Adversarial Learning in Credit Card Fraud Detection

Credit card fraud is an expensive problem for many financial institutions, costing billions of dollars to companies annually. Many adversaries still evade fraud detection systems because these systems often do not include information about the adversary's knowledge of the fraud detection mechanism. This project aims to include information about the "fraudster's" motivations and knowledge base into an adaptive fraud detection system. In this project, we use a game theoretical adversarial learning approach in order to model the fraudster's best strategy and pre-emptively adapt the fraud detection system to better classify these future fraudulent transactions. Using a logistic regression classifier as the fraud detection mechanism, we initially identify the best strategy for the adversary based on the number of fraudulent transactions that go undetected, and assume that the adversary uses this strategy for future transactions in order to improve our classifier. Prior research has used game theoretic models for adversarial learning in the domains of credit card fraud and email spam, but this project adds to the literature by extending these frameworks to a practical, real-world data set. Test results show that our adversarial framework produces an increasing AUC score on validation sets over several iterations in comparison to the static model usually employed by credit card companies.