Banks can leverage machine learning models to increase value through stronger customer acquisition, higher customer lifetime value, and lower operating costs. AI-powered decision making allows for personalized experiences, continuous customer engagement, automated document processing, and early risk detection. Advanced analytical models can be organized around significant elements like the customer lifecycle to benefit banks.

1. Artificial Intelligence (AI)
powered decision making for
the bank of the future
Pankaj Baid
1

2. 2
Banks that leverage machine-learning models have potential to increase value by
SOURCE: Multiple Sources from Internet
Stronger customer
acquisition
Higher customer
lifetime value
Lower operating
costs
▪ 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.
▪ Banks can increase the lifetime value of customers by engaging with them continuously
and intelligently to strengthen each relationship across diverse products and services.
▪ 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

3. 3
Banks can benefit from organizing their automation efforts around these significant elements
Leveraging advanced analytical / machine learning models for automated, personalized decisions
across the customer life cycle
Building and deploying advanced analytics and machine learning models at scale
Augmenting advanced analytical models with capabilities1 to reduce costs, streamline customer
journeys, and enhance the overall experience
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
1 Next generation technologies like Natural Language Processing (NLP), facial recognition, block chain, Robotic process automation and behavioural analytics
2 AA – Advanced Analytics, ML – Machine Learning

4. 4
4
4
4
4
4
4
4
4
4
• This is consolidated survey
responses from more than 700
senior decision makers across the
accounting, banking, financial
services, investment and insurance
industries in the United States,
United Kingdom, continental Europe
and Asia.
• About a quarter of all respondents
indicated that they use Predictive
Analytics or Data Mining / Analytics,
with respondents from the US and
UK leading the way.
• Meanwhile, about one in seven
(15%) of respondents signaled that
they are using AI for Robo Advisory,
a new class of tools that manage
assets with minimal human
intervention.
Primary Applications of AI
(% responses)
SOURCE: Goodwin’s Fintech 2020, A Global Survey on the State of Financial Technology
Technologies which are important today for the large banks (2020 Fintech survey results)
6%
7%
8%
15%
17%
23%
24%
Biometrics
Quant Trading
Other
Robo Advisory
Lending/Credit
Data Mining
Predictive
Analytics
Regional Highlights
(% responses)
14%
14%
28%
28%
Asia
Continental
Europe
UK
US

5. 5
5
5
5
5
5
5
5
5
5
SOURCE: Mckinsey
Analytical techniques for various problems in the banks (Survey results)
• This illustrates the relative total
value of these problem types across
Mckinsey database of use cases,
along with some of the sample
analytics techniques that can be
used to solve each problem type.
• The most prevalent problem types
are classification, continuous
estimation, and clustering,
suggesting that developing the
capabilities in associated
techniques could have the widest
benefit
• Some of the problem types that rank
lower can be viewed as
subcategories of other problem
types—for example, anomaly
detection is a special case of
classification, while
recommendations can be
considered a type of optimization
problem—and thus their associated
capabilities could be even more
relevant
0
14
9
19
17
16
37
44
7
1
8
6
21
39
29
29
Essential Relevant
Problem Types Sample Techniques
Classification Logistic Regression
Continuous Estimation Linear Regression
Clustering K-Means
Optimization Genetic Algorithms
Anomaly Detection K nearest neighbors
Ranking Ranking SVM
Recommender systems Collaborative Filtering
Data generation Markov Models
Total AI value potential that could be unlocked by problem
types as essential versus relevant to use cases (%)

