance to control facial movements.³

AI-driven credit decisioning can build the business

while lowering costs. Sharper identification of risky

customers enables banks to increase approval

rates without increasing credit risk. What is more, by

automating as much of the lending journey as possible,

banks can reduce the costs of support functions and

strengthen each customer’s experience with faster

loan approval and disbursement of funds, fewer

requests for documentation, and credit offers precisely

tailored to meet customer needs. Exhibit 2 illustrates

how AI-enabled decisioning capabilities underpin a

customer’s onboarding journey.

“Chinese banks start scanning borrowers’ facial movements,” Financial Times, October 28, 2018, ft.com.

Exhibit 2

The combination of AI and analytics enhances the onboarding journey for each

new customer.

The combination of AI and analytics enhances the onboarding journey for each

new customer.

Joy’s

landing page

shows her per-

sonalized oers:

personal loan for

travel, 5% o on

travel insurance

Analytics-backed

hyperpersonalized

oers based on

customer

microsegment

Propensity-to-buy

model to identify

whom to retarget

Channel propensity

to identify right

outreach channel

Joy receives a

reminder for

personal loan

for travel at zero

processing

charges

Joy

receives a call to

assist in journey;

caller also

informs

Joy about custom

travel services

Analytics-enabled

customer–caller

mapping with

specic cues

provided to caller

Joy completes a

streamlined

3-click journey

to see the

oer terms and

conditions

AA-enabled real-

time credit

underwriting, limit

assessment, and

pricing

Joy

completes

online know-your-

customer form,

provides details for

employment veri-

cation, and sets up

an online pay-

ment mandate

AI capabilities to

conduct relevant

fraud checks (eg,

facial recognition

with know-your-

customer docs)

Loan disbursed to

Joy; curated

catalog of oers

ahead of Joy’s

travel sent by

Hyperpersonalized

cross-sell and

upsell oers

Name: Joy

Age: 32 years

Occupation: Working professional

Family: Married, no children

Prole attribute: Avid traveler

33AI-powered decision making for the bank of the future

Monitoring and collections

Once a bank has employed AA/ML models to

automate loan underwriting and pricing, it can also

deploy AI and advanced analytics to reduce the

burden of nonperforming loans. Increasingly, banks

are engaging with clients proactively to help them

keep up with payments and work more closely

with clients who encounter difficulties. By drawing

upon internal and external data sources to build a

360-degree view of a customer’s financial position,

banks can recognize early-warning signals that a

borrower’s risk profile may have changed and that

the risk of default should be reassessed.

Beyond conventional data sources like repayment

data and credit bureau reports, banks can digitize

and leverage other interaction data from campaigns,

field visits, and collection agents’ comments to

draw insights for collections strategy. Further, a

variety of external data partnerships for location

data and transaction history can help the bank

understand both the customer’s position and the

most effective approach, or contact strategy, for

averting default (Exhibit 3).

Contact strategy. To determine an appropriate

contact strategy for customers at risk of default,

banks can segment accounts according to value

at risk (VAR), which is the loan balance times

the probability of default. This allows banks to

focus high-touch interactions on borrowers that

account for the highest VAR; banks can then use

low-cost channels like telephoning and texting for

borrowers posing less risk. Banks have used this

approach to reduce both the cost of collections

and the volume of loans to be resolved through

restructuring, sale, or write-off.

Exhibit 3

Advanced analytics and machine learning can classify customers into

microsegments for targeted interventions.

Source: Ignacio Crespo and Arvind Govindarajan, “The analytics-enabled collections model,” McKinsey on Payments, August 2018, McKinsey.com

Advanced analytics and machine learning can classify customers into

microsegments for targeted interventions.

Targeted

intervention

Impact

True low-risk

Use least

experienced agents

provided with set

scripts

Onscreen prompts

guide agent–client

conversation based

on probability of

breaking promises

Absentminded

Ignore or use

interactive voice

message (segment

will probably

self-cure)

10% of time saved,

allowing for

reassignment of

agents to more

dicult customers

and specic

campaigns

Dialer-based

Match agents to

customers; send live

prompts to agents

to modify scripts

Matching and

prompts can

increase sense of

connection and

likelihood of paying

True high-touch

Focus on customers

able to pay and at

high risk of not

paying

Added focus

addresses higher

probability of default

rates in this segment

Unable to cure

Oer debt-

restructuring

settlements early for

those truly

underwater

Signicant increase

in restructuring and

settlements

increases chance of

collecting at least

part of debt

Customer type

Ignacio Crespo and Arvind Govindarajan, “The analytics-enabled collections model,” April 2018, McKinsey.com.

34 AI-powered decision making for the bank of the future

Treatment strategy. If contact strategies through

various channels are inadequate to help the

customer resume timely payment, banks must

pursue stronger measures, according to the

customer’s ability and willingness to pay. Customers

with high willingness but limited ability to pay in the

short term may require restructuring of the loan

through partial-payment plans or loan extensions.

In cases where the customer exhibits both low

willingness and limited ability to pay, banks should

focus on early settlement and asset recovery.

Advanced analytics, enabled by unstructured

internal data sources such as call transcripts

from collections contact centers and external

data sources such as spending behavior on other

digital channels, can improve the accuracy of

determinations of ability and willingness to pay.

Deepening relationships

Strong customer engagement is the foundation

for maximizing customer value, and leaders

are using advanced analytics to identify less

engaged customers at risk of attrition and to craft

messages for timely nudges. As with any customer

communication in a smart omnichannel service

environment, each personalized offer is delivered

through the right channel according to the time of

day. Rich internal data for existing customers can

enable financial institutions to create a finely tuned

outreach strategy for each individual customer,

guided by risk considerations.

Deeper relationships are predicated on a bank’s

precise understanding of a customer’s unique

needs and expectations. A bank can craft offers to

meet emerging needs and deliver them at the right

time and through the right channel. By doing so, the

bank demonstrates that it understands customers’

current position and aspirations and can help them

get from the former to the latter. For example, by

analyzing browsing history and spending patterns, a

bank might recognize a consumer’s need for credit

to finance an upcoming purchase of a household

appliance. Analysis of internal data on product

usage can also reveal areas where the bank can

make its offering more relevant to a customer’s

current needs. Ping An, for example, has developed

a prediction algorithm to estimate the ideal

product-per-customer (PPC) ratio for each

user, based on individual needs. If analysis of

a customer’s needs produces an anticipated

product usage ratio of eight but the customer

uses only two products, the relationship

manager receives a prompt to reach out to the

customer and cross-sell or up-sell relevant

ecosystem products.

Servicing and engagement

AI-powered decisioning can enable banks to

create a smart, highly personalized servicing

experience based on customer microsegments,

thereby enabling different channels to deliver

superior service and a compelling experience

with interactions that are fast, simple, and

intuitive. Banks can support their relationship

managers with timely customer insights and

tailor-made offers for each customer. They can

also significantly improve agents’ productivity

with streamlined preapproved products crafted

to meet each customer’s distinct needs. Models

that analyze voice and speech characteristics

can match agents with customers based on

behavioral and psychological mapping. Similarly,

transcript analysis can enable prediction of

customer distress and suggest resolution to

the agent.

Deployment of AA/ML models at

scale

Leveraging AI to automate decision making in

near real time is a complex and costly endeavor.

If banks are to earn the required return on their

technology investments, they must begin with a

strategy and road map to capture maximal scale

benefits in the design, building, and deployment

of AA/ML models.

As banks embark on this journey, leaders must

encourage all stakeholders to break out of siloed

mindsets and think broadly about how models

can be designed for uses in diverse contexts

across the enterprise. AI-first organizations have

succeeded by organizing the effort around four

“Ping An Bank: Change everything,” Asiamoney, September 26, 2019, asiamoney.com.

Violet Chung, Malcolm Gomes, Sailee Rane, Shwaitang Singh, Renny Thomas, “Reimagining customer engagement for the AI bank of the future,”

McKinsey.com, October 2020.

35AI-powered decision making for the bank of the future

main elements: First, they prioritize the analytics

use cases with the biggest impact on customer

experience and the most value for the bank.

Second, they ensure that the data architecture,

data pipelines, application programming interfaces

(APIs), and other essential components are available

for building and deploying models at scale through

standardized, repeatable processes. Third, they

establish a semiautonomous lab for experimentation

and prototype development and set up a factory for

industrial-scale production of the solution. Fourth,

they assemble the right mix of talent for agile, cross-

functional teams and empower them to maximize

value in close alignment with enterprise strategy.

Several leading banks have established semi-

autonomous labs offering a test-and-learn

environment where cross-functional teams can

experiment with different approaches to achieving

the value-generating goals of a particular use case,

moving from minimal viable product to scalable

solution in a matter of weeks. Building AA/ML models

at scale and deploying them across the enterprise

depend on matching the right talent and skills with

each of the roles required for a successful analytics

lab and factory (Exhibit 4).

The lab combines talent from business, analytics,

technology, operations, and more. There are two

main technical roles. One is the data scientist,

who is responsible for identifying the analytics

techniques required to meet the business goal and

for programming advanced analytics algorithms.

The other is the data engineer, who scopes the data

Exhibit 4

Diverse roles are necessary for building and deploying AA/ML models at scale.

Continuous integration and continuous deployment.

Diverse roles are necessary for building and deploying AA/ML models at scale.

