BUILDING THE AI BANK OF THE FUTURE - MAY 2021 GLOBAL BANKING PRACTICE - MCKINSEY
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Global Banking Practice
Building the AI bank
of the future
May 2021
© Getty Images
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
4 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.
18 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.
29 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.
41 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.
52 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.
Introduction
Banking is at a pivotal moment. Technology leaders recognize that the economies of scale
disruption and consumer shifts are laying the basis afforded to organizations that efficiently deploy AI
for a new S-curve for banking business models, technologies will compel incumbents to strengthen
and the COVID-19 pandemic has accelerated customer engagement each day with distinctive
these trends. Building upon this momentum, experiences and superior value propositions. This
the advancement of artificial-intelligence (AI) value begins with intelligent, highly personalized
technologies within financial services offers banks offers and extends to smart services, streamlined
the potential to increase revenue at lower cost by omnichannel journeys, and seamless embedding
engaging and serving customers in radically new of trusted bank functionality within partner
ways, using a new business model we call “the AI ecosystems. From the customer’s point of view,
bank of the future.” The articles collected here these are key features of an AI bank.
outline key milestones on a path we believe can lead
banks to deeper customer relationships, expanded
market share, and stronger financial performance. The building blocks of an AI bank
Our goal in this compendium is to give banking
The opportunity for a new business model comes as leaders an end-to-end view of an AI bank’s full stack
banks face daunting challenges on multiple fronts. capabilities and examine how these capabilities
In capital markets, many banks trade at a 50 percent cut across four layers: engagement, AI-powered
discount to book, and approximately three-quarters decision making, core technology and data
of banks globally earn returns on equity that do not infrastructure, and a platform-based operating
cover their cost of equity.¹ Traditional banks also model.
face diverse competitive threats from neobanks and
nonbank challengers. Leading financial institutions In our first article, “AI-bank of the future: Can banks
are already leveraging AI for split-second loan meet the challenge?” we take a closer look at the
approvals, biometric authentication, and virtual trends and challenges leading banks to take an
assistants, to name just a few examples. Fintech AI-first approach as they define their core value
and other digital-commerce innovators are steadily proposition. We continue by considering a day in the
disintermediating banks from crucial aspects of life of a retail consumer and small-business owner
customer relationships, and large tech companies transacting with an AI bank. Then we summarize the
are incorporating payments and, in some cases, requirements for each layer of the AI-and-analytics
lending capabilities to attract more users with capability stack.
an ever-broader range of services. Further, as
customers conduct a growing share of their daily The second article, “Reimagining customer
transactions through digital channels, they are engagement for the AI bank of the future,” examines
becoming accustomed to the ease, speed, and the capabilities that enable a bank to provide
personalized service offered by digital natives, and customers with intelligent offers, personalized
their expectations of banks are rising. solutions, and smart servicing within omnichannel
journeys across bank-owned platforms and partner
To compete and thrive in this challenging ecosystems.
environment, traditional banks will need to build a
new value proposition founded upon leading-edge In our third article, “AI-powered decision making for
AI-and-analytics capabilities. They must become the bank of the future,” we examine how machine-
“AI first” in their strategy and operations. Many bank learning models can significantly enhance customer
1
“A test of resilience: Banking through the crisis, and beyond,” Global Banking Annual Review, December 2020, McKinsey.com.
2 Building the AI bank of the future
experiences and bank productivity, and we outline Once bank leaders have established their AI-first
the steps banks can follow to build the architecture vision, they will need to chart a road map detailing
required to generate real-time analytical insights and the discrete steps for modernizing enterprise
translate them into messages addressing precise technology and streamlining the end-to-end stack.
customer needs. Joint business-technology owners of customer-
facing solutions should assess the potential of
The fourth article, “Beyond digital transformations: emerging technologies to meet precise customer
Modernizing core technology for the AI bank of needs and prioritize technology initiatives with the
the future,” discusses the key elements required greatest potential impact on customer experience
for the backbone of the capability stack, including and value for the bank. We also recommend that
automated cloud provisioning and an API and banks consider leveraging partnerships for non-
streaming architecture to enable continuous, differentiating capabilities while devoting capital
secure data exchange between the centralized data resources to in-house development of capabilities
infrastructure and the decisioning and engagement that set the bank apart from the competition.
layers.
As we discuss in our final article, “Platform operating
model for the AI bank of the future,” deploying these Building the AI bank of the future will allow
AI-and-analytics capabilities efficiently at scale institutions to innovate faster, compete with digital
requires cross-functional business-technology natives in building deeper customer relationships
platforms comprising agile teams and new at scale, and achieve sustainable increases in
technology talent. 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.
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.
Renny Thomas
Senior Partner
McKinsey & Company
Building the AI bank of the future 3
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.
by Suparna Biswas, Brant Carson, Violet Chung, Shwaitang Singh, and Renny Thomas
© Getty Images
September 2020
4
In 2016, AlphaGo, a machine, defeated 18-time 3. What obstacles prevent banks from deploying
world champion Lee Sedol at the game of AI capabilities at scale?
