Case studies to engage

Typical Model Development – Bird’s Eye View
Existing Process
Study &
Documentation
Prospect Base
Segmentation
Channel
Optimization
Credit Approval &
Delinquency distribution
by band
Process Benchmarks
Steady-State
Model
Deployment
As-is execution Map
InputSolutionStepDeliverables
Models & Underwriting
Policies
Model Based
Decisioning
Target Market
Assessment
Market Segments &
Credit Needs
Suggested Modeling
Features
Basic Market Study
Competitive Landscape &
Offerings
Prospect Segment
Profiles
Prospect Segment
Model
Segment Product
Mapping
(if sample from past)
Prospect Scoring on
all models
Product-wise
Scoring Models
Model Performance
Details
Product
Propensity
Estimation
Definition of Constraints
Total Acquisition Cost
Definition
Optimal Inventory
allocation by channel
List Generation by
channel
Underwriting Cirteria &
Suppressions Overlay
Model Deployment
Diagrams
Invalid Score Codes
H/W, S/W requirements
Process Owner
Interview
Process Team
Interview
Existing Documents
Internal Reports
Statement of
Objectives
Demographic Data
on Sample
Credit Bureau
Data on Sample
Current / Past Prospect
Sample
Current credit
Distribution
(if sample from past)
Response History
Demographic & Risk
profile of prospects @
time of solicitation
Past Campaign
Samples
Channel Performance
report by product
Channel Costs
Underwriting Criteria &
Suppressions by channel
Prospect Database
Access
Models & Allocation
details(from previous
solution steps)
Steady-state process
IT Team Meetings
IT System
Architecture Diagram
DeployDefine & Measure Analyze, Design & Verify

Model Development– Data Analysis
•Data Access
•Data Transfer
•Data Storage
•Data Validation
•Quality Check
•Report Out
Customer
Data
• Missing Value Treatment
• Outlier Treatment
•Data Transformation
• Derived Variables
• Creation of Master Data Set
• Validation and Report Out of
MDS
Data
Manipulation • Generating Trend Reports
• Generating Uni/Multi-variate
and Correlation Reports
•Creating Visualization Charts
• Validation of Trends and
Correlation Reports
• Descriptive Analysis Report Out
Descriptive
Analysis
Next

Model Development– Intelligence Deployment
• Model Build – Creation of Candidate Models
• Model Validation – Out-of-Sample, Out-of-
Time, Bootstrapping
• Model Selection – What-if Scenarios, Lift
Charts, Customer Dimensions & Model
Complexity
• Statistical Tests – Multi-collinearity,
specification & identification condition
Statistical
Analysis
• Deciling and Segmenting
• “Actionable” Insights – Pattern
Recognition
• Population Summary Reports
• Impact Assessment Reports
• Margin of Error Estimation
Intelligence
Creation • Portal Base Deployment of
Visualization charts .
• Deployment of Scores for rank
ordering
• Creation of Scorecards to
differentiate customer behavior
Intelligence
Deployment
Overview
Next

CONFIDENTIAL & LEGALLY PRIVILEGED
Case Studies
Acquisition Strategy:
a. Lead Qualification using Decision Tree
b. Channel Effectiveness
c. Response Model for Manufacturer-Driven Auto Loan Program
Customer Management Strategy:
a. Automated Credit Line Increase Program (Details Provided)
b. Improving Product Holding Ratio
c. Attrition Scorecard (Details Provided)
d. Risk Based Pricing (Details Provided)
Risk Assessment and Mitigation:
a. Developing Identity Fraud Procedures as Risk Mitigation Lever
b. Risk Categorization based on Behavior Scores (Details Provided)
c. Collections Call Center Capacity Planning

Automated Credit Line Increase Program
Business Objective
 Who are the customers eligible for an automated credit line increase
program
 Of those customers, who are eligible, what should be the optimal size of
the increase
 What are the impacts on delinquencies, loss rates and Net Income due
to this program
Business Impact or Benefit
 Have identified about 20% of the customers who are eligible for one-
time CL increase program
 Provided a list of customers based on the decision tree who become
eligible for CL increase each month
 Annualized Net Income of $2MM was estimated
Analytics Solution Methodology
 The customers eligible for credit line increase program was determined
based on the past profitability of the customers using decision tree
methodology based on CHAID algorithm.
 An Account Level Profitability metric was calculated for each
customer and used as the objective function
 Critical drivers including behavior scores and Risk Scores were
analyzed to identify potential downside impacts
 Segments accounting for at least 5% of Net Income and having
at least 1% of the number of customers have been chosen for
credit line increase.
 Genetic Algorithm based linear optimization was performed where the
constraints given, including,
 Utilization rates post CL increase should not exceed 75%.
 loss rates not to exceed 10% of the current level.
 90+ days past due rate not to exceed 15% of the current level
 An Excel-based Monte Carlo simulation exercise was conducted to
analyze the potential downside with $300 and $600 CL increase .
Key Insights and Recommendations
 Past Profitability was best seen in customers have Low risk scores FICO
scores of 495 – 545 range, while, the behavior scores of 100-180.
 The utilization rates were also high in these buckets
 The best segments which were eligible for CL increase where
 Low FICO Scores
 Medium Behavior Scores
 Current Utilization of 45%
 Number of times 30+ Past Due <=2
 Days Since Last Transaction <= 3 months