6. 6
Banks should prioritize using advanced analytics (AA) and machine learning (ML) in decisions across the
customer life cycle
1 VAR is value at risk, 2 Non Performing Asset
3 AUM is assets under management
Customer
acquisition
• Personalized offers
• Customer retargeting
• Propensity to buy
scoring
• Channel mapping
Credit Decisioning
• Credit qualification
• Limit assessment
• Pricing Optimization
• Fraud prevention
Monitoring and
collections
• Early-warning signals
• Probability of Default
• VAR- based customer
segmentation1
• Agent-customer
mapping
Deepening
Relationships
• Intelligent offers
• Churn reduction
• Channel propensity
• Fatigue rule engine
Smart servicing
• Servicing Personas
• Dynamic customer
routing
• Real time
recommendation
engine
• AI- enabled agent
review and training
Monthly customer
acquisition run rate
Credit approval
turnaround time, % of
applications approved
Average days past due,
NPA2
Deposit/AUM3 attrition
rate
Net promoter score, cost
of servicing
Key Metrics

7. 7
7
7
7
7
7
7
7
7
7
SOURCE: https://www.is650agoodcreditscore.com/fico-credit-score-chart/
AI is becoming critical as banking frauds are on the rise in the Indian Banks
• The credit score chart below is
based on FICO’s data and shows
what percentage of the population
fall into certain FICO score ranges
• Delinquency rates are higher
around 61% with consumers having
FICO scores 599 and lower and
28% delinquency rates for
consumers having FICO scores
599-699 and 8% with scores 700-
749 and 3% in the rest
• Advanced Analytics is gaining
popularity in domains like fraud
detection, KYC analytics, credit
monitoring and collections in banks
4.9%
7.6% 9.4%
10.3%
13%
16.6%
18.2%
19.9%
Scores ranging From Very Bad to Excellent
61% delinquency rate
28% delinquency rate
FICO Credit Score Range: 300 - 850
8% delinquency rate

8. 8
8
8
8
8
8
8
8
8
8
SOURCE: https://www.mckinsey.com/business-functions/risk/our-insights/the-investigator-centered-approach-to-financial-crime-doing-what-matters
Case Study – Low performance risk rating models without advanced analytics should not be allowed into production
• This represents a typical multifactor
customer risk-rating model for the
retail business of a large North
American universal bank
• A manually conducted expert review
of the results revealed that for every
100 customers rated high risk, 72
were actually medium to low risk;
furthermore, 57 of every 100
customers rated medium to low risk
by the model proved on review to
have a high-risk profile
• To put this into perspective, a credit-
risk model with this kind of
performance would never be
allowed into production
100
72
57
85
High risk
customers
according to
customer risk
rating model
"Low risk" cases
removed (false
positives)
"High risk" cases
added (false
negatives)
High risk
customers after
expert review
High risk
customers sent
to enhanced due-
diligence units
(disguised real
data example),
indexed to 100

9. 9
9
9
9
9
9
9
9
9
9
1 Suspicious Activity Report
SOURCE: https://www.mckinsey.com/business-functions/risk/our-insights/the-neglected-art-of-risk-detection
Case Study – Bank used enhanced data and analytics to dramatically reduce the money laundering activities
• At one large US bank, the false-positive rate in
anti–money laundering (AML) alerts was very
high. The remedial process involved a two-stage
investigation. One team would determine
whether an alert was truly triggered by suspicious
activity. It would eliminate clearly false positives
and pass on the remainder to experts for further
investigation. Very few suspicious-activity-report
filings resulted.
• The bank rightly felt that this elaborate procedure
and meager result was overtaxing resources. To
improve the specificity of its tests so that AML
expertise could be better utilized, the bank
looked at the underlying data and algorithms. It
discovered that the databases incompletely
identified customers and transactions. By adding
more data elements and linking systems through
machine-learning techniques, the bank achieved
a more complete understanding of the
transactions being monitored.
• It turned out that more than half of the cases
alerted for investigation were perfectly innocuous
intracompany transactions. With their more
sensitive database, the bank was able to keep
the process from issuing alerts for these
transactions, which substantially freed resources
for allocation to more complex cases
Before
enhanced data
and analytics,%
After enhanced
data and
analytics,%
0 90
10 8
2
100
50
45
5 3 2
100
Total
alerts
Known
intra-
company
transfers
Reviewed
by
primary
team
and
closed
Reviewed
by
secondary
team
Closed
by
secondary
team
Filed
as
SAR
1

