Cost-sensitive Semi-supervised Classification for Fraud Applications

TL;DR

This paper introduces a novel integration of Cost-Sensitive Learning with Semi-Supervised Classification to improve fraud detection, especially for shill bidding, by effectively handling imbalanced data and misclassification costs.

Contribution

It is the first to combine CSL with SSC specifically for fraud detection, demonstrating improved accuracy and cost efficiency in identifying fraudulent bidders.

Findings

Achieved 99% fraud detection rate

Reduced total misclassification cost

Validated with real shill bidding dataset

Abstract

This research explores Cost-Sensitive Learning (CSL) in the fraud detection domain to decrease the fraud class's incorrect predictions and increase its accuracy. Notably, we concentrate on shill bidding fraud that is challenging to detect because the behavior of shill and legitimate bidders are similar. We investigate CSL within the Semi-Supervised Classification (SSC) framework to address the scarcity of labeled fraud data. Our paper is the first attempt to integrate CSL with SSC for fraud detection. We adopt a meta-CSL approach to manage the costs of misclassification errors, while SSC algorithms are trained with imbalanced data. Using an actual shill bidding dataset, we assess the performance of several hybrid models of CSL and SSC and then compare their misclassification error and accuracy rates statistically. The most efficient CSL+SSC model was able to detect 99% of fraudsters and with the lowest total cost.