Case studies to engage

© adiyanth – Distribution Restricted

INFORMED

INSIGHTS INTELLIGENT

DECISIONS

USE EXISTING DATA BETTER, TO GET

AND MAKE

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

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

OverviewNext

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

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.

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

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

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

Case studies to engage

Data & Analytics

Transcript of Case studies to engage