Lab environment

Product owner

Leads the squad;

typically a

business owner

who provides voice

of the customer

Data engineer

Scopes the data

available; builds

data architecture

and data pipelines

Designer

Focuses on

interaction

between end

users and the

analytics solution

output

Data scientist

Frames the

business problem

and develops

advanced analytics

algorithms

Translator

Interfaces

between business

and technical

stakeholders

Delivery manager

Responsible for all

aspects of delivery

of the analytics

solution to meet

squad goal

Factory environment

Product owner

First point of

contact for

external stake-

holders; denes

the solution criteria

DevOps engineer

Develops CI/CD1

pipelines to

automate parts of

the software-

deployment pipeline

Full-stack

developer

Develops software

components for

the back and front

ends of AI solutions

ML engineer

Optimizes ML

models for

performance and

scalability; deploys

the models into

production

Infrastructure

architect

Designs

infrastructure

components for the

analytics use case

Tara Balakrishnan, Michael Chui, Bryce Hall, and Nicolaus Henke, “The state of AI in 2020,” November 2020, McKinsey.com.

Nayur Khan, Brian McCarthy, and Adi Pradhan, “Executive’s guide to developing AI at scale,” October 2020, McKinsey.com.

36 AI-powered decision making for the bank of the future

available, identifies major sources of data to be

consolidated for analytics, develops data pipelines to

simplify and automate data movement, and sets up

data architecture for storage and layering. In addition,

the role of translator is crucial to ensure consistent

communication and smooth collaboration between

business leaders and analytics specialists.

On factory teams, one of the primary technical

roles is the DevOps engineer, who is responsible

for developing continuous integration (CI) and

continuous deployment (CD) pipelines for deploying

software. In addition, the full-stack developer is

responsible for developing software components

for other layers of the stack. The machine-learning

engineer prepares models for deployment at scale,

and the infrastructure architect ensures that the

analytics solution is compatible with the architecture

of the core tech and data layer of the capability stack.

The lab-and-factory setup requires flexible and

scalable technologies to handle the changing

requirements of analytics engines. It is also important

to give analytics teams access to the centralized

data lake, and these teams must be able to draw

upon raw data from diverse sources to generate data

sets to be used in building models. The technology

supporting the solution must be modular to allow the

transfer of developed solutions to factory production

using DevOps tools. Finally, it is crucial to embed

performance management and risk controls within

models to avoid adverse impacts on operations.

Once the lab has developed a model, the factory

takes over, running 24/7 to put the model into

production and deploying it at scale in diverse use

cases across the enterprise.

Augmented AA/ML models with edge

capabilities

The rapid improvement of AI-powered technologies

spurs competition on speed, cost, experience, and

intelligent propositions. To maintain its market

leadership, an AI-first institution must develop

models capable of meeting the processing

requirements of edge capabilities, including natural-

language processing (NLP), computer vision, facial

recognition, and more.

Some edge technologies already afford banks

the opportunity to strengthen existing models

with expanded data sets. For example, many

interactions with customers—via telephone,

mobile app, website, or increasingly, in a

branch—begin with a conversational interface

to establish the purpose of the interaction and

collect the information required to resolve the

query or transfer it to an agent. A routing engine

can use voice and image analysis to understand

a customer’s current sentiment and match the

customer with a suitable agent. The models

underpinning virtual assistants and chatbots

employ NLP and voice-script analysis to increase

their predictive accuracy as they churn through

vast unstructured data generated during

customer-service and sales interactions.

While each customer-service journey presents an

opportunity to deepen the relationship with the

help of next-product-to-buy recommendations,

banks should constantly seek to improve their

recommendation engines and messaging

campaigns. Feedback loops, for example, can

help marketing teams and frontline officers

gauge the effectiveness of an offer by analyzing

customers’ ongoing browsing and transaction

activity within the bank’s digital ecosystem and

beyond (Exhibit 5, next page).

As edge capabilities become more powerful,

leaders are developing new, increasingly

complex analytics solutions to create a superior

experience and introduce distinctive innovations.

Use of computer vision and voice-to-script

conversion can speed the completion of forms—

for instance, enabling a customer to respond

orally to questions and upload documents

from which relevant data can be extracted

automatically using optical character recognition

(OCR). Facial and sentiment analysis during an

in-person consultation or videoconference can

support frontline representatives with messages

and offers finely tuned to the customer’s needs

and aspirations.

Several banks use voice recognition to verify

customer identity for certain low-value, high-

volume transactions. Some are using facial

37AI-powered decision making for the bank of the future

recognition to authenticate customers’ identity as soon

as they enter a branch, approach an ATM, or open the

banking app on a mobile device. As noted earlier, facial

analysis is also useful in identifying

potential fraud.

Leading banks are using blockchain to create smart

contracts, secure trade documents and automate the

release of funds upon delivery of goods, and establish

shared utilities to reduce the burden of know-your-

customer (KYC) and anti-money-laundering (AML)

compliance for banks and customers. Edge capabilities

deployed as part of an enterprise strategy to enhance

the AI bank’s value proposition have the potential not

only to improve credit underwriting and fraud

prevention but also to reduce the costs of document

handling and regulatory compliance.

Enterprise-wide digital marketing engine

While the automated decisions generated by AA/ML

models provide highly accurate, real-time predictions

of customer behaviors, banks must go the last mile to

ensure that these analytical insights have an impact

on customer behavior, such as purchasing a product,

making a loan payment, or exploring new service

offers. In other words, an organization must establish

a mechanism to “translate” analytical outcomes

into compelling messages to communicate to the

customer at the right time, through the preferred

channel—be it email, SMS, mobile app, website,

branch staff, or a relationship manager—according

to the time of day.

This last mile from decisioning to messaging is the

domain of the digital marketing engine. Seamlessly

integrated with applications across the full AI-and-

analytics capability stack with the help of APIs,

from data infrastructure to engagement channels,

this engine supports the nearly instantaneous

processing of raw data to produce tailored messages

communicated via engagement channels. Exhibit 6,

on the next page, illustrates the position of the digital

Exhibit 5

Edge capabilities enhance customer-service journeys.

Interactive voice response.

2Natural-language-processing-enabled.

Edge capabilities enhance customer-service journeys.

Digital self-cure

channels for

customers (eg,

WhatsApp, mobile

app, website)

Customer may

seek immediate

resolution of

queries through

digital self-service

channels

Voice-recognition-

enabled IVR,1

frontline bots

handling 30–50%

of queries

If query is not

resolved through

automated

channels,

customer may

contact bank via

chat or request

call with live agent

AI-enabled

customer proling,

customer–agent

matching

Customer

connected to the

appropriate agent

(chat or call) based

on type of query

and customer

proling

Voice-analytics-

and NLP-enabled2

customer-

sentiment analysis

Contact-center

agents supported

by live feedback

and prompts to

sustain superior

customer

experience

AI-enabled

service-to-sales

engine

Customers are

prompted for any

specic oers

preapproved for

them

Feedback loop via

engagement

channels

Postcall feedback

and automated

follow-up occur via

digital channels

38 AI-powered decision making for the bank of the future

marketing engine (or martech stack) within the

decisioning layer of the AI-bank capability stack.

The digital marketing engine comprises platforms

and applications fulfilling four main functions: data

management, design and activation, measurement

and testing, and channel analytics. The data

management platform, which forms part of the

core tech and data infrastructure layer of the

AI-bank capability stack, supplies the data used

to create and manage target customer segments.

The design and activation function has three

elements: (1) the content management platform,

where messages, offers, advertisements, and

other interventions are created, managed,

and modified; (2) the ad tech server, which

automates advertisements based on data

analysis; and (3) the campaign management

platform, which supports the creation and

management of marketing campaigns, which

are conducted automatically according to the

microsegmentation generated by the data

management platform.

Just as the AI-and-analytics capability

stack entails fundamental changes in the

organization’s talent, culture, and ways of

working, the success of digital marketing

Exhibit 6

The digital marketing engine requires full stack capabilities.

Advanced-analytics and machine-learning.

Natural-language processing.

The digital marketing engine requires full stack capabilities.

Engagement layer

Email SMS

Decisioning layer

Customer

relationship

management

(CRM)

Edge

capabilities

NLP

Voice

analysis

Martech stack

Data lake layer

Raw data lake

Comprehensive set of data sources: internal structured data (eg, applications, product holding, payment behavior),

unstructured data (eg, call logs), CRM, external data (eg, telco, clickstream data), campaign performance data

Data mgmt.

platform

(DMP)

Mobile app Website

Branch

Contact

center

...

Channel

analytics/

feedback loop

Content

mgmt.

platform

Ad tech

server

Design and activation

Campaign

mgmt.

platform

Testing

platform

AA/ML models: Decisioning and personalization engine

Virtual

agents

Computer

vision

Facial

recognition

Blockchain

Robotics

Behavioral

analytics

Curated data lake

Other use cases

Additional derived

elements for personalization

Customer 360 Personalization store

39AI-powered decision making for the bank of the future

capabilities depends on an agile operating

model. This model consists of autonomous cross-

functional teams (or pods) drawing upon the

talent of different parts of the enterprise, such

as business units, marketing, analytics, channels,

operations, and technology. Each pod should also

include representatives from partner organizations

crucial to the digital marketing effort—for example,

user-interface and user-experience designers,

who lay out the campaign’s look and feel and its

flow, and copywriters, who finalize the language

of any intervention. The members of each pod

collaborate on developing, managing, and improving

engagement campaigns, and each member is

accountable for campaigns’ impact according to

clearly defined key performance indicators (KPIs).

To achieve the desired outcome, an AI-first bank

launching daily personalized communications to

millions of customers must build tools for continual

testing and learning. The measurement and testing

platform flags potential aspects of content or

distribution to improve, thereby enabling teams to

evaluate in real time the effectiveness of campaigns.

