profit based acquisition strategy for credit cards
DESCRIPTION
Based on a paper by RT StewartTRANSCRIPT
Based on paper on ‘Profit- based acquisition strategy
for credit cards’ by ‘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
LowCut 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