We were the National Finalist in the case study competition organized by ISB partnered with Barclays. Our solution to Barclay's bank to increase its foray into the consumer lending bank using customer segmentation using clustering techniques - Multi factor cluster analysis on 1.5 Million credit profile datasets. We identified profitable pools from our ML model which were then coupled with upcoming banking trends such as open banking to increase market share.

1. Team Stormtroopers
Disclaimer : Primary & Secondary research information has been used to validate the solutions in the case. All the recommendations related to the business
problems, as mentioned in the case, has been given by Team Stormtroopers Student research
CONSUMER DIGITAL LOANS IN
THE US MARKET
Nimble Powered by Barclays – ISB Case Study
Hermès bank is an International Bank which wishes to foray into the Consumer Digital Loans in the US
market which is estimated to be about $1 Trillion in size. The solution portrays a integrated digital strategy
on how Hermes bank can use analytics and existing consumer data to launch into the market amidst
established players

2. 02
U S C o n s u m e r D i g i t a l L e n d i n g – B r i e f O v e r v i e w
Digital Borrower Expectations
The expectations of the digital borrower have increased over the past several years, mostly based on marketplace
offerings and digital experiences in other industries. While the interest rate and closing costs on loans are still
primary considerations, the speed, simplicity, transparency and customer service of the entire process is important.
0.00%
0.50%
1.00%
1.50%
2.00%
2.50%
3.00%
3.50%
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
FY13 FY14 FY15 FY16 FY17 FY18
Growth of Personal Credit Requirements
in US ( In Trillions $)
0
2
4
6
Threat of
Substitutes
Bargaining
Power of
Buyers
Threat of New
Entrants
Bargaining
Power of
Suppliers
Competitive
Rivalry
Porter 5 Forces Analysis of the Digital
Lending Space
Most of the Consumer digital lending is
driven by P2P offerings strong incentives
to leverage existing cash deposits
Customer information like demographics, lifestyle, behavior, interests, major life
events (marriage, house, baby, retirement, and education), social, and internal
transactional data fuse to create a 360-degree customer view for financial institutions.
H
i
g
h
E
n
t
r
y
Estimated TAM Size Forecast for Personal Credit
Estimated Market Size form Morgan StanleyUS Base Case : Marketplace consumer lending grows rapidly,
mostly at expense of bank volumes
RiseinUnsecuredConsumerCredit
Source : Morgan Stanley Blue Paper: “Global Marketplace Lending: Disruptive Innovation in Financials”, May 19, 2015., Autonomous
Research, Digital Lending: The 100 billion dollar question, January 2016, Team Stormtroopers Student Research

3. 03
I D E N T I F Y I N G T H E TA R G E T C U S T O M E R G R O U P S E G M E N T B Y M U T L I - F A C T O R C L U S T E R A N A LY S I S
Creating
Target
Customer
Profiles
using
Market
Clusters
Demographic criteria Psychographic Criteria
Age Values
Location Attitudes
Gender Behaviors
Income Level Ethnic background
Education Level Occupation
RFM ( Recency, Frequency, Monetary) analysis is a proven marketing model for behavior based customer segmentation.
“It is based on the marketing axiom that 80% of your business comes from
20% of your customers. RFM helps to identify customers who are more
likely to respond to promotions by segmenting them into various categories.”
The heat map shows the average monetary value
for different categories of recency and frequency
scores. Higher scores of frequency and recency are
characterized by higher average monetary value as
indicated by the darker areas in the heatmap.
Millennials followed by Gen X showed strong MV
min
25
percent
median mean
75
percent
max std
ID -1.67 -0.83 0 0 0.83 1.67 1
V1 -1.52 -0.99 0.08 0 0.75 1.68 1
V2 -1.51 -0.79 -0.07 0 0.65 2.08 1
V3 -1.49 -0.98 0.03 0 1.03 1.54 1
V4 -1.4 -0.73 -0.07 0 0.77 1.93 1
V5 -1.41 -0.83 0.03 0 0.46 2.04 1
V6 -1.59 -0.91 -0.24 0 0.61 1.79 1
Income -1.22 -0.93 -0.2 0 0.81 1.97 1
SUMMARY STATISTICS OF THE SCALED DATASET CLUSTER ANALYZED OUTPUT VALUE OF THE US DATASET
Standardized Profiling Variables Identified Millennial Segment
Our 2 different
approaches
Kmeans and
Hierarchical
Clustering
have identified
“Millennials as
having highest
Monetary
value
https://catalog.data.gov/datasetDataset for Identifying Targeted Consumer Segment
Source : Bain Brief, Retail Banks Wake Up to Digital Lending, December 2017, Team StormTroopers Student Research