Go, a complex board game requiring intuition,
imagination, and strategic thinking—abilities 4. How can banks transform to become AI first?
long considered distinctly human. Since then,
artificial intelligence (AI) technologies have
advanced even further,¹ and their transformative 1. Why must banks become AI first?
impact is increasingly evident across Over several decades, banks have continually
industries. AI-powered machines are tailoring adapted the latest technology innovations to
recommendations of digital content to individual redefine how customers interact with them. Banks
tastes and preferences, designing clothing introduced ATMs in the 1960s and electronic,
lines for fashion retailers, and even beginning to card-based payments in the ’70s. The 2000s saw
surpass experienced doctors in detecting signs of broad adoption of 24/7 online banking, followed
cancer. For global banking, McKinsey estimates by the spread of mobile-based “banking on the go”
that AI technologies could potentially deliver up to in the 2010s.
$1 trillion of additional value each year.²
Few would disagree that we’re now in the
Many banks, however, have struggled to move AI-powered digital age, facilitated by falling costs
from experimentation around select use cases to for data storage and processing, increasing
scaling AI technologies across the organization. access and connectivity for all, and rapid
Reasons include the lack of a clear strategy for AI, advances in AI technologies. These technologies
an inflexible and investment-starved technology can lead to higher automation and, when deployed
core, fragmented data assets, and outmoded after controlling for risks, can often improve upon
operating models that hamper collaboration human decision making in terms of both speed
between business and technology teams. What and accuracy. The potential for value creation
is more, several trends in digital engagement is one of the largest across industries, as AI can
have accelerated during the COVID-19 pandemic, potentially unlock $1 trillion of incremental value
and big-tech companies are looking to enter for banks, annually (Exhibit 1).
financial services as the next adjacency. To
compete successfully and thrive, incumbent Across more than 25 use cases,³ AI technologies
banks must become “AI-first” institutions, can help boost revenues through increased
adopting AI technologies as the foundation for personalization of services to customers (and
new value propositions and distinctive customer employees); lower costs through efficiencies
experiences. generated by higher automation, reduced errors
rates, and better resource utilization; and uncover
In this article, we propose answers to four new and previously unrealized opportunities
questions that can help leaders articulate a clear based on an improved ability to process and
vision and develop a road map for becoming an generate insights from vast troves of data.
AI-first bank:
More broadly, disruptive AI technologies can
1. Why must banks become AI first? dramatically improve banks’ ability to achieve
four key outcomes: higher profits, at-scale
2. What might the AI bank of the future look like? personalization, distinctive omnichannel
1
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.
2
“The executive’s AI playbook,” McKinsey.com.
3
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?
Exhibit 1
Potential
Potentialannual
annualvalue
valueofof
AIAI
and analytics
and forfor
analytics global banking
global could
banking reach
could as high
reach as as
$1 trillion.
high as $1 trillion.
Total potential annual value, $ billion
1,022.4 (15.4% of sales)
Traditional AI
Advanced AI
and analytics
660.9 361.5
% of value driven by advanced AI, by function
100
Finance and IT: 8.0 Other operations: $2.4 B
0.0 8.0 0.0 2.4
50
HR: 14.2
8.6 5.7
Marketing and sales: 624.8 Risk: 372.9
363.8 261.1 288.6 84.3
0
Source: "The executive's AI playbook," McKinsey.com. (See "Banking," under "Value & Assess.")
experiences, and rapid innovation cycles. Banks As consumers increase their use of digital
that fail to make AI central to their core strategy banking services, they grow to expect more,
and operations—what we refer to as becoming particularly when compared to the standards
“AI-first”—will risk being overtaken by competition they are accustomed to from leading consumer-
and deserted by their customers. This risk is internet companies. Meanwhile, these digital
further accentuated by four current trends: experience leaders continuously raise the bar
on personalization, to the point where they
— Rising customer expectations as adoption sometimes anticipate customer needs before
of digital banking increases. In the first few the customer is aware of them, and offer highly-
months of the COVID-19 pandemic, use of tailored services at the right time, through the
online and mobile banking channels across right channel.
countries has increased by an estimated 20
to 50 percent and is expected to continue at — Leading financial institutions’ use of advanced
this higher level once the pandemic subsides. AI technologies is steadily increasing. Nearly
Across diverse global markets, between 15 and 60 percent of financial-services sector
45 percent of consumers expect to cut back respondents in McKinsey’s Global AI Survey
on branch visits following the end of the crisis.⁴ report⁵ that their companies have embedded
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.
5
Arif Cam, Michael Chui, Bryce Hall, “Global AI Survey: AI proves its worth, but few scale impact,” November 2019, McKinsey.com.
AI bank of the future: Can banks meet the AI challenge? 6
at least one AI capability. The most commonly but also to book a cab, order food, schedule
used AI technologies are: robotic process a massage, play games, send money to a
automation (36 percent) for structured contact, and access a personal line of credit.
operational tasks; virtual assistants or Similarly, across countries, nonbanking
conversational interfaces (32 percent ) for businesses and “super apps” are embedding
customer service divisions; and machine financial services and products in their
learning techniques (25 percent) to detect journeys, delivering compelling experiences
fraud and support underwriting and risk for customers, and disrupting traditional
management. While for many financial services methods for discovering banking products and
firms, the use of AI is episodic and focused on services. As a result, banks will need to rethink
specific use cases, an increasing number of how they participate in digital ecosystems,
banking leaders are taking a comprehensive and use AI to harness the full power of data
approach to deploying advanced AI, and available from these new sources.
