Profit- based acquisition strategy for credit cards

- RT Stewart

Presented by Piyush

1. Revenue Spends/Interchange Finance charges Others

2. Cost Fixed costs Acquisition costs Other operating costs

3. Loss

Credit Card Profit & Loss

Which customer to acquire?

◦ Potential Revenue NOT considered◦ Approve or Decline decision based solely on

risk Minimize bad rates

Current practice for customer acquisition

Decline Approve

Low Cut off

Risk

High

High

Credit Score

Before we move ahead…

Bad Rate / Charge-Off rate - ◦ Ratio of number of customers defaulting on credit cards

to the total number of customers.

Credit /FICO score – ◦ A score representing the creditworthiness of a person.

Few Credit Card Jargons

Primary - Develop and test a methodology to model revenue.

Use revenue models along with risk models for acquisition decisions.

Objective

1. Revenue is highly correlated with risk

2. Structural Change / Population drift

Challenges in modelling revenue / profit

Modeling problem - ◦ Predict cumulative spends during first 2 years of account’s

life

Independent variables –◦ Credit bureau data◦ Account application data

Training data –◦ A sample data set of 300,000 credit card accounts

Segmentation – ◦ Segments based on credit bureau scores. ◦ Multiple spend models.

Methodology

Log(Spend) used as response variable

Modeling equation –log(Spend) = β₀ + β1 X1 + β2 X2 + β 3 X 3 +......

where β₀ , β1, are regression parameters

Model details - I

Independent variables (X1 , X2 , ...)◦ Binning approach used

Increases model stability Easier implementation Capture non-linear relationships

◦ Correlated with spend but uncorrelated with Risk

◦ Examples – Applicant’s monthly income

[$0-$2500] , [$2500-$5000] ,.. Age of oldest revolving trade in months

[0-71] , [71-999]

Model details - II

1. Revenue is highly correlated with risk

2. Structural Change / Population drift

Challenges

1. Revenue is highly correlated with risk◦ Creating risk segments based on bureau score

2. Structural Change / Population drift

Challenges addressed!

1. Revenue is highly correlated with risk◦ Creating risk segments based on bureau score

2. Structural Change / Population drift◦ Leveraging binning approach for independent

variables

Challenges addressed!

Results Models rank order spend.

Example - Model for segment FICO (720-760)◦ Spend shows a positive

slope.◦ Charge-off line is

approximately horizontal.

Higher mean spend with same bad rate.

 Approval rate(%)

Bad Rate (%)

Mean Spend ($)

FICO Only 90% 1.60% 15,032FICO and Spend

score 83% 1.60% 16,617

Conclusion Use risk model in conjunction with a

revenue model

Advantages ◦ Easily communicated◦ Easily implemented in systems◦ Track able and easily recalibrated.

Limitations◦ A single credit card portfolio

considered