Role of Artificial Intelligence in Enhancing Fraud Detection Capabilities Across Financial Platforms
Keywords:
artificial intelligence, fraud detection, machine learning, financial platforms, graph neural networks, anomaly detectionAbstract
Financial fraud has escalated in scale and sophistication alongside the digitalisation of banking, payments, insurance, and cryptocurrency platforms, exposing the limitations of static, rule-based detection systems. Artificial intelligence (AI), encompassing machine learning, deep learning, and graph-based methods, has emerged as the dominant paradigm for identifying fraudulent activity across these platforms. This paper presents a systematic review and conceptual synthesis of the literature on the role of AI in enhancing fraud detection capabilities across financial platforms. Drawing on foundational data-mining frameworks and recent systematic reviews published between 2011 and 2026, the review traces the field's evolution from early statistical and rule-based approaches to supervised machine learning, deep neural architectures, and relational graph neural networks (GNNs) capable of modelling collusive and networked fraud. The synthesis indicates that AI substantially improves detection accuracy and adaptability relative to traditional methods, particularly for credit card fraud, insurance fraud, and money laundering, but that persistent challenges, severe class imbalance, concept drift, limited labelled data, and the trade-off between predictive performance and interpretability, continue to constrain real-world deployment. The paper concludes that AI offers substantial and demonstrable value for fraud detection, but that its effectiveness in practice depends on addressing data quality, regulatory interpretability requirements, and the operational realities of real-time financial systems.
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