An Adversarial Learning with Sum of Top-K Loss Framework for Credit Card Fraud Detection

Credit card fraud detection is a challenging problem due to imbalanced data and adversarial attacks. We propose an adversarial learning with sum of top-K loss (AST) framework for this task. Our approach integrates adversarial learning into logistic regression to mitigate the blind spots of traditional machine learning methods, such that fraudsters manipulate features to evade detection. To further improve the robustness of classification under imbalanced data, we introduce a sum of top-K loss, replacing the plain empirical loss. We have designed a gradient descent approach to optimize the adversarial logistic regression model with the sum of top-K loss. Experiments validate the effectiveness of our AST framework over the prevalent machine learning techniques and existed adversarial learning algorithm.