embedding it across the full lifecycle, from the
front- to the back-office (Exhibit 2). — Technology giants are entering financial
services as the next adjacency to their
— Digital ecosystems are disintermediating core business models. Globally, leading
traditional financial services. By enabling technology giants have built extraordinary
access to a diverse set of services through market advantages: a large and engaged
a common access point, digital ecosystems customer network; troves of data, enabling a
have transformed the way consumers discover, robust and increasingly precise understanding
evaluate, and purchase goods and services. of individual customers; natural strengths
For example, WeChat users in China can use in developing and scaling innovative
the same app not only to exchange messages, technologies (including AI); and access to
Web
Exhibit
Exhibit 2 of
Banks are
Banks areexpanding
expandingtheir
theiruse
useofof
AIAI
technologies to improve
technologies customer
to improve customer
experiences and
experiences andback-office
back-officeprocesses.
processes.
Front office Back office
Smile-to-pay facial scanning Micro-expression analysis Biometrics (voice, video, Machine learning to detect
to initiate transaction with virtual loan officers print) to authenticate and fraud patterns,
authorize cybersecurity attacks
Conversational bots for Humanoid robots in branches Machine vision and natural- Real-time transaction
basic servicing requests to serve customers language processing to scan analysis for risk monitoring
and process documents
7 AI bank of the future: Can banks meet the AI challenge?
low-cost capital. In the past, tech giants have digital era, the AI-first bank will offer propositions
aggressively entered into adjacent businesses and experiences that are intelligent (that
in search of new revenue streams and to is, recommending actions, anticipating and
keep customers engaged with a fresh stream automating key decisions or tasks), personalized
of offerings. Big-tech players have already (that is, relevant and timely, and based on a
gained a foothold in financial services in select detailed understanding of customers’ past
domains (especially in payments and, in some behavior and context), and truly omnichannel
cases, lending and insurance), and they may (seamlessly spanning the physical and online
soon look to press their advantages to deepen contexts across multiple devices, and delivering
their presence and build greater scale. a consistent experience) and that blend banking
capabilities with relevant products and services
beyond banking. Exhibit 3 illustrates how such a
2. What might the AI bank of the bank could engage a retail customer throughout
future look like? the day. Exhibit 4 shows an example of the banking
To meet customers’ rising expectations and experience of a small-business owner or the
beat competitive threats in the AI-powered treasurer of a medium-size enterprise.
Exhibit 3
How
How AI
AI transforms banking
transforms banking forfor a retail
a retail customer.
customer.
Name: Anya
Age: 28 years
Occupation: Working professional
Anya receives
App offers money- integrated portfolio
management and view and a set of
Anya uses smile- savings solutions, actions with the
Seamless to-pay to Analytics- prioritizes card potential to
Aggregated
integration with initiate payment backed payments overview of daily augment returns
nonbanking apps personalized offers activities
Bank app Facial recognition Anya gets 2% off Personalized Anya receives Savings and
recognizes Anya's for frictionless on health money-management investment recom-
end-of-day mendations
spending patterns payment insurance solutions overview of her
and suggests premiums based activities, with
coffee at nearby on her gym augmented reality,
cafes activity and and reminders to
sleep habits pay bills
Intelligent Personalized Omnichannel Banking and beyond banking
AI bank of the future: Can banks meet the AI challenge? 8Exhibit 4
How AI transforms banking for a small- or medium-size-enterprise customer.
How AI transforms banking for a small- or medium-size-enterprise customer.
Name: Dany
Age: 36 years
Occupation: Treasurer of a small manufacturing unit
Dany answers
short questionnaire;
app scans his facial An AI-powered
movements virtual adviser
Dany is assisted
Firm is credited in sourcing and resolves queries
with funds after selecting the Dany seeks
Customized application Seamless right vendors Beyond- professional advice
lending solutions approval inventory and receiv- and partners banking support on a lending offer
ables management services
Bank is integrated Micro-expression App suggests SME platform to Dany gets prefilled Serviced by an AI-
with client analysis to review loan items to reorder, source suppliers tax documents to powered virtual
business applications gives visual reports and buyers review and adviser
management on receivables approve; files with
systems management a single click
Dany gets loan Dany receives
offer based on customized
company projected solutions for
cash flows invoice discounting,
factoring, etc.
Intelligent Personalized Omnichannel Banking and beyond banking
Internally, the AI-first institution will be optimized The AI-first bank of the future will also enjoy
for operational efficiency through extreme the speed and agility that today characterize
automation of manual tasks (a “zero-ops” mindset) digital-native companies. It will innovate
and the replacement or augmentation of human rapidly, launching new features in days or
decisions by advanced diagnostic engines in weeks instead of months. It will collaborate
diverse areas of bank operations. These gains extensively with partners to deliver new
in operational performance will flow from broad value propositions integrated seamlessly
application of traditional and leading-edge AI across journeys, technology platforms, and
technologies, such as machine learning and data sets.
facial recognition, to analyze large and complex
reserves of customer data in (near) real time.
