A Statistical Technique to Enhance Application Scorecard Monitoring

28 Pages Posted: 18 Jun 2019

Date Written: June 17, 2019

Abstract

Application scoring plays a critical role in determining the future quality of a lender’s book. It is therefore important to monitor the performance of an application scorecard to ensure it performs as expected. Attention has so far been focused on application scorecard modeling and assembly techniques. An area that has received less consideration is application scorecard implementation. Performance measures on the accepted population appear to change in predictive power after the implementation of an application scorecard. This paper introduces and demonstrates a statistical measure to track the performance of the accepted population after the point of implementation on a comparable basis against the development window of the application scorecard.

Keywords: credit risk, application scoring, credit risk management, Gini coefficient

Suggested Citation

Kritzinger, Nico and van Vuuren, Gary W, A Statistical Technique to Enhance Application Scorecard Monitoring (June 17, 2019). Journal of Credit Risk, Vol. 15, No. 2, 2019. Available at SSRN: https://ssrn.com/abstract=3405462

Nico Kritzinger (Contact Author)

North West University ( email )

Hoffman Street
Potchefstroom, 2520
South Africa

Gary W Van Vuuren

North West University ( email )

Hoffman Street
Potchefstroom, 2520
South Africa

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