The fraud loss for selecting the model complexity in fraud detection

TL;DR

This paper introduces a new fraud loss function for selecting model complexity in fraud detection, demonstrating improved or comparable performance over traditional AUC-based methods through simulations and real data analysis.

Contribution

A novel fraud loss function is proposed for model complexity tuning, tailored to fraud detection scenarios with limited investigation capacity.

Findings

Fraud loss-based selection often outperforms AUC-based methods.

The proposed method performs well on both simulated and real datasets.

Model complexity tuned by fraud loss yields better detection of fraudulent cases.

Abstract

In fraud detection applications, the investigator is typically limited to controlling a restricted number k of cases. The most efficient manner of allocating the resources is then to try selecting the k cases with the highest probability of being fraudulent. The prediction model used for this purpose must normally be regularized to avoid overfitting and consequently bad prediction performance. A new loss function, denoted the fraud loss, is proposed for selecting the model complexity via a tuning parameter. A simulation study is performed to find the optimal settings for validation. Further, the performance of the proposed procedure is compared to the most relevant competing procedure, based on the area under the receiver operating characteristic curve (AUC), in a set of simulations, as well as on a VAT fraud dataset. In most cases, choosing the complexity of the model according to the fraud loss, gave a better than, or comparable performance to the AUC in terms of the fraud loss.