4. 04
U S C o n s u m e r D i g i t a l L o a n s M a r k e t S y n o p s i s & P r o p o s e d I n t e g r a t e d D i g i t a l E n t r y S t r a t e g y
1. Recommendation for Pursuing younger borrowers Younger borrowers of
today will become the
majority segment of
the market tomorrow.
With these customer
showing higher
preference of digital,
they become our key
target sector
LoansBranch
Location
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
3.2
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4
Home Improvement Loans
Unsecured personal loans
Student Loans
Auto Finance
2. Loan Class data used for selecting the target launch segment
Competencies
The student lending analysis
provided a fascinating glimpse into
the future borrower market since the
majority of those surveyed are within
younger demographics. Also,
perhaps not coincidentally, student
lenders received much lower
satisfaction ratings overall than the
other asset classes.
Also, in addition the rising huge sector
is the Peer-to-Peer Unsecured
borrowing based on credit scores.
0
5
10
15
20
25
2015 2016 2017 2018 2019 2020
Estimated growth of student
loan & Unsecured loan sector
3. Synergies between Credit card and Consumer Loan lending
Evaluate
Brands enter and exit
consideration set at any point
uptopurchase.
Ultimately the consumer
selects a brand at the
momentofpurchase.
DecideConsider
Consumerconsidersaninitial
set of brands.
Experience
After purchase, a formative
experience informs the next
decisionjourney.
Momentof
purchase
Initial
consideration
set
c
Trigger
Use credit-related offers to
highlight brands that might
otherwise have fallen out of the
consideration set (e.g.,
higher-quality, higher-cost
items that create more value for
consumers and merchants, but
havehigher upfront costs).
Make credit available at the
point of sale to customers who
can afford products outright,
but would prefer to preserve
cash flow.
Enhance consideration of the
brands that are available with
specific credit offers.
Usebetter data to ensure that
customers only take on credit
theycan afford.
Gap
Identification
BuildingOrganizational
Competency
Source: The Nilson Report, a twice-monthly newsletter based in Carpinteria, CA, 2015 Federal Reserve Payments Study ,
Team StormTroopers Student Research
Credit Analytics

5. 05
I N F O R M AT I O N A L S Y N E R G I E S B E T W E E N C R E D I T C A R D A N D C O N S U M E R L O A N S
We examined a dozen major data mining techniques to evaluate their performance and gain insight on which
credit card accounts were likely to default compared to the client’s world-class baseline model. The resulting
model ensemble significantly improved early identification of bad credit risks based on R-Code Simulations
We evaluate the credit risk prediction accuracy based on
different binary classifications (SVM & Decision Models)
Dataset
Credit Card data
set from the UC
Irvine repository.
 The first column lists the machine learning
algorithm used to generate a model.
 The second column indicates the type of
training set.
 The third column contains the cost-
sensitive accuracy value.
This data set contains 1000 samples with 20
features and 1 label. Each sample represents a
person. The 20 features include both numerical and
categorical specimens. The last column is the label,
which denotes the credit risk and has only two
possible values: high credit risk = 2, and low credit
risk = 1
Hotspot Profiling of Risky Credit Segments
Credit risk profiling (finance risk profiling) is very important. The Pareto principle suggests that
80%~90% of the credit defaults may come from 10%~20% of the lending segments. Profiling
risky segments can reveal useful information for credit risk management.
Credit providers often collect a vast amount of information on credit users. Information on
credit users (or borrowers) often consists of dozens or even hundreds of variables, involving
both categorical and numerical data with noisy information. Hotspot profiling is to identify
factors or variables that best summarize risky segments.
Credit Risk Scoring by Machine Learning - Credit Risk Predictive Models
Credit risk score is a risk rating of credit loans. It measures the level of risk of being
defaulted/delinquent. The level of default/delinquency risk can be best predicted with
predictive modeling using machine learning tools. Credit risk scores can be measured
in terms of default/delinquency probability and/or relative numerical ratings.
https://www.roselladb.com/credit-risk-analysis.htm, Accenture (2015): North America Consumer Digital Banking
Survey, Team StormTroopers Student Analysis
In the above chart, positive
value weight-links are colored
in red. Negative value weight-
links are colored in blue.
Colors are scaled according to
absolute value ratios against
the largest absolute value.
Absolute value zero is colored
in black.