9 AI bank of the future: Can banks meet the AI challenge?cases. Without a centralized data backbone, it is
3. What obstacles prevent banks from
practically impossible to analyze the relevant data
deploying AI capabilities at scale?
and generate an intelligent recommendation or
Incumbent banks face two sets of objectives,
offer at the right moment. If data constitute the
which on first glance appear to be at odds. On
bank’s fundamental raw material, the data must be
the one hand, banks need to achieve the speed,
governed and made available securely in a manner
agility, and flexibility innate to a fintech. On the
that enables analysis of data from internal and
other, they must continue managing the scale,
external sources at scale for millions of customers,
security standards, and regulatory requirements
in (near) real time, at the “point of decision” across
of a traditional financial-services enterprise.
the organization. Lastly, for various analytics and
advanced-AI models to scale, organizations need
Despite billions of dollars spent on change-
a robust set of tools and standardized processes
the-bank technology initiatives each year, few
to build, test, deploy, and monitor models, in a
banks have succeeded in diffusing and scaling
repeatable and “industrial” way.
AI technologies throughout the organization.
Among the obstacles hampering banks’ efforts,
Banks’ traditional operating models further
the most common is the lack of a clear strategy
impede their efforts to meet the need for
for AI.⁶ Two additional challenges for many
continuous innovation. Most traditional banks
banks are, first, a weak core technology and data
are organized around distinct business lines,
backbone and, second, an outmoded operating
with centralized technology and analytics
model and talent strategy.
teams structured as cost centers. Business
owners define goals unilaterally, and alignment
Built for stability, banks’ core technology
with the enterprise’s technology and analytics
systems have performed well, particularly in
strategy (where it exists) is often weak or
supporting traditional payments and lending
inadequate. Siloed working teams and “waterfall”
operations. However, banks must resolve
implementation processes invariably lead
several weaknesses inherent to legacy systems
to delays, cost overruns, and suboptimal
before they can deploy AI technologies at scale
performance. Additionally, organizations lack
(Exhibit 5). First and foremost, these systems
a test-and-learn mindset and robust feedback
often lack the capacity and flexibility required
loops that promote rapid experimentation and
to support the variable computing requirements,
iterative improvement. Often unsatisfied with the
data-processing needs, and real-time analysis
performance of past projects and experiments,
that closed-loop AI applications require.⁷ Core
business executives tend to rely on third-party
systems are also difficult to change, and their
technology providers for critical functionalities,
maintenance requires significant resources.
starving capabilities and talent that should ideally
What is more, many banks’ data reserves are
be developed in-house to ensure competitive
fragmented across multiple silos (separate
differentiation.
business and technology teams), and analytics
efforts are focused narrowly on stand-alone use
6
Michael Chui, Sankalp Malhotra, “AI adoption advances, but foundational barriers remain,” November 2018, McKinsey.com.
7
“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.
AI bank of the future: Can banks meet the AI challenge? 10Exhibit 5
Investmentsinincore
Investments core tech
tech areare critical
critical to meet
to meet increasing
increasing demands
demands for for
scalability,flexibility,
scalability, flexibility,and
and speed.
speed.
Cloud
Data API1
Challenges How cloud computing can help
Core/legacy systems can’t scale sufficiently Enables higher scalability, resilience of services and
(eg, 150+ transactions/second) platforms through virtualization of infrastructure
Significant time, effort, and team sizes Reduces IT overhead, enables automation of several
required to maintain infrastructure infrastructure-management tasks, and allows development
Long time required to provision environments teams to “self-serve”
for development and testing (eg, 40+ days in Enables faster time to market; dramatically reduces time by
some cases) providing managed services (e., setting up new environments
in minutes vs days)
Challenges How best-in-class data management can help
High error rates; poor refresh rates; lack of Ensures high degree of accuracy and single source of truth
golden source of truth in a cost-effective manner
Hard to access in a timely fashion for various Enables timely and role-appropriate access for various use
use cases cases (eg, regulatory, business intelligence at scale, advanced
Data trapped in silos across multiple units and analytics and machine learning, exploratory)
hard to integrate with external sources Enables a 360-degree view across the organization to enable
generation of deeper insights by decision-making algorithms
and models
Challenges How APIs can help
Longer time to market, limited reusability of Promote reusability and accelerate development by enabling
code and software across internal teams access to granular services (internal and external)
Hard to partner or collaborate with external Reduce complexity and enable faster collaboration with
partners; long time to integrate external partners
Suboptimal user experience—hard to stitch Enhance customer experience by enabling timely access to
data and services across multiple functional data and services across different teams; faster time to market
siloes for an integrated proposition due to limited coordination, cross-team testing
1
Application programming interface.
11 AI bank of the future: Can banks meet the AI challenge?4. How can banks transform to First, banks will need to move beyond highly
become AI-first? standardized products to create integrated
To overcome the challenges that limit propositions that target “jobs to be done.”⁸ This
organization-wide deployment of AI requires embedding personalization decisions
technologies, banks must take a holistic (what to offer, when to offer, which channel
approach. To become AI-first, banks must invest to offer) in the core customer journeys and
in transforming capabilities across all four layers designing value propositions that go beyond the
of the integrated capability stack (Exhibit 6): the core banking product and include intelligence
engagement layer, the AI-powered decisioning that automates decisions and activities on
layer, the core technology and data layer, and the behalf of the customer. Further, banks should
operating model. strive to integrate relevant non-banking
products and services that, together with the
As we will explain, when these interdependent core banking product, comprehensively address
layers work in unison, they enable a bank to the customer end need. An illustration of the
provide customers with distinctive omnichannel “jobs-to-be-done” approach can be seen in the
experiences, support at-scale personalization, way fintech Tally helps customers grapple with
and drive the rapid innovation cycles critical the challenge of managing multiple credit cards.
to remaining competitive in today’s world. The fintech’s customers can solve several pain
Each layer has a unique role to play—under- points—including decisions about which card to
investment in a single layer creates a weak link pay first (tailored to the forecast of their monthly
that can cripple the entire enterprise. income and expenses), when to pay, and how
much to pay (minimum balance versus retiring
The following paragraphs explore some of the principal)—a complex set of tasks that are often
changes banks will need to undertake in each not done well by customers themselves.
layer of this capability stack.
