Title: DISCO: Decoupling Representation Learning and Risk Control for Reliable Credit Card Fraud Detection

Authors:  Ruonan Zhu, Xinru Zhang, Tao Wang, Jing-Xiao Liao, Sai-Ho Chung and Xiaoge Zhang

Abstract: Credit card fraud detection remains a significant challenge due to extreme class imbalance, evolving fraud patterns, and asymmetric misclassification costs. While existing machine learning methods often prioritize fraud detection performance, they frequently overlook model risk management, which is crucial for reliable decision making in high-stakes environments. To address this issue, we introduce DISCO — a framework that combines discriminative and robust representation learning with a conformal prediction-based mechanism for provable risk control. In essence, DISCO is established upon the principle of decoupling representation learning from risk-controlled decision-making. To this end, we first leverage deep metric learning (DML) to construct an embedding space that is inherently robust to class imbalance and resilient to concept drift. Subsequently, we employ a conformal risk control (CRC) mechanism to provide a formal statistical guarantee on the false negative rate (FNR) of the resulting model according to a user-specified FNR target. Computational experiments on a real-world dataset demonstrate that DISCO outperforms state-of-the-art methods in both classification performance and operational efficiency. To the best of our knowledge, this is the first work to integrate a DML-based representation learning with a formal risk control mechanism for reliable credit card fraud detection. The proposed framework therefore offers a trustworthy and practical solution that addresses the dual imperatives of predictive performance and reliability.

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