Consumer Credit Risk Models Via Machine-Learning Algorithms

Abstract:     
We apply machine-learning techniques to construct nonlinear nonparametric forecasting models of consumer credit risk. By combining customer transactions and credit bureau data from January 2005 to April 2009 for a sample of a major commercial bank's customers, we are able to construct out-of-sample forecasts that significantly improve the classification rates of credit-card-holder delinquencies and defaults, with linear regression R-squared's of forecasted/realized delinquencies of 85%. Using conservative assumptions for the costs and benefits of cutting credit lines based on machine-learning forecasts, we estimate the cost savings to range from 6% to 25% of total losses. Moreover, the time-series patterns of estimated delinquency rates from this model over the course of the recent financial crisis suggests that aggregated consumer-credit risk analytics may have important applications in forecasting systemic risk.

Number of Pages in PDF File: 49

Keywords: Household Behavior, Consumer Credit Risk, Risk Management, Credit Card Borrowing, Machine Learning, Nonparametric Estimation

JEL Classification: G21, G33, G32, G17, G01, D14

working papers series