Heterogeneous Graph Attention for Credit Card Fraud Detection in Online Payments

Abstract

The rapid expansion of the digital economy has led to a proportional increase in online payment systems, which in turn has attracted sophisticated malicious actors attempting to exploit these platforms. Credit card fraud in card-not-present scenarios presents a formidable challenge for financial institutions and merchants globally. Traditional machine learning models largely rely on tabular data representations, evaluating transactions in isolation or through rudimentary sequential patterns. These approaches fail to capture the complex, multi-relational dynamics that exist between distinct entities such as users, devices, internet protocol addresses, and the transactions themselves. This paper proposes a comprehensive framework utilizing heterogeneous graph attention networks to model and detect fraudulent activities in online credit card payments. By structuring the transactional ecosystem as a heterogeneous graph, the proposed methodology explicitly maps the diverse interactions between multiple node types. Furthermore, the integration of a dual-level attention mechanism, comprising node-level and semantic-level aggregations, enables the model to dynamically weigh the importance of different neighboring entities and metapaths. Through extensive theoretical analysis and empirical evaluation on large-scale payment datasets, the heterogeneous graph attention approach demonstrates significant improvements over baseline models, particularly in mitigating false positives and identifying deeply hidden organized fraud rings. The results underscore the necessity of relational data modeling in modern financial security architectures.