Models of sequential decision making in consumer lending

Background In consumer lending, portfolio managers typically have access to a scorecard used to forecast default probability for each applicant. Scorecards are built using historical data on loan accounts and their respective performance data. The inputs into the scorecard include financial, demographic and other personal information about each applicant. The output of the scorecard is a real-valued score for each applicant, which can then be mapped to a probability of default [see Hand and Henley (1997) for scorecard construction]. Similarly, portfolio managers may have access to a scorecard forecasting applicant responses to offers. In turn, forecasts of default probabilities and response probabilities serve as inputs to business metric functions such as expected profit. In setting loan prices (or loan interest rates), loan portfolio managers face a trade-off between response and risk. Consumers prefer lower loan rates and hence lower loan rates results in higher take-up of the products, but lower profits for each account. Loan pricing is further complicated by the phenomenon of adverse selection in which the default rates of individuals who accept a loan offer may be higher than that of those who decline the offer, all other factors being equal (Phillips and Raffard 2009). Adverse selection is thought to be the result of information asymmetry. Credit bureau reports and public records, which lenders use as input for credit risk and response models, may not reflect the circumstances and immediate financial needs of the borrower. Additionally, Abstract