The second necessary shift is to embed
Layer 1: Reimagining the customer customer journeys seamlessly in partner
engagement layer ecosystems and platforms, so that banks
Increasingly, customers expect their bank to be engage customers at the point of end use and
present in their end-use journeys, know their in the process take advantage of partners’
context and needs no matter where they interact data and channel platform to increase higher
with the bank, and to enable a frictionless engagement and usage. ICICI Bank in India
experience. Numerous banking activities embedded basic banking services on WhatsApp
(e.g., payments, certain types of lending) are (a popular messaging platform in India) and
becoming invisible, as journeys often begin and scaled up to one million users within three
end on interfaces beyond the bank’s proprietary months of launch.⁹ In a world where consumers
platforms. For the bank to be ubiquitous in and businesses rely increasingly on digital
customers’ lives, solving latent and emerging ecosystems, banks should decide on the
needs while delivering intuitive omnichannel posture they would like to adopt across multiple
experiences, banks will need to reimagine how ecosystems—that is, to build, orchestrate, or
they engage with customers and undertake partner—and adapt the capabilities of their
several key shifts. engagement layer accordingly.
8
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.
9
“ICICI Bank crosses 1 million users on WhatsApp platform,” Live Mint, July 7, 2020, livemint.com.
AI bank of the future: Can banks meet the AI challenge? 12Exhibit 6
To
Tobecome
becomeanan AI-first institution,
AI-first a bank
institution, must
a bank streamline
must its capability
streamline stack stack
its capability for
value creation.
for value creation.
AI bank of the future
Personalization Omnichannel Speed and
Profitability
at scale experience innovation
Intelligent products, Within-bank channels and Beyond-bank channels
Reimagined tools, experiences journeys (eg, web, apps, and journeys (eg, Smart service and
engagement for customers and mobile, smart devices, ecosystems, partners, operations
employees branches, Internet of Things) distributors)
1 2 3 4
5
Digital marketing
6
Retention Servicing
Advanced Customer Credit Monitoring
and cross- and
analytics acquisition decision and
selling, engagement
AI-powered making collections
upselling
decision
making
Natural- Voice-
7 Virtual Facial Behav-
language Block-
script agents, Computer recog- Robotics ioral
AI capabilities process- analysis vision chain
bots nition analytics
ing
A. Tech-forward strategy (in-house build of differential capabilities
vs buying offerings; in-house talent plan)
Core 8 B. Data C. Modern D. Intelligent E. Hollow- F. Cyber-
manage- API archi- infrastructure ing the security
technology Core technology
ment for tecture (AI operations core (core and
and data and data
AI world command, moderniza- control
hybrid cloud tion) tiers
setup, etc)
A. Autonomous business + tech teams
9
Operating Platform operating B. Agile way C. Remote D. Modern talent E. Culture and
model model of working collaboration strategy (hiring, capabilities
reskilling)
10 Value capture
13 AI bank of the future: Can banks meet the AI challenge?Third, banks will need to redesign overall and stronger risk management (e.g., earlier
customer experiences and specific journeys for detection of likelihood of default and
omnichannel interaction. This involves allowing fraudulent activities).
customers to move across multiple modes (e.g.,
web, mobile app, branch, call center, smart To establish a robust AI-powered decision
devices) seamlessly within a single journey layer, banks will need to shift from attempting
and retaining and continuously updating the to develop specific use cases and point
latest context of interaction. Leading consumer solutions to an enterprise-wide road map for
internet companies with offline-to-online deploying advanced-analytics (AA)/machine-
business models have reshaped customer learning (ML) models across entire business
expectations on this dimension. Some banks domains. As an illustration, in the domain of
are pushing ahead in the design of omnichannel unsecured consumer lending alone, more
journeys, but most will need to catch up. than 20 decisions across the life cycle can be
automated.11 To enable at-scale development
Reimagining the engagement layer of the of decision models, banks need to make the
AI bank will require a clear strategy on how development process repeatable and thus
to engage customers through channels capable of delivering solutions effectively and
owned by non-bank partners. Banks will on-time. In addition to strong collaboration
need to adopt a design-thinking lens as they between business teams and analytics
build experiences within and beyond the talent, this requires robust tools for model
bank’s platform, engineering engagement development, efficient processes (e.g., for
interfaces for flexibility to enable tailoring and re-using code across projects), and diffusion
personalization for customers, reengineering of knowledge (e.g., repositories) across teams.