Another source of continual feedback is channel

analytics, which includes tools and dashboards

for real-time tracking of engagement across each

target segment. Every day, each pod leverages the

channel analytics and measurement and testing

platforms to closely track various indicators,

including delivery rates, email open rates, click-

through rates by channel for customers seeking

more information (the first call to action), conversion

rates, and more. These diagnostics help members

of the pod experiment with potential enhancements

to messages, advertisements, and campaign design.

As an example, Commonwealth Bank of Australia

(CBA) leverages its mobile app to test messages

and learn within hours what works and what must

be changed. This cadence enables rapid scaling of

campaigns to similar customer segments.

In measurement of campaigns’ impact, scientific

rigor is crucial. To allow for precise measurement

of the incremental value of the campaign, each

target segment should include a control group of

customers excluded from the campaign. The tools

and capabilities for evaluating the effectiveness of

customer-engagement campaigns help employees

across the organization understand how they can

enhance their impact on individual customers and

add value to an AI-oriented culture.

The rapid improvement of AI-powered technologies

spurs competition on speed, cost, experience, and

intelligent propositions. To remain competitive,

banks must engage customers with highly

personalized and timely content to build loyalty.

Personalized offers with tailored communication

delivered at the right time through the customer’s

preferred channel can help banks maximize the

lifetime value of each customer relationship and

reinforce the organization’s market leadership.

To achieve these benefits, banks must build

AI-powered decisioning capabilities fueled by a rich

mixture of internal and external data and augmented

by edge technologies. The core technology and

data infrastructure required to collect and curate

increasingly diverse and voluminous data sets is the

topic of the next article in our series on the AI-bank

capability stack.

Akshat Agarwal is an associate partner in McKinsey’s Bangalore office. Charu Singhal a consultant and Renny Thomas is a

senior partner, both in the Mumbai office.

Paul McIntyre, “CommBank’s analytics chief on how its AI-powered ‘Customer Engagement Engine’ is changing everything,” Mi3, September 21,

2020, mi-3.com.au.

40 AI-powered decision making for the bank of the future

Global Banking & Securities

Beyond digital transformations:

Modernizing core technology

for the AI bank of the future

For artificial intelligence to deliver value across the organization, banks

need core technology that is scalable, resilient, and adaptable. Building

that requires changes in six key areas.

April 2021

by Sven Blumberg, Rich Isenberg, Dave Kerr, Milan Mitra, and Renny Thomas

An artificial-intelligence (AI) bank leapfrogs

the competition by organizing talent, technology,

and ways of working around an AI-first vision

for empowering customers with intelligent value

propositions delivered through compelling

journeys and experiences. Making this vision

a reality requires capabilities in four areas:

an engagement layer, decisioning layer, core

technology layer, and platform operating model.

We discussed the the first two areas in the

previous articles. The capabilities of the

reimagined engagement layer enable the AI bank

to deliver highly personalized seamless journeys

across bank channels and within partner

ecosystems. The capabilities of the AI-powered

decisioning layer transform customer insights

into messages and offers tailored to address a

customer’s unique needs. The current article

identifies capabilities needed in the third area,

the core technology and data infrastructure of

the modern capability stack.

Deploying AI capabilities across the organization

requires a scalable, resilient, and adaptable

set of core-technology components. When

implemented successfully, this foundational

layer can enable a bank to accelerate technology

innovations, improve the quality and reliability

of operations, reduce operating costs, and

strengthen customer engagement.

We begin by summarizing the primary demands

banking leaders should consider as they plan

an enterprise-wide initiative to modernize

core technology, data management, and the

underlying infrastructure. Next, we examine

the key transformations required to modernize

the core technology and data infrastructure.

We conclude by sharing 12 actions technology

leaders should consider taking to ensure the

transformation creates value for customers and

the bank.

An AI-first model places demands on

a bank’s core technology

Across industries, many organizations have

struggled to keep pace with the demand for

digitization, especially as consumers accelerated

their adoption of digital channels for daily

transactions during the COVID19 crisis.¹ Even

before that, however, the financial-services

industry has historically had mixed success

in technology. Institutions that were early

adopters and innovators in technology have

built up a complex landscape of technical assets

over decades and accumulated significant

technical debt. Some institutions have tackled

this challenge; many are behind the curve.

Meanwhile, alongside the incumbents, an

extremely active fintech industry has been

constantly innovating and raising the bar.

Financial institutions that have shifted from

being intensive consumers of technology to

making AI and analytics a core capability are

finding it easier to shift into the real-time and

consumer-centric ecosystem. As AI technologies

play an increasingly central role in creating value

for banks and their customers, financial-services

organizations need to reinvent themselves

as technology-forward institutions, so they

can deliver customized products and highly

personalized services at scale in near real time.

At many institutions, standard practices now

include omnichannel engagement, the use of

APIs to support increased real-time information

exchange across systems, and the use of big

data analytics to improve credit underwriting,

evaluate product usage, and prioritize

opportunities for deepening relationships. As

financial-services organizations continue

to mature, the increasing demands on the

technology infrastructure to support more

complex use cases involving analytics and real-

Tamara Charm, Anne Grimmelt, Hyunjin Kim, Kelsey Robinson, Nancy Lu, Mayank, Mianne Ortega, Yvonne Staack, and Naomi Yamakawa,

“Consumer sentiment and behavior continue to reflect the uncertainty of the COVID19 crisis,” October 2020, McKinsey.com.

42 Beyond digital transformations: Modernizing core technology for the AI bank of the future

time insights are pushing firms to reexamine

their overall technology function. Once they have

committed to modernizing the core technology

and data infrastructure underpinning the

engagement and decision-making layers of the

capability stack, banks should organize their

transformation around six crucial demands:

technology strategy, superior experiences,

scalable data and analytics platforms, scalable

hybrid infrastructure, configurable product

processors, and cybersecurity strategy (Exhibit 1).

Robust strategy for building technology

capabilities

Before embarking on a fundamental

transformation of core technology and data

infrastructure, financial-services organizations

should craft a detailed strategy for building

an AI-first value proposition. They should also

develop a road map for the transformation,

focusing on three dimensions of value creation:

faster time to market with efficient governance

and productivity tracking, clear alignment of

demand and capacity to meet strategic and near-

term priorities, and a well-defined mechanism to

coordinate “change the bank” and “run the bank”

initiatives according to their potential to

generate value.

Faster time to market requires efficient and

repeatable development and testing practices

coupled with robust platforms and productivity-

measurement tools. Aligning demand and capacity

according to strategic priorities works on two

levels. On one level, banks need to ensure that

execution, infrastructure, and support capacity are

optimized to ensure constant operation of all use

cases and journeys. On the other, with constant

uptime assured, work should be organized and

scheduled to expedite projects having the greatest

impact on value. Finally, financial institutions

should establish clear mechanisms for setting

priorities and ensuring that each use case is

designed and built to generate a return exceeding

capital investments and operating costs.

Exhibit 1

The AI-bank transformation places several crucial demands on core technology

and data infrastructure.

The AI-bank transformation places several crucial demands on core

technology and data infrastructure.

Robust strategy for building

technology capabilities

Superior omnichannel journeys

and customer experiences

Modern, scalable platform for

data and analytics

Scalable hybrid infrastructure

strategy for the cloud

Highly congurable and

scalable core product processors

Secure and robust

perimeter for access

43Beyond digital transformations: Modernizing core technology for the AI bank of the future

Superior omnichannel journeys and customer

experiences

Building journeys that excite customers with

their speed, intuitiveness, efficiency, and impact

typically involves various applications spanning

multiple bank and nonbank systems, all linked

together by a series of APIs and integrations.

This complex information exchange enables the

organization to ingest valuable data from diverse

sources to produce highly personalized messages

and offers that speak directly to the customer

in near real time. In addition to a standardized

approach to managing APIs, banks should develop

a clear mechanism to integrate across channels,

core systems, and external interfaces while

managing changes across multiple dependent

systems. They should bear in mind, for example,

that introducing a change in an existing digital

channel could potentially entail changes not only

across the front end but also across multiple

interfacing systems, core product processors, and

analytics layers.

A focus on journeys and user experience also

benefits back-office and operations teams. New

products are increasingly automated at the back

end, freeing staff to focus on genuinely exceptional

scenarios and differentiating activities, rather than

repetitive low-value activities.

Finally, to ensure maximum value, use cases and

capabilities should be designed as “enterprise

products” to be reused in other areas. For example,

the deployment of microservices handling discrete

tasks like document collection and ID verification

can ensure consistency in the way things are

done across the organization. APIs should also

be documented and catalogued for reuse. APIs

that are domain- or product-centric (for example,

enabling the retrieval of customer details from a

single customer store) have higher reusability and

take an enterprise-level view of the capability, as

compared with a journey-centric API design—for

example, one where an API supports retrieval of

customer details for a specific mobile journey.

Modern, scalable platform for data and

analytics

Delivering highly personalized offers in near

real time requires AI-powered decision-making

capabilities underpinned by robust data

assets. What is more, the at-scale development

of machine-learning (ML) models that are

context aware in real time requires automated

DevSecOps² and machine-learning ops (MLOps)

tools to enable secure and compliant continuous

integration (CI) and continuous deployment (CD).

This entails complex orchestration across source

systems, data platforms, and data sciences

to enable lab experimentation and factory

production. This is particularly complex in a highly

regulated environment where the involvement of

security, audit, risk, and other functions is crucial

in many stages of the process.

