Case studies to engage
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Page 1© adiyanth – Distribution Restricted
INFORMED
INSIGHTS INTELLIGENT
DECISIONS
USE EXISTING DATA BETTER, TO GET
AND MAKE
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Page 2
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
Inpu
tSo
lutio
n St
epDe
liver
able
s
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 TeamInterview
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
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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
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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
OverviewNext
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CONFIDENTIAL & LEGALLY PRIVILEGED
Case Studies
Acquisition Strategy: a. Lead Qualification using Decision Treeb. Channel Effectivenessc. Response Model for Manufacturer-Driven Auto Loan Program
Customer Management Strategy:d. Automated Credit Line Increase Program (Details Provided)e. Improving Product Holding Ratio f. Attrition Scorecard (Details Provided)g. Risk Based Pricing (Details Provided)
Risk Assessment and Mitigation:h. Developing Identity Fraud Procedures as Risk Mitigation Leveri. Risk Categorization based on Behavior Scores (Details Provided)j. Collections Call Center Capacity Planning
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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
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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.
Business Impact or Benefit 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.
Analytics Solution Methodology 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
Key Insights and Recommendations 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
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Attrition Scorecard
Business Objective
Identify customers who are likely to attrite
Business Impact or Benefit
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
Analytics Solution Methodology
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
Key Insights and Recommendations
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
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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).
Business Impact or Benefit
The Risk Scorecard was used to identify savings account customers eligible
for Cross-Sell for Asset Products.
Analytics Solution Methodology
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.
Key Insights and Recommendations
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