Consumer Credit-Risk Models Via Machine-Learning Algorithms
Name
Consumer Credit Risk.pdf
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6.18 MB
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Author(s)
Khandani, Amir Ehsan
Kim, Adlar J.
Lo, Andrew W.
Date Issued
June 2010
Journal
Journal of Banking and Finance
Publisher
Elsevier B.V.
Citation
Khandani, Amir E., Adlar J. Kim, and Andrew W. Lo. “Consumer credit-risk models via machine-learning algorithms☆.” Journal of Banking & Finance 34 (2010): 2767-2787.
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Sloan School of Management
Sloan School of Management. Laboratory for Financial Engineering
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Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
https://doi.org/10.1016/j.jbankfin.2010.06.001