The incorporation of feedback loops with channel

systems enables models to evaluate the output

performance and make automated adjustments

to increase the effectiveness of personalized

messages, so the organization can generate

personalized offers nearly instantaneously. For

example, in the case of location-based offers

for adjacent products, an organization must be

able to overlay in real time customer location

and preferences (as reflected in previous

transactions) with predefined offers from nearby

participating merchants.

Scalable hybrid infrastructure utilizing the cloud

With the continued expansion of customer

engagement across bank and nonbank platforms,

financial institutions need to create hyperscalable

infrastructure to process high-volume

transactions in milliseconds. This capability

is made possible, in part, by infrastructure

as code, automated server provisioning, and

robust automated configuration management

processes, which together solve the problem of

“snowflake” configurations resulting from organic

and complex linkages and changes that have

accumulated over time.

DevSecOps tools support the integration of “development, security, infrastructure, and operations at every stage in the product’s life cycle,

from planning and design to ongoing use and support.” See Santiago Comella-Dorda, James Kaplan, Ling Lau, and Nick McNamara, “Agile,

reliable, secure, compliant IT: Fulfilling the promise of DevSecOps,” May 2020, McKinsey.com.

44 Beyond digital transformations: Modernizing core technology for the AI bank of the future

Hosting these environments on a distributed-

network cloud environment allows a balance

between paid-up-front baseline storage and

computing capacity, on the one hand, and, on the

other, elastic on-demand surge capacity without

disruptions to service. Self-monitoring and

preventive maintenance also are automated, and

disaster recovery and resiliency measures run in

the background to ensure constant uptime even

if incidents evade automated self-repair and

require manual intervention. As a result, the risk

of disruption to critical operations is minimized,

and customer-facing applications run with high

availability and responsiveness. The combination

of on-premises and cloud-based infrastructure

is increasingly relevant in high-volume and high-

frequency areas such as payments processing,

core banking platforms, and customer

onboarding systems. Making workloads “cloud

native” and portable allows the work to be moved

to the most appropriate platform.

Highly configurable and scalable core product

processors

To sustain a leading-edge value proposition

founded upon AI and ML capabilities, banks

must continually evaluate their core products

and identify opportunities for innovations

and customizations. Combined with deep

understanding of customer needs, enabled

by advanced analytics, an organization can

anticipate emerging customer requests and

design distinctive products accordingly. The

need for real-time reconciliation and round-

the-clock transaction processing also emerges

as a key competitive advantage for financial

institutions. For example, with the advent

of next-generation core banking platforms,

organizations can now develop products that are

built for scale and can be readily configured to

meet specific customer expectations.³

Secure and robust perimeter for access

It is crucial to ensure that the organization

maintains an appropriate cybersecurity posture

across the entire technology infrastructure

as protection against vulnerabilities within

applications, operating systems, hardware,

and networks. Financial institutions should

also implement appropriate measures to

secure the perimeter and control access

to various systems and applications within

the organization’s infrastructure footprint,

including private and public cloud servers

and on-premises data centers. For example,

transferring workloads from traditional

on-premises infrastructure to public cloud

requires careful measures to protect customer

data, along with a robust strategy for detecting

and remediating potential threats and

vulnerabilities.

The “classical” approaches of securing the

perimeter should be coupled with more modern

approaches to limit the impact of intrusions or

reduce the “blast radius.” Again, AI has a part

to play here, given the advent of increasingly

sophisticated network intrusion detection,

anomaly detection, and even forensics during

postmortems of security incidents.

Start the transformation by

prioritizing key changes

To meet these demands, financial institutions

will need to transition from a legacy

architecture and operating model to an

automation and cloud-first strategy. Building

the core technology and data capabilities upon

a highly automated, hybrid-cloud infrastructure

can enable the AI bank to scale rapidly

and efficiently as it gains competitive and

differentiating capabilities.

The AI-bank capability stack combines core

systems and AI-and-analytics capabilities in

a unified architecture designed for maximal

automation, security, and scalability. Getting

to this target state requires a series of complex

initiatives to transform the organization’s core

technology and data infrastructure. These

initiatives focus on several key areas: tech-

Xavier Lhuer, Phil Tuddenham, Sandhosh Kumar, and Brian Ledbetter, “Next-generation core banking platforms: A golden ticket?” August

2019, McKinsey.com.

45Beyond digital transformations: Modernizing core technology for the AI bank of the future

forward strategy, modern API and streaming

architecture, core processors and systems, data

management, intelligent infrastructure, and

cybersecurity and control tower (Exhibit 2).

Tech-forward strategy

Banks should begin this far-reaching initiative by

translating the AI-first vision into an enterprise

strategy that merges technology with business,

funding investments in innovation with the returns

on incremental changes in technology. Business

and technology collaborate as co-owners in

designing and managing operating models

and outcomes. This “tech-forward” mindset

thrives in interdisciplinary teams focused on

innovation and led by skilled engineering talent

leveraging modern tools and practices for first-

time-right releases. Organizations should also

adopt enterprise agile practices for high-velocity

engineering teams, with integrated cross-functional

teams of business, technology, and functional

experts, and external partners using modern

approaches to software development, testing,

release, and support cycles. In addition, efficient

management of the full stack requires governance

of the technology function through a standardized

set of metrics, along with ongoing tracking of

uptime and health for each component of the stack.

Modern API and streaming architecture

Next, banks should integrate internal and external

systems to support seamless customer journeys

across internal platforms, partner ecosystems,

and numerous external interfaces. This requires

The technology transformation described in this and other articles in our series on the AI bank of the future aligns broadly with our colleagues’

discussion of the tech-forward approach, which applies across industries. See Anusha Dhasarathy, Isha Gill, Naufal Khan, Sriram Sekar, and Steve

Van Kuiken, “How to become tech-forward: A technology-transformation approach that works,” November 2020, McKinsey.com.

Exhibit 2

Building a modern core technology and data infrastructure entails

changes in several key areas.

Building a modern core technology and data infrastructure entails changes in

several key areas.

Tech-forward

strategy

Modern APIs and

streaming architecture

Core processors

and systems

Data management

for the AI world

Intelligent infrastructure

Cybersecurity and

control tower

Channels and digital-journey integrations

46 Beyond digital transformations: Modernizing core technology for the AI bank of the future

a robust, scalable, and standardized approach

to building and hosting integrations and APIs.

The APIs, in turn, should be rigorously tested for

performance and developed using agile release

principles. When a well-defined stock of APIs-as-

products are orchestrating flows across systems,

product innovations can advance from concept

to production and deployment of minimum viable

product within 30 to 60 days.

To complement a robust API strategy, technology

leaders should also consider establishing a

high-speed data-streaming channel to enable

standardized asynchronous data transfer across

the enterprise in real time.

Core processors and systems

With the right architecture in place, banks can

shift away from traditional, complex, and tightly

intertwined core systems to lightweight and

highly configurable core product processors

and workflows. These processors are also

complemented by “microservices,” or discrete

applications (such as for payments, card

accounts, or loans) that “externalize” the logic

within traditional core platforms.

The transition to lightweight core processors and

systems hosted on scalable, modular, and lean

platforms exposed as APIs supports, for example,

real-time reconciliation and allows changes to be

made in live systems with zero downtime. Use of

modern cloud-based infrastructure to host such

platforms also makes it easier to scale up.

If successfully implemented, a lightweight

processor platform can enable an organization

to advance from new-product concept to launch

in two to three months. This is a significant

advantage against organizations constrained

by legacy technology, where launching a new

product or customizing an existing product

can take six months or more. Assembly of new

off-the-shelf product stacks can also enable

innovative new customer propositions, such

as an end-to-end lending journey on a modern

stack using these principles.

Data management for the AI world

It is crucial to establish a modern data and

analytics platform to fuel the real-time ML

models of the decision-making layer. The

analytical insights generated by these models

are deployed through martech tools to craft the

intelligent offers and smart experiences that set

an AI bank apart from traditional incumbents. In

order to support superior omnichannel customer

journeys and seamless integration with partner

ecosystems, the data platform must be capable

of ingesting, analyzing, and deploying vast

amounts of data in near real time.

The data platform should also provide scalable

workbenches with AI and data-science

capabilities to lab and factory teams. These

workbenches enable teams to access relevant

data sets as they develop models and deploy

insights in product iterations. The infrastructure

should also support the development of ML

models through automated and repeatable

processes.

If an organization allows interdisciplinary teams

across the enterprise to search and extract

data held on the platform, these teams can

optimize their data consumption according to

customer needs and market opportunities. It

is essential to enable data-science teams with

appropriate tooling and access to scalable

computing power so that they may experiment

and innovate. Underpinning these actions,

appropriate technical documentation and

cataloging of assets (for example, APIs, ML

models, data dictionary, DevOps and MLOps

tools) ensure proper governance and access

control. By creating ML models and scorecards

through a well-defined lab-factory model,

AI-first organizations empower employees to

leverage self-serve, real-time data and analytics

infrastructure to guide value-based planning

and support daily decision making.

Intelligent infrastructure

Banks then should ensure they have an

effective strategy to modernize infrastructure.

For this, they should consider the adoption

of public cloud to complement the traditional

infrastructure in situations where workloads

require resiliency, scale, and use of hosted

or managed offerings (such as hosted

databases). Public cloud enables velocity

47Beyond digital transformations: Modernizing core technology for the AI bank of the future

through higher levels of automation, templates,

and reduction of operational risk. When setting

up such environments, banks must build upon

the foundational elements of infrastructure

management, including observability, resiliency,

and high availability, as well as a robust

configuration strategy. A well-tuned, scalable,

and load-balanced stack can support response

times of less than a second while scaling

horizontally to cater to variations in transaction

volume.

