Journal of Banking & Finance Vol. 34 No. 11 2010
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 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.
- DOI
- 10.1016/j.jbankfin.2010.06.001
- Volume
- 34
- Issue
- 11
- Pages
- 2767-2787
- Language
- en
- Sources
- openalex crossref bibtex:phds-export.bib