A Systematic Review of Transaction Graph Modelling for Anti-Money Laundering in FinTech Apps: Methods, Architectures, and Future Research Directions - Morgan, H. P.; Dimitrov, N.; Laurent, P. (International Journal of Recent Advances in Engineering and Technology, 2025)
Money laundering remains one of the most critical challenges in modern financial systems, particularly within rapidly growing FinTech ecosystems. Traditional rule-based Anti-Money Laundering (AML) systems are increasingly inadequate due to the complexity, scale, and dynamism of digital financial transactions. In response, transaction graph modelling has emerged as a powerful paradigm for detecting suspicious activities by representing financial interactions as interconnected networks. This systematic review examines 30 peer-reviewed studies published between 2018 and 2023 that focus on graph-based AML detection in FinTech applications. The review categorizes methods into graph analytics, machine learning on graphs, deep learning approaches such as Graph Neural Networks (GNNs), and hybrid AI-graph architectures. It further explores system-level implementations including real-time monitoring frameworks, distributed ledger integration, and cloud-based AML pipelines. The findings reveal a significant shift from static rule-based systems to adaptive, AI-driven graph intelligence models capable of capturing hidden laundering patterns such as layering, smurfing, and circular transactions. However, challenges persist in scalability, data imbalance, false positives, and explainability of graph-based models. Additionally, real-world deployment is limited by privacy regulations and lack of standardized datasets. This study highlights emerging trends such as temporal graph modelling, federated learning for AML, and real-time streaming graph analytics. The review concludes by proposing future research directions focusing on explainable AI, privacy-preserving graph learning, and large-scale real-time AML systems tailored for FinTech environments.