Cybersecurity and control tower

Finally, institutions should address cybersecurity

and control. This includes setting up a centralized

control tower to monitor data, systems, and

networks across the infrastructure. The scope

of responsibility includes ensuring boundary

security and identifying and rectifying threats

and intrusions. Also crucial is to establish a

well-defined set of compliance measures for

security testing and vulnerability scanning

before deploying assets on live systems. These

measures reduce the risk posed by potential

threat scenarios.

Technology leaders should prioritize

interconnected capabilities

Given the broad scope of components to be

transformed, organizations should bear in

mind that optimal outcomes are much likelier

when they first establish a holistic strategy for

technology transformation. Unfortunately, not

all have found the resources to embrace fully the

potential offered by the rapid advancement of

AI technologies and the steady rise in customer

expectations. Some financial institutions,

despite seeing the imperative to change, have

maintained and modernized their legacy

platforms. Various business lines have set up

organically built platforms upon this foundation,

making it costlier and more and more complex

to maintain. Many organizations have spent billions

of dollars on multiyear technology initiatives

within silos, only to find that they fail to generate

the scale benefits required to justify investments.

Leaders should heed these lessons, adopt a holistic

perspective, and map priorities according to the

end-to-end impact that each step in the technology

transformation has on the value of the enterprise.

If an organization meets the strategic demands

outlined at the top of this article, the implementation

of modern core technology and data infrastructure

can yield significant value in the form of faster

delivery of changes and improvements, increased

cost efficiency, higher quality of assets, and

stronger customer outcomes. For example, a sound

DevOps and release-management strategy can

contribute to a 25 to 30 percent increase in capacity

creation, a reduction in time to market of 50 to 75

percent, and more than a 50 percent reduction

in failure rates. In turn, development efforts can

improve schedule adherence by 1.5 times and

reduce customer defects by 20 to 30 percent

through process automation and agile ways of

working, and leading organizations have improved

issue-resolution time and planning time by between

30 and 50 percent. There are indirect benefits as

well: by empowering employees with a clear mission,

autonomy, and strong focus on customers, agile

organizations have been able to increase employee

engagement by 20 to 30 percent, as reflected both

in willingness to recommend their workplaces and in

employee-satisfaction surveys.

Technology transformations are fraught with risk,

including delays and cost overruns, and only those

organizations whose leaders are prepared to

commit the energy and capital necessary to carry

through with the comprehensive effort should

embark on the journey. Ultimately, this is a decision

not just to survive, but to thrive, and it requires a

change in mindset. Specifically, traditional financial

institutions will need to break out of their legacy

Thomas Delaet and Ling Lau, “DevOps: The key to IT infrastructure agility,” March 2017, McKinsey.com.

Matt Brown, Ankur Dikshit, Martin Harrysosn, Shivam Srivastava, and Kunal Thanki, “A new management science for technology product

delivery,” February 2020, McKinsey.com.

Wouter Aghina, Christopher Handscomb, Jesper Ludolph, Daniel Rona, and Dave West, “Enterprise agility: Buzz or business impact?” March

2020, McKinsey.com.

“Enterprise agility,” March 2020.

48 Beyond digital transformations: Modernizing core technology for the AI bank of the future

technology architecture and explore AI-and-

analytics opportunities. Should they undertake

the challenge and begin thinking about how

best to chart their course to becoming an AI

bank, their leaders may consider 12 key insights

gleaned from the experience of financial-

services leaders that are in the process of

carrying out such transformations (Exhibit 3):

1. Consider the factory model to build at scale.

Leverage a factory approach in fast-evolving

and critical areas of the transformation to

enable repeatable execution and development

of capabilities within technology teams and to

promote standardization to speed up execution.

For example, a core system factory consisting

of teams, predefined operating procedures,

and systems to manage, prioritize, and execute

changes across business units can expedite

deployment of new solutions significantly.

2. Consider insourcing differentiating capabilities.

Based on the eventual outcomes desired, build

certain differentiating capabilities in-house,

Exhibit 3

Leaders should consider 12 key insights as they embark on the technology-

transformation journey.

Leaders should consider 12 key insights as they embark on the technology-

transformation journey.

Tech-forward strategy

Modern APIs and

streaming architecture

Core processors and systems

Cybersecurity and control tower

Intelligent infrastructure

Data management for

the AI world

Consider the factory model to build at scale

Consider insourcing dierentiating capabilities

Maintain rigorous documentation on integrations

Identify an anchor stack but experiment with others

Maintain automation-rst and fast-release posture

Consider a modern core for high-velocity areas

Adopt a value-centric approach to building data platforms

Set up a lab and factory for analytics

Dene the enterprise cloud strategy

Establish end-to-end visibility across the stack

Identify the right perimeter design for the cloud

Ensure data security on the cloud

49Beyond digital transformations: Modernizing core technology for the AI bank of the future

with robust engineering support, perhaps

starting with APIs, infrastructure, or the data

and analytics platform.

3. Maintain rigorous documentation on

integrations. Remember that the development

of engagement systems and comprehensive

changes in core-technology require significant

adjustments to integrations, and substandard

documentation of the specifications for these

integrations often slows the broader initiative

to transform the bank.

4. Identify an anchor stack but experiment

with others. Emphasize the importance of

standardization for engineering-centric

development at scale, and build on a single

stack to support faster change. At the same

time, continue experimenting with other stacks

and stack components for smaller builds in

order to adopt alternative or newer approaches

where the incremental benefits are clearly

defined.

5. Maintain an automation-first and fast-

release posture. Adopt an automation-first

and frequent-deployments posture on fast-

evolving applications and stacks. While initial

hiccups are not uncommon, release rails

should be hardened over time to speed up time

to market. Well-defined release management

and deployments are key to execution velocity.

Standardizing through DevSecOps typically

unlocks productivity gains of as much as 20 to

30 percent.

6. Consider a modern core for high-velocity

areas. Consider modern and lightweight

core systems built on scalable and hybrid

infrastructure to enable an efficient rollout of

new capabilities while enabling a modular build

of financial products.

7. Adopt a value-centric approach to building

data platforms. Take advantage of the fact that

data and analytics platforms evolve over time,

and do not allow teams to be overwhelmed

by the rapid shift of tooling and available

technology. We have observed that organizations

that budget the anticipated return of change

efforts are able to prioritize use cases that are

functionally simple, fit the road map for building

the platform in iterations, and realize economic

value along the way.

8. Set up a lab and factory for analytics. Establish

a lab to experiment with tools and platforms for

efficient development in test-and-learn cycles.

Also, build a central factory for producing and

deploying analytics use cases at scale on an

individual stack.

9. Define the enterprise cloud strategy. Create a

common strategy across stakeholders to enable

a structured and systematic migration to the

cloud. Cloud adoption poses multiple firsts in

the enterprise in terms of security perimeters,

change management, and cloud-migration and

disposition strategy.

10. Establish end-to-end visibility across the

technology and infrastructure stack. Recognizing

that at-scale digital transformations impose

limitations on volume and scale, implement robust

automated tools to observe stack performance

and to diagnose and resolve issues.

11. Identify the right perimeter design for the cloud.

To safeguard against potential malicious attacks

on cloud-based public-facing applications,

design an appropriate network perimeter that

optimizes the potential attack radius.

12. Ensure data security on the cloud. Design

robust data-categorization and data-security

safeguards to avoid critical customer-data

combinations and comply with national data-

protection and data-residency laws.

If banks are to thrive in a world where customer

expectations are increasingly shaped by the

AI-and-analytics capabilities of technology leaders,

they must rebuild their core technology and data

infrastructure to support AI-powered decision

50 Beyond digital transformations: Modernizing core technology for the AI bank of the future

making and reimagined customer engagement.

These are the three “technology layers” of the

AI-bank capability stack. The full stack also

includes a leading-edge operating model to

ensure that all layers work together in unison to

deliver intelligent propositions through smart

servicing and experiences. The AI bank of the

future requires an agile culture and platform-

oriented operating model that respond promptly

to emerging opportunities and deliver innovative

solutions rapidly at scale. The next article in

this series examines the crucial elements of the

platform operating model.

Sven Blumberg is a senior partner in McKinsey’s Dusseldorf office, Rich Isenberg is a partner in the Atlanta office, Dave Kerr is

a senior expert in the Singapore office, Milan Mitra is an associate partner in the Bengaluru office, and Renny Thomas is a senior

partner in the Mumbai office.

The authors would like to thank Brant Carson, Kayvaun Rowshankish, Yihong Wu, and Himanshu Satija for their contributions to

this article.

51Beyond digital transformations: Modernizing core technology for the AI bank of the future

Global Banking & Securities

Platform operating model

for the AI bank of the future

Technology alone cannot define a successful AI bank; the AI bank

of the future also needs an operating model that brings together the

right talent, culture, and organizational design.

May 2021

by Brant Carson, Abhishek Chakravarty, Kristy Koh, and Renny Thomas

As we noted at the beginning of this series on the

AI bank of the future, disruptive AI technologies can

dramatically improve banks’ performance in four key

areas: higher profits, at-scale personalization, smart

omnichannel experiences, and rapid innovation

cycles. The stakes could not be higher, and success

requires a holistic transformation spanning all layers

of the organization’s capability stack.

Our previous articles have focused on the capability

stack’s technology layers: reimagined engagement,

AI-powered decision making, and modern core

technology and data infrastructure. Leveraging

these capabilities to create value requires an

operating model combining structure, talent, culture,

and ways of working to synchronize all layers of the

stack. Synchronizing these layers is not easy. Any

organization undertaking an AI-bank transformation

must determine how to structure the organization

so that its people interact and leverage tools and

capabilities to deliver value for each customer at

scale. In this article, we take a closer look at the

need for a platform operating model, the categories

and scope of operating models, and the building

blocks of effective models.