back-end processes, and ensuring that data- Beyond the at-scale development of decision
capture funnels (e.g., clickstream) are granularly models across domains, the road map should
embedded in the bank’s engagement layer. All also include plans to embed AI in business-
of this aims to provide a granular understanding as-usual process. Often underestimated,
of journeys and enable continuous this effort requires rewiring the business
improvement.10 processes in which these AA/AI models will be
embedded; making AI decisioning “explainable”
Layer 2: Building the AI-powered decision- to end-users; and a change-management plan
making layer that addresses employee mindset shifts and
Delivering personalized messages and skills gaps. To foster continuous improvement
decisions to millions of users and thousands beyond the first deployment, banks also
of employees, in (near) real time across the full need to establish infrastructure (e.g., data
spectrum of engagement channels, will require measurement) and processes (e.g., periodic
the bank to develop an at-scale AI-powered reviews of performance, risk management of AI
decision-making layer. Across domains within models) for feedback loops to flourish.
the bank, AI techniques can either fully replace
or augment human judgment to produce Additionally, banks will need to augment
significantly better outcomes (e.g., higher homegrown AI models, with fast-evolving
accuracy and speed), enhanced experience capabilities (e.g., natural-language processing,
for customers (e.g., more personalized computer-vision techniques, AI agents
interaction and offerings), actionable insights and bots, augmented or virtual reality) in
for employees (e.g., which customer to contact their core business processes. Many of
first with next-best-action recommendations), these leading-edge capabilities have the
10
Jennifer Kilian, Hugo Sarrazin, and Hyo Yeon, “Building a design-driven culture,” September 2015, McKinsey.com.
11
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.
AI bank of the future: Can banks meet the AI challenge? 14potential to bring a paradigm shift in customer technology backbone, starved of the investments
experience and/or operational efficiency. While needed for modernization, can dramatically
many banks may lack both the talent and the reduce the effectiveness of the decision-making
requisite investment appetite to develop these and engagement layers.
technologies themselves, they need at minimum
to be able to procure and integrate these The core-technology-and-data layer has six key
emerging capabilities from specialist providers elements (Exhibit 7):
at rapid speed through an architecture enabled
by an application programming interface (API), — Tech-forward strategy. Banks should have
promote continuous experimentation with these a unified technology strategy that is tightly
technologies in sandbox environments to test and aligned to business strategy and outlines
refine applications and evaluate potential risks, strategic choices on which elements, skill
and subsequently decide which technologies to sets, and talent the bank will keep in-house
deploy at scale. and those it will source through partnerships
or vendor relationships. In addition, the
To deliver these decisions and capabilities and to tech strategy needs to articulate how each
engage customers across the full life cycle, from component of the target architecture will both
acquisition to upsell and cross-sell to retention support the bank’s vision to be an AI-first
and win-back, banks will need to establish institution and interact with each layer of the
enterprise-wide digital marketing machinery. This capability stack.
machinery is critical for translating decisions and
insights generated in the decision-making layer — Data management for the AI-enabled world.
into a set of coordinated interventions delivered The bank’s data management must ensure
through the bank’s engagement layer. This data liquidity—that is, the ability to access,
machinery has several critical elements, which ingest, and manipulate the data that serve as
include: the foundation for all insights and decisions
generated in the decision-making layer.
— Data-ingestion pipelines that capture a range Data liquidity increases with the removal of
of data from multiple sources both within the functional silos and allows multiple divisions
bank (e.g., clickstream data from apps) and to operate off the same data, with increased
beyond (e.g., third-party partnerships with coordination. The data value chain begins with
telco providers) seamless sourcing of data from all relevant
internal systems and external platforms. This
— Data platforms that aggregate, develop, and includes ingesting data into a lake, cleaning
maintain a 360-degree view of customers and and labeling the data required for diverse use
enable AA/ML models to run and execute in cases (e.g., regulatory reporting, business
near real time intelligence at scale, AA/ML diagnostics),
segregating incoming data (from both existing
— Campaign platforms that track past actions and prospective customers) to be made
and coordinate forward-looking interventions available for immediate analysis from data to
across the range of channels in the be cleaned and labeled for future analysis.
engagement layer Furthermore, as banks design and build their
centralized data-management infrastructure,
Layer 3: Strengthening the core technology and they should develop additional controls and
data infrastructure monitoring tools to ensure data security,
Deploying AI capabilities across the organization privacy, and regulatory compliance—for
requires a scalable, resilient, and adaptable set example, timely and role-appropriate access
of core-technology components. A weak core- across the organization for various use cases.
15 AI bank of the future: Can banks meet the AI challenge?Exhibit 7
The
The core-technology-and-data
core-technology-and-data layer layer accommodates
accommodates increasingincreasing use of the
use of the cloud
cloud and reduction
and reduction of legacyof legacy technology.
technology.
Capabilities Our perspective
Build differentiating capabilities in-house by augmenting the internal skill base;
Tech-forward strategy 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
Data management for AI world
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
Modern API1 architecture complex orchestrations while creating experience-enhancing integrations across
the ecosystem
Implement infrastructure as code across on-premises and cloud environments;
Intelligent infrastructure increase platform resiliency by adopting AIOps to support deep diagnostics, auto-
recoverability, and auto-scale
Distribute transaction processing across the enterprise stack; selectively identify
Hollowing the core components that can be externalized to drive broader reuse, standardization, and
efficiency
Implement robust cybersecurity in the hybrid infrastructure; secure data and
Cybersecurity and control tiers applications through zero-trust design principles and centralized command-and-
control centers
1
Application programming interface.
