CATCHM: A novel network-based credit card fraud detection method using node representation learning

Abstract

Advanced fraud detection systems leverage the digital traces from (credit-card) transactions to detect fraudulent activity in future transactions. Recent research in fraud detection has focused primarily on data analytics combined with manual feature engineering, which is tedious, expensive and requires considerable domain expertise. Furthermore, transactions are often examined in isolation, disregarding the interconnection that exists between them. In this paper, we propose CATCHM, a novel network-based credit card fraud detection method based on representation learning (RL). Through innovative network design, an efficient inductive pooling operator, and careful downstream classifier configuration, we show how network RL can benefit fraud detection by avoiding manual feature engineering and explicitly considering the relational structure of transactions. Extensive empirical evaluation on a real-life credit card dataset shows that CATCHM outperforms state-of-the-art methods, thereby illustrating the practical relevance of this approach for industry.