The heart of an AI bank is always-on

customer interaction

The need to change a bank’s operating model

arises from a combination of external and internal

circumstances. Externally, as consumers and

businesses increasingly rely on AI technologies in

daily life, banks are shifting the foundation of their

business models from products to experiences.

In other words, as many traditional banking

products become embedded—or even “invisible”—

within beyond-the-bank journeys, experiences

become the more salient element of a customer’s

relationship with the bank. This shift involves a rapid

increase in the number of customer interactions,

and at the same time, the revenue associated with

each interaction is declining. This is a fundamental

change: just a few years ago, customers conducted

business with the bank by visiting a branch once or

twice a month; more recently, they would conduct

transactions several times each week through the

bank website; now many customers interact with

their bank daily through their mobile banking app,

and often several times a day through wearable

devices. In short, banks and their customers now

have an interconnected, always-on relationship.

Circumstances within the bank are changing as

well—albeit at a slower pace, due largely to the

complexity of legacy technology and operating

models coupled with the steadily rising cost of

maintaining and upgrading IT infrastructure. Siloed

structures also hamper organizations’ ability

to transform themselves. Decision making at

traditional banks is typically slow and cumbersome,

and ineffective prioritization (done at too high a

level without understanding underlying resource

contentions) results in frequent project delays

and cost overruns. Insufficient domain expertise

and blurred accountability—particularly between

business units and technology teams—too often

cause new solutions to fall short of customer

expectations. What is more, multiple systems

perform similar functions, and the increasing

complexity of IT architecture with a proliferation of

applications weakens system resilience and stability

and increases risk when changes are made.

The widening divide between fast-evolving

customer expectations and inertia within the bank

reinforces silos and weakens the bank’s ability to

respond to the demands of the new machine age.

The challenge for leaders is to shift the organization

from this siloed structure to a radically flattened

network of platforms.

Platforms focus on delivering business

solutions

Today, banks that recognize the value of AI and

technology enabling better customer and business

experiences are moving steadily toward a platform

operating model, leveling command-and-control

structures to speed decision making and bring

people together in teams relentlessly focused on

delivering solutions that customers value. In this

agile approach, each platform can be thought of

as a collection of software and hardware assets,

funding, and talent that together provide a specific

capability. While some platforms, such as those

for retail mortgages, deliver business-technology

solutions to serve internal or external clients, others

53 Platform operating model for the AI bank of the future

enable other platforms with shared services and

support functions (for example, payments and core

banking). Each platform is largely self-contained in

producing business and technology outcomes and

autonomous in prioritizing its work to meet strategic

goals within clearly defined guardrails, such as

common standards, finance, and risk control.

Platform elements

As banks think about setting up a platform operating

model, they should bear in mind that each platform

comprises three main elements. When structured

correctly, these elements will help a platform team

set its North Star and carry out its mission in a way

that creates value for customers and the enterprise.

— Strategy and road map. The joint vision

combines business and technology outcomes

to deliver end-to-end value. Close alignment

between the business unit and the technology

group on performance objectives and agenda

unites all members of the platform around a

shared strategic vision, with a road map for

executing priorities that balance change and

resiliency.

— Organization and governance. Organization

of business-facing platforms (e.g., retail

mortgages) should be based on a “two in a

box” engagement model, meaning business

and technology leaders own joint performance

metrics that track both commercial and

technological outcomes. Each platform

manages its business and technological

priorities through a shared backlog of work and

delivers through persistent cross-functional

agile teams, each of which builds its platform

over time and focuses not only on one project,

but continually improves the platform.

— Technology. Each platform owns its technology

landscape and standardized interaction

mechanisms with other platforms (for example,

leveraging APIs). It also has an inherent

objective to modernize its technology.

Platform categories

In most cases, a platform can be thought of

as a nimble fintech group in one of three main

categories: business platforms, enterprise

platforms, or enabling platforms (Exhibit 1).

Exhibit 1

The platforms crucial to a bank’s success can be grouped into three categories.

Source: McKinsey & Company

The platforms crucial to a bank’s success can be grouped into three categories.

Business

platforms

Platforms directly aligned to a business unit to deliver business and technology

outcomes (eg, revenue growth, protability)

Enabling

platforms

Platforms not aligned to a business unit

• Provide scale benets through consolidation

• Safeguard the bank by dening guardrails

• Enable business and enterprise platforms to deliver business outcomes

Example platforms

Enterprise architecture

IT infrastructure

Cybersecurity

Consumer lending

Cards

Wealth management

Core banking

Payments

Analytics and data

Finance

Risk

Enterprise

platforms

Platforms aligned to multiple

business units to deliver

outcomes across units

Enterprise

shared

services

Act as service providers

largely enabling other

platforms

Tech assets providing similar

services aggregated to create

a center of excellence

Enterprise

support

units

Act as business owners

delivering services

across the enterprise

54Platform operating model for the AI bank of the future

Business platforms are aligned to business

units and deliver joint business-and-technology

outcomes. As an example, a business platform for

consumer lending would include several cross-

functional teams, each of which owns front-end

technology assets and includes business teams for

a specific function or service area.

One team might focus on preapproval and new-

customer acquisition, with responsibility for

next-generation credit-scoring models using

traditional data sources (such as credit bureau

reports and internal transaction histories)

and nontraditional sources (including, upon

the customer’s permission, tax returns, online

presence, partner ecosystem transactions, and

more). Another team often takes responsibility

for loan underwriting, determining credit limits for

individual accounts in accordance with enterprise

risk policy. A third team might focus on consumer

insights and personalized messaging, including

machine-learning decision models and marketing

technology (“martech”) tools to deliver intelligent

credit offers to new and existing customers. The

customization team owns the design, development,

and management of product configurations

to ensure that each solution addresses the

customer’s precise needs.

Other teams focus on services and capabilities to

support external developers and other technology

partners, including, for example, partner

onboarding and sandbox management and APIs

supporting customer journeys and experiences

(managing standards and documentation through

development hubs or platforms). Still other

teams support the consumer lending platform by

managing technology—for example, provisioning

of cloud infrastructure.

Enterprise platforms enable diverse business

platforms by providing shared services such

as vendor management and procurement,

standardization of cloud and DevSecOps tooling,¹

build-to-stock process APIs and reusable

microservices, and standardized data access and

governance. Other enterprise platforms aggregate

support functions such as finance, risk, and human

resources within a center of excellence.

Enabling platforms support other platforms by

ensuring that technical functionality is delivered

quickly and securely at scale. This approach has

proven effective at maximizing scale benefits

while protecting the enterprise with standardized

processes. Examples of enabling platforms include

core technology infrastructure, DevOps tools and

capabilities, and cybersecurity.

Implementing a platform operating

model requires five main building

blocks

The distinct advantage of a platform operating

model is the foundation it provides for business-

and-technology partnerships focused on delivering

leading-edge AI-enabled solutions (Exhibit 2). As

they begin planning the transition from hierarchical

silos to a network of horizontally interconnected

platforms, bank leaders should focus on five main

building blocks: agile ways of working, remote

collaboration, modern talent strategy, culture and

capabilities, and architectural guardrails. The value

and efficiency that can be derived from platform

operating models are possible only if organizations

design their operating model to enable these five

elements. Once they have established their vision

of the new management approach, they should

develop a road map for implementing the platform

model.

1. Agile ways of working

By extending the platform structure to all groups,

an organization gains the ability to quickly redirect

their people and priorities toward value-creating

opportunities.² For this model to work, however,

banks need to develop agile mindsets within each

team and equip team members with agile ways

of working, such as rapid decision and learning

cycles, breaking initiatives into small units of

DevSecOps tools integrate security measures with DevOps processes.

Wouter Aghina, Christopher Handscomb, Jesper Ludolph, Daniel Rona, and Dave West, “Enterprise agility: Buzz or business impact?,” March

2020, McKinsey.com.

55 Platform operating model for the AI bank of the future

work, piloting new products to get user input, and

rapidly testing operational effectiveness before

scaling.³ This methodology, when deployed across

the organization, underpins a new corporate

culture that enables fast communication and

collaboration within and among platforms. It gives

the organization a strong and stable backbone for

developing and scaling dynamic capabilities.

The starting point depends on where the bank is

in its technology transformation. Some may set up

an agile pilot within a platform and gradually train

other groups in the new practices. For banks where

diverse groups have already achieved a degree of

organizational and operational flexibility, the time

may be right for an end-to-end transformation

program that “flips” the organization to agile.

Each platform consists of one or multiple squads or

pods combining IT, design, and customer-journey

experts, among others (up to nine people). Banks

should also create “chapters” as cross-squad

groups of employees with similar functional

competencies to ensure growth of expertise and

cross-training of colleagues across technologies.

In some cases, a bank will need to create new roles,

Exhibit 2

The platform operating model for consumer lending captures the agility of a ntech

and scale of a large enterprise.

Source: McKinsey & Company

The platform operating model for consumer lending captures the agility of a

ntech and scale of a large enterprise.

