Consumer credit-risk models via machine-learning algorithms |
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Authors: | Amir E Khandani Adlar J Kim Andrew W Lo |
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Institution: | MIT Sloan School of Management and Laboratory for Financial Engineering, United States |
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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 R2’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 suggest that aggregated consumer credit-risk analytics may have important applications in forecasting systemic risk. |
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Keywords: | G21 G33 G32 G17 G01 D14 |
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