Validating risk models with a focus on credit scoring models

This paper encompasses three parts of validating risk models. The first part provides an understanding of the precision of the standard statistics used to validate risk models given varying sample sizes. The second part investigates jackknifing as a method to obtain a confidence interval for the Gini coefficient and K-S statistic for small sample sizes. The third and final part investigates the odds at various cutoff points as to its efficiency and appropriateness relative to the K-S statistic and Gini coefficient in model validation. There are many parts to understanding the risk associated with the extension of credit. This paper focuses on obtaining a better understanding of present methodology for validating existing risk models used for credit scoring, by investigating the three parts mentioned. The empirical investigation shows the precision of the Gini coefficient and K-S statistic is driven by the sample size of the smaller, either successes or failures. In addition, a simple adaption of the standard jackknifing formula is possible to use to get an understanding of the variability of the Gini coefficient and K-S statistic. Finally, the odds is not a reliable statistic to use without a considerably large sample of both successes and failures.