10. 10
Advanced Analytics in Credit Decisioning
Machine learning 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 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.
Analytics models can use 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.
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.
Advanced analytical models can predict fraud related instances.
Limit
Assessment
Pricing
Fraud
Management
SOURCE: EY Global - https://www.ey.com/en_gl/consulting/how-data-analytics-is-leading-the-fight-against-financial-crime

11. 11
The combination of AI and analytics enhances the onboarding journey for each new customer
•Joy’s landing page
shows her
personalized offers:
personal loan for
travel, 5% off on
travel insurance
Analytics-backed
hyper personalized
offers based on
customer
microsegment
•Joy receives a
WhatsApp reminder
for personal loan for
travel at zero
processing charges
Propensity-to-buy
model to identify
whom to retarget.
Channel propensity
to identify right
outreach channel
•Joy receives a call to
assist in journey;
caller also informs
Joy about custom
travel services
Analytics-enabled
customer–caller
mapping with
specific cues
provided to caller
•Joy completes a
streamlined 3-click
journey to see the
offer terms and
conditions
AA-enabled real time
credit underwriting,
limit assessment,
and pricing
•Joy completes online
KYC form, provides
details for
employment
verification, and sets
up an online
payment mandate
AI capabilities to
conduct relevant
fraud checks (eg,
facial recognition
with KYC docs)
•Loan disbursed to
Joy; curated
catalogue of offers
ahead of Joy’s travel
sent by email
Hyper-personalized
cross-sell and upsell
offers
Name: Joy
Age:32 years
Occupation: Working professional
Family: Married, no children
Profile attribute: Avid traveler

12. 12
Artificial Intelligence in Monitoring & Collections comes to rescue to reduce NPA in banks
Treatment Strategy
▪ 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.
▪ 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. (detailed in the next slide)
Contact Strategy
AI helps build a 360-degree view of a customer’s financial position, helping banks to recognize early-warning signals that
a borrower’s risk profile may have changed and that the risk of default should be reassessed.

13. 13
AI and ML 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
True low-risk
• Use least
experienced
agents
provided
with set
scripts
Absent
minded
• Use
interactive
voice
message
(segment
will probably
self-cure)
Dialer-based
• Match
agents to
customers;
send live
prompts to
agents to
modify
scripts
True high-
touch
• Focus on
customers
able to pay
and at high
risk of not
paying
Unable to
Cure
• Offer debt
restructuring
settlements
early for
those who
truly
underwater
Onscreen
prompts guide
agent–client
conversation
based on
probability of
breaking
promises
10% of time
saved,
allowing for
reassignment
of agents to
more difficult
customers
and specific
campaigns
Matching and
prompts can
increase
sense of
connection
and
likelihood of
paying
Added focus
addresses
higher
probability of
default rates in
this segment
Customer
Type
Description
Impact
Significant
increase in
restructuring &
settlements
increases
chance of
collecting at
least part of
debt

14. 14
Artificial Intelligence in nurturing customer relationships to maximize customer value
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.
Deepening
Relationships
▪ 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.
▪ 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.
▪ 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.
▪ 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.
▪ 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 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.
Servicing and
engagement

15. 15
Augmented AA/ML models with cutting edge capabilities
NLP – Natural Language Processing
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.
• 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 info 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 (details
on next slide)
Description of an analytical use case in a customer-service journey
AI enabled Use Case
Cutting 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

16. 16
Cutting edge capabilities enhance customer-service journeys
1 Interactive Voice response
2 Natural language processing enabled
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
profiling, customer–agent
matching
• Customer connected to the
appropriate agent (chat or call)
based on type of query and
customer profiling
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 specific offers
preapproved for them
Feedback loop via
engagement channels
• Post-call feedback and
automated follow-up occur
via digital channels

17. Appendix