Illustrative future state of consumer lending platform Key shifts

Business

platforms

Enterprise

platforms

Enabling

platforms

Business platforms completely own

front-end technology assets and a

cross-functional team for

— Onboarding and sandbox for partners

— Experience APIs controlled via contracts

— Consumer-lending specic

microservices for bureau, credit scoring

(new build)

— Provisioning of cloud infrastructure

— Consumer-lending specic AA models

(from IT)

— Product conguration

— Credit underwriting adjustments (in line

with overarching risk policy)

Enterprise platforms provide core shared

services for business platforms, including:

— Standardization of cloud/DevSecOps

tooling

— Standardized data access and governance

Only few core technology foundations like

enterprise infrastructure and DevOps

would be common across the bank

Consumer lending platform

• External partnerships

• Credit underwriting

• Consumer lending policies

• Product denition

Core banking services

Payments

Analytics and data (data lake, standards, analytical tools,

governance)

Enterprise architecture

Delivery enablement (DevOps)

Cybersecurity and technology risk

Infrastructure/site reliability engineering

Cloud infrastructure and applications

Experience APIs for digital commerce

ecosystems

Credit limit

Lending

Bureau check

Credit model

Loan cross-sell model

Wealth Cards

Standalone

micro-services

Anusha Dhasarathy, Isha Gill, Naufal Khan, Sriram Sekar, and Steve Van Kuiken, “How to become tech-forward: A technology-transformation

approach that works,” November 2020, McKinsey.com.

56Platform operating model for the AI bank of the future

such as tribe leaders and agile coaches. It is also

crucial to adopt a performance-management model

that aligns all individuals with team goals.

The agile way of working is a means to an end, not

an end in it itself. As banks begin to implement a

platform operating model, it is crucial that they

set a North Star, not only to unite people around

business goals but also to offer them a sense of

meaning and purpose within society. Shared values

reinforce team spirit and—when combined with

opportunities to learn, experiment, and make a

difference for customers—strengthen employee

engagement. This stronger employee engagement

can be measured in, for example, productivity and

loyalty and can indicate how well an organization

has embraced the agile transformation.

2. Remote collaboration

For a variety of reasons, including geographic

distribution, work-from-home policies, travel

restrictions, and other disruptions due to COVID-

19, banks have moved to a fully or partially

remote model. The sharp decline in co-location

has put pressure on organizations to improve

collaboration and consistency in ways of working.

Given the expectation that a significant share of

bank employees may not return to shared work

environments, banks need to develop mechanisms

to support effective collaboration—and thus reduce

errors—in distributed environments.

Indeed, banks need to revisit agile teams after an

abrupt shift to remote models and consider the

types of work to be done remotely according to how

well interaction models and system readiness can

be adapted. Two criteria are key for determining

which roles can function effectively in remote

work arrangements. First is the required level of

human interaction, such as the degree of real-time

collaboration and creative work among groups

of people and the degree to which work can be

segmented and individualized. Second is bank

systems’ readiness—particularly in terms of data

accessibility, software accessibility, and tooling—

to support secure and efficient remote work.

For example, setting clear decision-making

and escalation paths is essential to maintain

a fast cadence. Shared workflows, roles, and

responsibilities help move work through the

pipeline for even the most complex and highly

interactive jobs.

Setting up a single source of truth or single

backlog of work also helps keep different

platforms aware of interdependencies. What is

more, banks can and should ensure the security

of remote working arrangements by leveraging

specialized technology for managing remote

access. Areas subject to management may

include data retrieval (role-based access to

data, restrictions in downloading sensitive data,

restriction of all data copying even on encrypted

removable hard drives), sophisticated detection

(tracking and monitoring mechanisms to detect

data breach), and governance procedures to

review breaches and enforce corrective actions.

Banks should also set up mechanisms to address

both interaction and security criteria. These

mechanisms are particularly crucial for remote-

working arrangements, which are increasingly

important to top talent in technology-intensive

industries, including financial services.

3. Modern talent strategy

A modern talent strategy for an AI bank is not only

about the commitment and capability to hire the

best engineering talent or the best business talent.

The AI-bank operating model also requires leaders

to rethink their strategy for hiring and retaining

top talent in a world with blurring lines between

business, IT, and digital expertise. Leaders must

form a detailed picture of the diverse skills and

expertise required to deliver business-technology

solutions. Reskilling is equally critical to building

teams with the right mix of talent.

Susan Lund, Anu Madgavkar, James Manyika, and Sven Smit, “What’s next for remote work: An analysis of 2,000 tasks, 800 jobs, and nine

countries,” McKinsey Global Institute, November 2020.

Santiago Comella-Dorda, Lavkesh Garg, Suman Thareja, and Belkis Vasquez-McCall, “Revisiting agile teams after an abrupt shift to remote,”

April 2020, McKinsey.com.

57 Platform operating model for the AI bank of the future

This strategy focuses on attracting digital talent

and requires that leaders understand the unique

needs of digital talent. It employs a diversified

approach to recruiting: engaging with technologist

communities, sponsoring hackathons to scout

talent, and ensuring that recruiters have experience

in technology. The best technical talent has a

disproportionately higher impact, so the ability to

attract and develop superior candidates is crucial. In

a similar vein, leading tech organizations enlist their

top performers in the recruiting effort.

Furthermore, banks need to improve retention

and reskilling. Reskilling may involve charting a

clear career development path for digital talent,

creating an environment that prioritizes and rewards

learning, and rewarding deep expertise over

fungible skill sets. There is also opportunity to build

capability-development programs that help reskill

nontechnical colleagues as technologists. Finally,

so that attracting and developing digital talent can

produce the desired results, banks need a clear

strategy for retaining this talent, such as providing

flexible and collaborative ways of working and

empowering digital talent to implement change.

To develop a comprehensive talent strategy, an

AI bank would first review existing initiatives, the

structure and makeup of each platform, and the

technical talent required to execute the strategy.

The second step is to build from the ground up

a model of talent required for the next stage of

growth, including both existing and future initiatives.

Next, it is important to create a set of talent

interventions that can tap into existing talent within

the organization, developing an “ecosystem” of

partners (vendors, developers, gig workers, remote

talent, and others) and using hiring mechanisms,

including the acquisition of smaller companies

and start-ups, to establish platforms requiring

skills beyond the traditional scope of the bank’s

roles and capabilities. Finally, banks have to make

themselves externally appealing to fresh tech talent

and internally exciting for their people. This means

transforming themselves so top technical talent

want to stay and grow within the organization and

so all employees see and embrace the change and

invest in upgrading their skills. In short, banks need

to become great engineering organizations.

4. Culture and capabilities

As banks build sophisticated technical solutions,

they also need to develop a culture suited to the

experts building these solutions. Organizations

need to manage culture and capabilities to create

a virtuous circle that attracts talent, sparks

innovation, and creates impact. This underscores

the importance of talent and culture in tech-enabled

transformations, including AI-bank transformations.

For the platform operating model to work, leaders

need to steer their organizations to focus on

the end user, collaborate across silos, and

foster experimentation. Establishing this digital

culture across the bank involves addressing four

dimensions of culture: understanding/conviction,

reinforcement, reskilling, and interaction.

First, understanding and conviction follow largely

from the bank’s leadership, expressed through

role modeling and encouraging desired behaviors,

including continuous learning, knowledge-sharing,

and interdisciplinary collaboration. For example, if

a top team visibly takes part in upskilling programs

for AI and machine learning, this demonstrates to

all in the organization the importance of automation

and evidence-based decision making to all parts

of the business. Another approach is to support

technology start-ups by giving them access to

nonsensitive code and shareable data to build their

own “open solutions” related to AI banking.

The second is to reinforce new practices with formal

mechanisms, so that the structures, processes, and

systems of the AI bank become embedded within

the culture. For example, banks might consider

organizing institution-wide innovation challenges

or inviting managers to daily huddles where they

actively work with the centers of excellence to solve

problems and own outcomes.

Abhishek Chakravarty, Dave Kerr, and Nina Magoc, “10 Principles That Build Great Engineering Organizations,” March 26, 2021, medium.com.

Reed Doucette and John Parsons, “The importance of talent and culture in tech-enabled transformations,” February 2020, McKinsey.com.

58Platform operating model for the AI bank of the future

Third, leaders need to ensure that every individual

has access to the skills they require to be effective.

One way to do this is by developing entirely new

tools and technology using in-house open-source

systems. Another is to ensure transparency by

setting up digital wikis that anyone can use to

access knowledge. Organizations can also learn

from others by sending employees on “innovation

tours” or actively encouraging and sponsoring

attendance at high-quality conferences.

Finally, leaders should model various approaches

to interaction. Banks can visibly change the

ways managers interact with teams, such as by

moving from meetings to offline asynchronous

communications using highly collaborative tooling.

Leaders can also use symbols in remote and

in-person meetings to emphasize enterprise values

such as customer centricity. At a leading bank, for

example, every meeting has an empty chair to

remind participants of the customer for whom they

are building solutions.

5. Architectural guardrails

Each platform is responsible for its own technology

landscape, but standardized mechanisms for

interaction among platforms should be jointly

designed across all platforms. It is important,

therefore, to ensure that architectural guardrails

are observed so that each platform can easily

interact with others. These guardrails should not be

perceived as restricting platforms from developing

and improving their own technology and technical

decisions.

As each platform is free to build the

technology elements required to deliver on its

mandated business goals, there is potential

for miscommunication among platforms. For

example, instead of developing its own interest

rate calculation, a consumer lending platform

would leverage a single, standard calculation via

an API. With no guardrails in place, there would

be significant inefficiency, because efforts would

be duplicated in some areas and tasks would

be unfinished in others. By contrast, guardrails

support efficient management and operation of the

overall IT landscape, with responsibility for various

elements of the enterprise architecture delegated to

individual platforms. These various responsibilities

are formally documented and communicated widely.

Without such guardrails, inefficiencies would

multiply.

