Improving the Art, Craft and Science of Economic Credit Risk Scorecards Using Random Forests: Why Credit Scorers and Economists Should Use Random Forests

33 Pages Posted: 13 Jun 2011 Last revised: 6 Sep 2011

Date Written: June 9, 2011

Abstract

This paper outlines an approach to improving credit score modeling using random forests and compares random forests with logistic regression. It is shown that on data sets where variables have multicollinearity and complex interrelationships random forests provide a more scientific approach to analyzing variable importance and achieving optimal predictive accuracy. In addition it is shown that random forests should be used in econometric and credit risk models as they provide a powerful too to assess meaning of variables not available in standard regression models and thus allow for more robust findings.

Keywords: logistic regression, credit scoring, random forest, econometrics, statistics, predictive models, variable selection

Suggested Citation

Sharma, Dhruv, Improving the Art, Craft and Science of Economic Credit Risk Scorecards Using Random Forests: Why Credit Scorers and Economists Should Use Random Forests (June 9, 2011). Available at SSRN: https://ssrn.com/abstract=1861535 or http://dx.doi.org/10.2139/ssrn.1861535

Dhruv Sharma (Contact Author)

Independent ( email )

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Arlington, VA 22201
United States

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