— Modern API architecture. APIs are the — Intelligent infrastructure. As companies
connective tissue enabling controlled access in diverse industries increase the share of
to services, products, and data, both within workload handled on public and private
the bank and beyond. Within the bank, APIs cloud infrastructure, there is ample evidence
reduce the need for silos, increase reusability that cloud-based platforms allow for the
of technology assets, and promote flexibility higher scalability and resilience crucial to an
in the technology architecture. Beyond the AI-first strategy.13 Additionally, cloud-based
bank, APIs accelerate the ability to partner infrastructure reduces costs for IT maintenance
externally, unlock new business opportunities, and enables self-serve models for development
and enhance customer experiences. While teams, which enable rapid innovation cycles by
APIs can unlock significant value, it is critical to providing managed services (e.g., setting up new
start by defining where they are to be used and environments in minutes instead of days).
establish centralized governance to support
their development and curation.¹2
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.
AI bank of the future: Can banks meet the AI challenge? 16Layer 4: Transitioning to the platform operating
model
The AI-first bank of the future will need a new The journey to becoming an AI-first bank entails
operating model for the organization, so it can transforming capabilities across all four layers
achieve the requisite agility and speed and of the capability stack. Ignoring challenges or
unleash value across the other layers. While underinvesting in any layer will ripple through all,
most banks are transitioning their technology resulting in a sub-optimal stack that is incapable
platforms and assets to become more modular of delivering enterprise goals.
and flexible, working teams within the bank
continue to operate in functional silos under A practical way to get started is to evaluate
suboptimal collaboration models and often lack how the bank’s strategic goals (e.g., growth,
alignment of goals and priorities. profitability, customer engagement, innovation)
can be materially enabled by the range of AI
The platform operating model envisions cross- technologies—and dovetailing AI goals with the
functional business-and-technology teams strategic goals of the bank. Once this alignment
organized as a series of platforms within the bank. is in place, bank leaders should conduct a
Each platform team controls their own assets comprehensive diagnostic of the bank’s starting
(e.g., technology solutions, data, infrastructure), position across the four layers, to identify areas
budgets, key performance indicators, and that need key shifts, additional investments
talent. In return, the team delivers a family of and new talent. They can then translate these
products or services either to end customers of insights into a transformation roadmap that spans
the bank or to other platforms within the bank. business, technology, and analytics teams.
In the target state, the bank could end up with
three archetypes of platform teams. Business Equally important is the design of an execution
platforms are customer- or partner-facing teams approach that is tailored to the organization. To
dedicated to achieving business outcomes in ensure sustainability of change, we recommend
areas such as consumer lending, corporate a two-track approach that balances short-term
lending, and transaction banking. Enterprise projects that deliver business value every quarter
platforms deliver specialized capabilities and/ with an iterative build of long-term institutional
or shared services to establish standardization capabilities. Furthermore, depending on their
throughout the organization in areas such as market position, size, and aspirations, banks need
collections, payment utilities, human resources, not build all capabilities themselves. They might
and finance. And enabling platforms enable the elect to keep differentiating core capabilities
enterprise and business platforms to deliver in-house and acquire non-differentiating
cross-cutting technical functionalities such as capabilities from technology vendors and
cybersecurity and cloud architecture. partners, including AI specialists.
By integrating business and technology in For many banks, ensuring adoption of AI
jointly owned platforms run by cross-functional technologies across the enterprise is no longer
teams, banks can break up organizational silos, a choice, but a strategic imperative. Envisioning
increasing agility and speed and improving the and building the bank’s capabilities holistically
alignment of goals and priorities across the across the four layers will be critical to success.
enterprise.
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.
by Violet Chung, Malcolm Gomes, Sailee Rane, Shwaitang Singh, and Renny Thomas
© Getty Images
October 2020
18From instantaneous translation to The value of reimagined customer
conversational interfaces, artificial-intelligence engagement
(AI) technologies are making ever more evident In recent years, many financial institutions
impacts on our lives. This is particularly true in have devoted significant capital to digital-and-
the financial-services sector, where challengers analytics transformations, aiming to improve
are already launching disruptive AI-powered customer journeys across mobile and web
innovations. To remain competitive, incumbent channels. Despite these big investments, most
banks must become “AI first” in vision and banks still lag well behind consumer-tech
execution, and as discussed in the previous companies in their efforts to engage customers
article, this means transforming the full with superior service and experiences.
capability stack, including the engagement layer, The prevailing models for bank customer
AI-powered decision making, core technology acquisition and service delivery are beset by
and data infrastructure, and operating model. missed cues: incumbents often fail to recognize
If fully integrated, these capabilities can and decipher the signals customers leave
strengthen engagement significantly, supporting behind in their digital journeys.
customers’ financial activities across diverse
online and physical contexts with intelligent, Across sectors, however, leaders in delivering
highly personalized solutions delivered through positive experiences are not just making
an interface that is intuitive, seamless, and fast. their journeys easy to access and use but
These are the baseline expectations for an also personalizing core journeys to match
AI bank. an individual’s present context, direction of
movement, and aspiration.