These architectural guidelines should focus on

strategic activities rather than operational tasks,

which are subject to the discretion of the platform.

This requires significant time upfront for strategic

planning, and each platform must stay alert to new

value-creation opportunities related to its mandated

strategic objectives.

Further, platform owners can evaluate the

effectiveness of these guardrails by tracking the

number of business capabilities in accordance with

these guardrails, rather than simply counting the

various technology applications found within the

organization.

Mapping the operating model of a

financial-services organization

A large global or regional AI bank implementing a

platform-based operating model would typically

have 20 to 40 platforms, each focused on a specific

type or set of services, such as payments, lending,

infrastructure, or cybersecurity (Exhibit 3). As noted

above, these platforms are often grouped into one

of three areas.

— Business platforms typically include a consumer

platform, which is linked to channels (digital,

branch) and products (wealth, consumer) as

well as customer relationship management

and analytics; a corporate platform, which

spans channels and products (transaction

banking, lending) and relationship management

(corporate servicing); and a global-markets

platform, which covers channels, products, and

global market operations, as well as market and

credit risks.

— Enterprise platforms provide shared services

across different business platforms across the

enterprise on administrative elements such as

customer servicing; employee services; finance;

HR; risk, legal, and compliance; and technology

platforms usable by business platforms such

59 Platform operating model for the AI bank of the future

as payment infrastructure, cloud infrastructure,

data, and API management.

— Enabling platforms support business and

enterprise platforms to deliver technical

functionality quickly. These platforms include

enterprise architecture, delivery enablement,

access and authentication management,

cybersecurity, and infrastructure/site reliability

engineering (SRE).

The platform model can help

organizations seize new opportunities

Executing on a platform operating model is arduous.

However, when done correctly, it has the potential

to deliver four main benefits to all stakeholders:

value-oriented business-technology partnerships,

stronger performance (speed, efficiency, and

productivity), transparency, and a future-ready

business model.

Exhibit 3

If implemented across the bank, the platform operating model can enable each group

to optimize performance.

Source: McKinsey & Company

If implemented across the bank, the platform operating model can enable

each group to optimize performance.

Business

platforms

Enterprise

platforms

Enabling

platforms

Digital and assisted digital channels

Branches and self-service banking

Wealth products

Consumer products

Consumer banking operations

Customer marketing and analytics

Payments utility (fullment and settlement,

payment interfaces, remittances)

Customer servicing (reconciliation, digital servicing)

Analytics and data (data lake, standards, analytical

tools, governance)

Employee services (intranet, facilities booking,

video conferencing, end-user computing)

Core banking

Consumer platforms Corporate platforms Global Markets

Digital channels

Trading

Product control

Global markets operations

Market risk

Credit risk

Sales and analytics

Digital and assisted digital channels

Transaction banking (securities and

duciary services, trade nance and

cash management)

Lending and other products

Corporate servicing and operations

Customer marketing and analytics

Finance and HR (recruiting, talent management,

HR policies, accounting)

Risk (credit, market, operational, and liquidity risk)

Compliance

Group services (eg, strategic vendor management,

real estate, project management oce)

Enterprise architecture (application/data/infrastructure architecture, API standards)

Delivery enablement/ITSS (DevOps, agile, test automation, service monitoring)

Access management (eg, single sign-on, authentication, token management)

Cybersecurity and technology risk

Infrastructure/site reliability engineering

API management (tech and operations for all APIs)

Cloud infrastructure and applications

60Platform operating model for the AI bank of the future

The collaborative framework of the platform model

brings business and technology leaders together

as co-owners in creating value for the enterprise.

Joint owners of business-facing platforms share

accountability for outcomes, merging business

knowledge of market opportunities with expert

insight into how technological advances can

enhance customer experiences. The leader of the

platform facilitates the interaction of business

and technology owners in determining the right

balance between run-the-bank and change-the-

bank initiatives. All members of a particular team

are unified in delivering a solution (just as those

of the entire “tribe” of a platform are focused on a

service line) in order to create value in alignment

with enterprise strategic objectives. This unity is

reinforced by the fact that all team members share

in performance metrics for both business and

technology outcomes, including impact on users

(internal and external), on-time delivery of solutions,

customer and employee satisfaction ratings,

and more.

The platform approach can strengthen an

organization’s performance in terms of speed,

efficiency, and productivity when each platform is

large enough to address a set of use cases crucial

to realizing the business model of the enterprise

but small enough to keep the team agile. Each

team enjoys a degree of autonomy, with a budget

and mandate to experiment and discover the best

way to maximize value within a discrete domain in

alignment with predefined guardrails (for instance,

finance, risk, compliance) without having to wait

for approvals from finance and allocations from

IT and human resources. This autonomy speeds

up decision making, innovation, and solution

delivery. The use of automated tools, enterprise

standards, and agile patterns of communication and

collaboration increases efficiency in two ways. First,

this approach minimizes duplication of effort by

documenting repeatable processes and cataloging

technology tools and analytical models available for

deployment in diverse contexts. Second, it allows

individuals to access data (according to clearly

defined need-to-know criteria) and advanced

analytical tools to extract insights to augment their

impact. Over time, persistent agile teams build their

domain expertise and agile skills for collaboration

and timely delivery.

In addition to the emphasis on interdisciplinary

collaboration, the platform model is designed

to increase transparency, accountability, and

knowledge sharing to the fullest extent possible.

Transparency should be high not only so employees

can clearly identify the services available from

each platform but also to support independent

benchmarking of team performance and

identification of best practices. Each platform

should also be clear about how it prioritizes work,

tracks initiatives in the pipeline, and manages the

backlog.

Finally, shifting to a platform model can help

an organization future-proof its business

model because each platform is incentivized to

continuously improve on its technology landscape.

Within a culture of continuous learning, team

members are accustomed to change and adept

at finding the best response to fast-evolving

circumstances. Interdisciplinary initiatives led by

business-technology co-owners strengthen a

team’s capacity to anticipate and consider potential

challenges and opportunities before they appear

on the horizon. Enterprise-wide standards, rigorous

documentation of processes, and consistent

cataloging of technology assets enable teams to

apply best practices as they develop and implement

new solutions.

By underpinning business-technology

co-ownership of solutions delivery and value

creation, the platform operating model offers

banks an opportunity to maximize the impact of

their technology capabilities in ways that count for

customers. The implementation of the platform

model begins logically with the formation of joint

business-and-technology teams focused on the

design, development, and implementation at scale

of new AI-bank innovations, always striving toward

a more intelligent value proposition and smarter

experiences and servicing. Further, the creation

of cross-functional platforms is also an excellent

61 Platform operating model for the AI bank of the future

approach to increase business–technology

collaboration, developing an IT operating model

that generates immediate and tangible business

value and moves the full organization, not just

technology, to an agile way of working. However,

to derive maximum value from platforms and the

people who make up these platforms requires new

skills, mindsets, and ways of working. Bringing all

these elements together is a powerful mechanism

to optimize the full capability stack, from core

technology and data infrastructure to AI-powered

decision making and reimagined customer

engagement. The platform operating model

ensures that these layers run in sync to spur the

growth of an AI bank of the future.

Brant Carson is a partner in McKinsey’s Sydney office; Abhishek Chakravarty is an associate partner in the Singapore office,

where Kristy Koh is an associate partner; and Renny Thomas is a senior partner in the Mumbai office.

62Platform operating model for the AI bank of the future

Contacts

For more information about this report, please contact:

Akshat Agarwal

Associate Partner, Bengaluru

Akshat_Agarwal@mckinsey.com

Anubhav Bhattacharjee

Solution Leader, Mumbai

Anubhav_Bhattacharjee@mckinsey.com

Suparna Biswas

Partner, Mumbai

Suparna_Biswas@mckinsey.com

Sven Blumberg

Senior Partner, Düsseldorf

Sven_Blumberg@mckinsey.com

Brant Carson

Partner, Sydney

Brant_Carson@mckinsey.com

Abhishek Chakravarty

Associate Partner, Singapore

Abhishek_Chakravarty@mckinsey.com

Violet Chung

Partner, Hong Kong

Violet_Chung@mckinsey.com

Malcolm Gomes

Partner, Bengaluru

Malcolm_Gomes@mckinsey.com

Rich Isenberg

Partner, Atlanta

Rich_Isenberg@mckinsey.com

Xiang Ji

Partner, Shenzhen

Xiang_Ji@mckinsey.com

Dave Kerr

Senior Expert, Singapore

Dave_Kerr@mckinsey.com

Kristy Koh

Associate Partner, Singapore

Kristy_Koh@mckinsey.com

Milan Mitra

Associate Partner, Bengaluru

Milan_Mitra@mckinsey.com

Kayvaun Rowshankish

Partner, New York

Kayvaun_Rowshankish@mckinsey.com

Anushi Shah

Consultant, Mumbai

Anushi_Shah@mckinsey.com

Shwaitang Singh

Partner, Mumbai

Shwaitang_Singh@mckinsey.com

Charu Singhal

Consultant, Mumbai

Charu_Singhal@mckinsey.com

Yihong Wu

Practice Manager, Hong Kong

Yihong_Wu@mckinsey.com

Renny Thomas

Senior Partner, Mumbai

Renny_Thomas@mckinsey.com

Editor: John Crofoot

Design and production: Matt Cooke, Kate McCarthy, Paul Feldman

A special thank you to: Arihant Kothari, Amit Gupta, Himanshu Satija, Jinita Shroff, Vineet Rawat

63 Building the AI bank of the future

May 2021

www.mckinsey.com

@McKinsey