6. Current time spent in US in banking operations total %
Technical feasibility: % of
activities that can be automated
by adopting the demonstrated
technology
A n t F i n a n c i a l E c o s y s t e m : B e c o m i n g i n t e g r a l p a r t o f u s e r ’ s l i f e
06Source: The BCG Report, a twice-monthly newsletter based in Carpinteria, CA, 2015 Federal Reserve Payments Study , Team
StormTroopers Student Research
Wealth Management
AI
Big
Data
Bio
Metric
Online
loans for
businesses
3-1-0
model for
loan
approval
Internet Banking
Payments
Shopping
Entertainment
-3 Minutes to
Decide
- 1 Minute to
transfer money
- 0 human touch
Open Sesame
Herme’s Bank,
provides financial
services to individuals
as well as businesses
A vast network of
partnerships(online and offline) to
provide consumers with an
engaging ecosystem
Generates and analyses
consumption data from
transactions throughout the
ecosystem
Leverage Big Data and AI to
understand behavior and
offer personalized service
Consumer Lending End to End Digital Lending Blueprint
Consumer requests
for Financing
Basic info and
eKYC Credit Bureau Check
Online fee collection Automated Sanction Ecosystem for Data
Digital Disbursement
Details
Digital Signature
and Stamping
Instant
Disbursement
Presence Less Layer
Cash Less Layer
Paper Less Layer
Consent Layer
Stack
Herme’s
Intelligence
Wealth Management
30%
31%
12%
27%
Current ability to onboard new
consumer loan customers
digitally in US
Yes, Currently we have
No, plan to have in 1 year
No, plan to have in next 3 years
No plans at this time

7. 10
9
8
7 F1
6 O1
5 F3
4 O1
3 L1
2 F2
1 O2
1 2 3 4 5 6 7 8 9
Probability
Impact
Hermes
Bank
N e w D i g i t a l C a p a b i l i t i e s w i t h t a n g i b l e f e a t u r e s f o r r e f i n i n g E n d t o E n d c u s t o m e r j o u r n e y
Customer
Journey
Capture Basic
Data
Customer provides all data
digitally Data captured
eKYC, financials, property
details etc.
Processing Fee
Processing Fee is
collected through
payment gateway
eSign Sanction Letter
Customer notified of
offer; eSigns sanction
letter
Disbursement docs and
eSign
Customer uploads NOC and
eSigns collects original docs
from customer
eKYC
Social Security Number
(SSN) used to capture
KYC data (eKYC)
Bureau Check
Customer details used to
trigger automatic bureau
check and duplication
Income Assessment
Customer obligations &
income automatically
assessed using 3rd party
providers Bank A/c
statements
Business Rules
Automatic Credit using
bureau and other input data
Instant decisioning
Business Rule
Yes No
Counter
Offer
Loan Sanction
Customer notified of
offer and sanction letter
shared digitally FI and
RCU triggered
Technical and Legal
Evaluation
Technical valuation of
the property by vendor.
Digital output directly fed
into the LOS. Legal
opinion triggered
Disbursement
Online disbursement of
loan amount
System
Steps
(APIs)
TAT Instantaneous TAT : A few hours
Customer
Involved
Back End
System
07
Eliminating Friction
and provide
consistency across
channels
76%
71%
65%
34%
18%
Signatures
Documentation
ID Verification
Application
Other
Steps of online application
process that must be completed
in branch and can be
automated
Source: The BCG Report, a twice-monthly newsletter based in Carpinteria, CA, 2015 Federal Reserve Payments Study , Team
StormTroopers Student Research
Automated
underwriting
Intelligent Decision Making

8. 08
D i f f e r e n t i a t i n g F e a t u r e s a n d K e y S u c c e s s M e t r i c s o f P r o p o s e d L e n d i n g M o d e l
Given the increased complexity when applying for a loan and
changing socio-economic dynamics, the need for both low- and
high-touch financial advising is stronger than ever to help
improve borrower financial literacy and to help ensure borrowers
know how to properly manage their loans
Financial advice can be the differentiator
Bundling financial advising
services with lending products,
including free initial
consultations with advisors
and simple loan management
tools, could improve customer
perception of lender value.
Of the services consumers would
value, simple financial tools that
help them track credit, stay on
top of payments, and manage
budgets are at the top of the
list. Build or partner with
vendors to offer these self-
service tools as an entry point
to more full-service advising
Advising services most valued by borrowers who
were not offered those services from their lenders
Enriched journeys enhance
customer experience
Differentiating Features
Key Performance Metrics to measure Success Rate
Provider-to-customer ratioLiquidity – Cost to Income Ratio
Net Promoter Score (NPS)Customer Acquisition Cost (CAC)
The percentage of listings that lead to
transactions within a certain time period”. In
practice, provider liquidity is calculated a bit
differently for different types of marketplaces
Number of customers that one provider can serve.
There is no single right ratio that all marketplaces
should strive for. In some cases, the provider-to-
customer ratio might be as low as 1:1
Customer acquisition cost means the price you pay
to acquire a new customer. In an ideal situation,
this number is close to zero: each customer refers
your site to at least one new potential customer, and
your audience grows organically, without you
having to do anything.
Score is obtained by asking the following
question: “How likely is it that you would
recommend [product] to a friend or
colleague?” This gives us more accurate
satisfaction results than simply asking if your users
like your product or not.
Source: ABA survey: The state of the digital lending landscape, Team StormTroopers Student Research

