Journal of Risk Model Validation

Credit scoring optimization using the area under the curve

Anne Kraus and Helmut Küchenhoff


In consumer credit scoring, the area under the receiver operating characteristic curve (AUC) is one of the most commonly used measures for evaluating predictive performance. In our analysis, we aim to explore different methods for optimizing the scoring problem in order to maximize the AUC. Not only are the existing methods pertaining to the use of the AUC to measure prediction accuracy evaluated, but the AUC is introduced as an objective function to optimize prediction accuracy directly. For the AUC approach, the coefficients are estimated by calculating the AUC measure using the Wilcoxon-Mann-Whitney and Nelder-Mead algorithms. In a simulation study, we compare our new method to the logit model using different measures for predictive performance. The simulation study indicates the superiority of the AUC approach in cases where the logistic model assumption fails. From machine learning we explore boosting methods by additionally using the AUC as a loss function. Our evaluation of German retail credit data includes different performance measures and shows superior results in terms of the prediction accuracy of the boosting algorithms as well as the AUC approach compared with the most widely used logistic regression model.

Sorry, our subscription options are not loading right now

Please try again later. Get in touch with our customer services team if this issue persists.

New to View our subscription options

You need to sign in to use this feature. If you don’t have a account, please register for a trial.

Sign in
You are currently on corporate access.

To use this feature you will need an individual account. If you have one already please sign in.

Sign in.

Alternatively you can request an individual account here