Behavior-Based Predictive Models

2. Behavior-Based Predictive Models

11. Data Summary For outcome, Variance = 4 times Mean

12. EDA on Outcome 1. 80% Cardholders have 0 delinquency. 2. Large dispersion with long tail

15. NB Output

16. NB Portfolio Prediction

17. How to Score

18. NB Account Prediction

21. Hurdle Output Drivers for Presence of Delinquency Drivers for Severity of Delinquency

22. Hurdle Portfolio Prediction Un-normalized Truncated Poisson Distribution Composite Distribution

23. Hurdle Segmentation 1. Segmentation Model: Logistic Model separates BLUE from RED 2. Severity Model: Truncated Poisson predicts severity of RED

24. Hurdle Account Prediction

27. ZIP Output Drivers for Existence of Risk Drivers for Severity of Risk

28. ZIP Portfolio Prediction Un-normalized Poisson Distribution Composite Distribution

29. ZIP Segmentation Same outcome but different risk implications 1. Blue (72%): Established, free from financial risk 2. Red (8%): Vulnerable, might deteriorate in bad time

30. ZIP Account Prediction

33. LCP Output Drivers for Low Risk Drivers for High Risk

34. LCP Portfolio Prediction Poisson Distribution of High Mean Composite Distribution Poisson Distribution of Low Mean

35. LCP Segmentation

36. LCP Account Prediction ~ 5% benefit at high-risk zone

37. Parameter Comparison In Hurdle / ZIP, 1 st set of BETAs explain why delinquent and 2 nd set explain how many delinquencies will be.