9. 09
U S O P E N B A N K I N G – S U G G E S T E D M O D E L S F O R B U I L D I N G A N I N C L U S I V E L A N D S C A P E
“Using open banking, financial institutions can securely provide other financial institutions and TPPs with seamless access to, and
communication with, customer data through a standards-based technology called open Application Programming Interfaces (APIs) “
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Banking
& capital
markets
2 5 12 38
68
111
179
455
703
911
1,066
Payments
1,347
1,675
Valueadded
services*
Digital
currencies
FIGURE:Growth in Financial Services related APIS 2005-2017 cumulative numbers
• Payments - PSD2 gives merchants direct access to consumers’ bank accounts to
take payments, thereby diverting transaction fees (which can be up to 3%, or
even more, of the cost of products) away from the chain of banks, credit-card
companies and payment processors and onto the merchant’s bottom line (or
passed onto the consumer). Merchants will start leveraging the benefits of
this in the next two years, with some – such as ticket-sellers, online
retailers, and transport operators – growing and diversifying what they
choose to sell.
• Cash management - If a business signs up with four or five banks, its money will
be automatically moved between these accounts by one intermediary to avoid
overdraft fees, maximize the benefits of interest rates and so on.
• Loans. Any business with money to invest will now be able to
extend loans based on the ability to access the borrower’s
bank account to assess risk and then regularly monitor cash
flow.
In exchange, lenders can offer more favorable interest rates.
Perhaps the third most disruptive change.
• Personal financial management. Instead of having an app for
each of our bank accounts, we will be able to use just one app
to get an overview of all our finances, with useful graphs and
trackers to monitor our spending, flag any potential problems,
help us set goals, and offer us solutions, such as loans, to help
us meet our goals a little faster.
Instead of the incumbent bank, a new third party will own the
direct relationship with the customer.
Accenture (2015): North America Consumer Digital Banking Survey, Fujitsu (2016): The Fujitsu European Financial
Services Survey, Team StormTroopers Student Analysis

10. 10
U S O P E N B A N K I N G F U T U R I S T I C U S E C A S E S R E P L A C E M E N T M O D E L S
The onboarding process should aim for convenience
and ease of use, while gathering all attributes required,
minimizing risks, and adherence to KYC obligations
where needed. A flexible architecture therefore
comprises of:
1. Variation in the order of steps: offer a relevant
and tailored onboarding experience;
2. Adjust to local flavors: f.i. KYC requirements
could be a quick check against sanction and
PEP lists, but could also include full
identification procedures;
3. Leaving out steps: when onboarding a TPP that
offers APIs with limited risk exposure, f.i.
finding the nearest ATM, there is no need for
building blocks 4 –7. When a TPP offers PSD2
APIs only, you are only allowed to apply
building block 1.
Best opportunities for incremental revenue growth
WordCloudPrediction
Data Source : Discussion forums, Data Dictionary
We Scrapped the online open
banking forums and white papers
available on the field of Open
Banking. The approach was
based on the Logistic Regression
model to give more weightages.
Our Main objectives for doing
this predictive run was to
come up with the key themes
which we can leverage to
invest a substantial play in the
Open Banking Segment
Source : https://www.pwc.com/us/en/financial-services/financial-crimes/publications/assets/pwc-open-
banking.pdf, Financial Times – AlphaVille, Team Stormtroopers – Student Analysis

11. A p p e n d i x - 1
Digital lenders provide a meaningful amount of debt consolidation
funding (85% of digital lenders consumer originations). Digital
lenders offer yields that are on average 7% below credit cards.
Interest rates on the
platform usually lie
between 7.7% and 9.9%
with 58 loan requests
currently available for
investment. Additionally,
Lending platform supplies
loans to applicants with
varying purposes, whether
to finance their education,
pay their medical bills or
hire a nanny, meaning that
lenders can find higher
interest rates if they wish
to up the risk.
In this highly disruptive environment,
one traditional truth of business has
withstood, or has perhaps even guided,
these technological advances: above
all, the customer experience is king.
More than ever before, businesses have
effective technologies at their fingertips
to quickly and effectively address
customer pain points, while at the same
time dramatically improving their
internal operations.
As machine learning (ML), artificial
intelligence - leadership teams are
finding that failure to harness and
leverage AI puts them behind the
competition.

12. www.companyname.com
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“The best way
to predict the future
is to create it”
A b r a h a m L i n c o l n
Thank you for this wonderful opportunity