Consumer Credit Risk Models Via Machine-Learning Algorithms (Khandani A., Kim A. J., Lo A. W.)

Credit Cards Household Strategies Risk-taking and Risk Management

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.
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Libref/ Khandani A., Kim A. J., Lo A. W. (2010) "Consumer Credit Risk Models Via Machine-Learning Algorithms", AFA 2011 Denver Meetings Paper, pp. 1 - 49
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