Risk Based Pricing
Business Objective
 What kind of revenue opportunities does re-pricing of customer
portfolio offer based on Risk Based Pricing
 What are the impacts on delinquencies and long term profitability due
to changes on customer profile arising out of this strategy
 What is the best method of quantifying customers’ responsiveness and
the risk behavior and determine the price point at which it is still
profitable to acquire a customer though the risk is high.
 The client has successfully acquired new customers from segments that
were originally not targeted.
 This approach has helped to penetrate deeper into the customer base,
which was, earlier out of bound for the marketing department
 This initiative helped the client to provide $2MM net income towards
the annual Net Income target.
 This initiative provided the roadmap for more efficient trade-off matrix
to address the burning issue of Low Risk prospects also demonstrate low
responsiveness to marketing campaigns.
 The Risk scores and Response scores for a customer has been calculated
 Grouped customers into 100 segments based on the risk scores
and response scores
 Calculated profitability of the segment taking into
consideration – Acquisition, activation, response rates,
utilization rates, delinquencies, roll rates, charge-off rates,
operational costs, Technology enabling costs and Customer
Service costs.
 Look-alikes based on credit limit, average ticket size, vintage, sourcing
were created to understand the future behavior of customers if re-
priced.
 In case of new acquisitions, pilot campaigns were conducted by
lowering the minimum Risk Score Cut-off.
 Finally, Customer segments that had +ve ROI were targeted and
acquired
 Higher Risk customers are comparatively price inelastic. However, the
lower risk customers display much higher elasticity towards Risk Based
Pricing.
 In most cases, in high risk customers, the increased margins are negated
by higher operational costs.
 Acquiring new customers at higher APR is far more profitable than re-
pricing an existing customer to higher APR because of adverse selection.
Risk Bucket Re-pricing
Very Low Risk Reduce the APR in the range
of 5%-10%
Moderate Risk Unchanged
Moderate-High Risk 1%-2% increase in APR
High Risk 2%-5% increase in APRDetails

Attrition Scorecard
Business Objective
 Identify customers who are likely to attrite
 Attrition Score provided propensity to attrite, basis which Retention
Campaigns could be evolved
 This exercise also provided a detailed analysis to understand the drivers of
attrition.
 An Annualized Retention of over INR 50 Crores of Balances-at-Risk by
executing retention campaigns
 Solution developed analyzed the 4 stages of customer attrition –
 Changes in Customer Transaction Behavior
 Reasons for closing the account to identify “preventable” attrition
 Link the potential reasons with actionable mitigates
 Rank order customers based on their likelihood of attriting in the
next 6 months
 Separate solution was developed for 2 types of attrition noticed
 Silent Attrition: Customers who reduce keep only min balance and
do not transact on their account
 Formal Attrition: Customers who formally close their relationship
with the Bank.
 Customers were rank ordered based on their Attrition Score, CNR (customer
Net Revenue) and Product Holding
 Primary Reason for attrition is change of employment followed by change
of residence.
 Customers who have more than 1 product tend to be less prone to
attrition.
 The early warning signs of attrition are
 Reduction in Average Quarterly Balance
 Reduction in number of customer-initiated transactions
 Customers having Investment relationship with the Bank are least prone to
attrition.
Details

Risk Categorization based on Behavior Scores
Business Objective
 Categorize Risk Behavior at the time of acquisition based on expected loss
rates and PDO (points to Double Odds).
 The Risk Scorecard was used to identify savings account customers eligible
for Cross-Sell for Asset Products.
 The Solution methodology involved the following 4 steps
 Development of Risk Scorecard using Credit Bureau and Internal
transaction behavior.
 Converting the default propensity scores into an scorecard ranging
from 200 to 800 using the concept of scaling.
 Calculating the Points to Double Odds ratio for each scorecard by
fixing the points at 20. This is done to ensure customer risk is
ascertained across the score bands.
 Run an historical validation to ascertain the ability of the scorecard
to categorize risk across the spectrum.
 The critical variables that have come significant are
 Number of Trades (Credit Bureau Data)
 Number of times Past Due in the previous 12 months (Internal
Data)
 Number of trades past due (Credit Bureau Data)
 Account Vintage
 Channel of acquisition

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