Evaluation of Deep Neural Networks for Reduction of Credit Card Fraud Alerts

Abstract

Fraud detection systems support advanced detection techniques based on complex rules, statistical modelling and machine learning. However, alerts triggered by these systems still require expert judgement to either confirm a fraud case or discard a false positive. Reducing the number of false positives that fraud analysts investigate, by automating their detection with computer-assisted techniques, can lead to significant cost efficiencies. Alert reduction has been achieved with different techniques in related fields like intrusion detection. Furthermore, deep learning has been used to accomplish this task in other fields. In our paper, a set of deep neural networks have been tested to measure their ability to detect false positives, by processing alerts triggered by a fraud detection system. The performance achieved by each neural network setting is presented and discussed. The optimal setting allowed to capture 91.79% of total fraud cases with 35.16% less alerts. Obtained alert reduction rate would entail a significant reduction in cost of human labor, because alerts classified as false positives by the neural network wouldn't require human inspection.

RDQN: ensemble of deep neural network with reinforcement learning in classification based on rough set theory for digital transactional fraud detection

“…Here are some of the best training models created by various authors. Carrasco et al [18] applied advanced fraud detection techniques based on complicated rules, statistical modelling, and ML. As is the case with this, he discussed several deep neural networks, such as MLP, CNN, and DAE, that are tested to quantify their potential to detect the false-positive rate.…”