In this article, we examine how banks can take
an AI-first approach to reimagining customer Creating a superior experience can generate
engagement. We focus on three elements with significant value. A McKinsey survey of US
potential to give the bank a decisive competitive retail banking customers found that at the
edge: banks with the highest degree of reported
customer satisfaction, deposits grew 84
1. The value of re-imagined customer percent faster than at the banks with the lowest
engagement: By reimagining customer satisfaction ratings (Exhibit 1).
engagement, banks can unlock new value
through better efficiency, expanded market Superior experiences are not only a proven
access, and greater customer lifetime value. foundation for growth but also a crucial means
of countering threats from new attackers. In
2. Key elements of the re-imagined engagement particular, three trends make it imperative for
layer: The combination of intelligent propositions, banks to improve customer engagement:
seamless embedding within partner ecosystems,
and smart servicing and experiences underpins 1. Rising customer expectations. Accustomed
an overall experience that sets the AI bank apart to the service standards set by consumer
from traditional incumbents. internet companies, today’s customers
have come to expect the same degree of
3. Integrated supporting capabilities: As banks consistency, convenience, and personalization
rethink and rebuild their engagement capabilities, from their financial-services institutions. For
they need to leverage critical enablers, each example, Netflix has been able to raise the
of which cuts across all four layers of the bar in customer experience by doing well
capability stack. on three crucial attributes: consistency of
19 Reimagining customer engagement for the AI bank of the futureExhibit 1
US retail banks with high customer satisfaction typically grow deposits faster.
US retail banks with high customer satisfaction typically grow deposits faster.
Real differences in customer satisfaction1 Leaders in customer satisfaction grow faster
CSAT2 (Percent of customers rating 9 or 10) Deposit CAGR (2014-17)
Top
65
quartile
+84%
3rd
55
quartile
5.9
2nd
49
quartile
3.2
Bottom
39
quartile
Top Bottom
-26 pp quartile quartile
CSAT CSAT
1
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 satisfied or dissatisfied are you with [product] with [Bank]?” Banks were ranked based on average satisfaction scores
and then divided into quartiles.
2Customer satisfaction score.
Source: McKinsey 2018 Retail Banking Customer Experience Benchmark Survey
experience across channels (mobile app, laptop, providing access to financial products within their
TV), convenient access to a vast reserve of nonbanking ecosystems. Messaging app WeChat
content with a single click, and recommendations allows users in China to make a payment within
finely tailored to each profile within a single the chat window. Google has partnered with eight
account. Improving websites and online portals US banks to offer cobranded accounts that will be
for a seamless experience is one of the top three mobile first and focus on creating an intuitive user
areas where customers desire support from experience and new ways to manage money with
banks.¹ Innovation leaders are already executing financial insights and budgeting tools.²
transactions and loan approvals and resolving
service inquiries in near real time. Beyond access, nonbank innovators are also
disintermediating parts of the value chain that
2. Disintermediation. Nonbank providers are were once considered core capabilities of financial
disintermediating banks from the most valuable institutions, including underwriting. Indian agtech
services, leaving less profitable links in the value company Cropin uses advanced analytics and
chain to traditional banks. Big-tech companies are machine learning to analyze historical data on
1
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.
2
“Google to offer co-branded cards with 8 US banks” August 3, 2020, Finextra.com.
Reimagining customer engagement for the AI bank of the future 20crop performance, weather patterns, land usage, If reimagined customer engagement is properly
and more to develop underwriting models that aligned with the other layers of the AI-and-
predict a customer’s creditworthiness much more analytics capability stack, it can strengthen
accurately than traditional risk models. a bank’s competitive position and financial
performance by increasing efficiency, access
3. Increasingly human-like formats. and scale, and customer lifetime value (Exhibit 2).
Conversational interfaces are becoming the
new standard for customer engagement. With
approximately one third of adult Americans Key elements of the AI-first
owning a smart speaker,³ voice commands are engagement layer
gaining traction, and adoption of both voice and For banks, successfully integrating core
video interfaces will likely expand as in-person personalization elements across the range
interactions continue to decline. Several banks of touchpoints with customers will be critical
have already launched voice-activated assistants, to deliver a superior experience and better
including Bank of America with Erica and ICICI outcomes. The reimagined engagement layer
bank in India with iPal. should provide the AI bank with a deeper and
3
Bret Kinsella, “Nearly 90 million U.S. adults have smart speakers, adoption now exceeds one-third of consumers,” April 28, 2020, voicebot.ai.
Exhibit 2
Withan
With an AI-first
AI-first approach
approach totocustomer
customerengagement, banks
engagement, have
banks the the
have
opportunity to reap gains in crucial areas.
opportunity to reap gains in crucial areas.
Access to newer, previously untapped
customer segments
Higher speed to reach critical scale
Increased access
and scale
Reduced cost of acquisition
Key Stronger activation and usage of
(more cross-sell, partner metrics existing products
platform-led growth) impacted Higher Higher engagement (eg, monthly
Lower cost to serve (less or Higher customer usage), satisfaction (eg, NPS,1
“zero” operations) efficiency lifetime value lower TAT2) and reduced churn
Lower risk (better data, early Higher cross-sell of new products
warnings, proactive nudging)
1
Net promoter score.
2Turn around time.
Source: McKinsey analysis
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