MACHINE LEARNING FRAMEWORKS FOR INTELLIGENT FINANCIAL FRAUD DETECTION SYSTEMS
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Abstract
The global financial system loses an estimated USD 47.6 billion annually to fraudulent transactions, cyber-enabled theft, identity deception, and money laundering, with losses escalating at a compound annual growth rate of 21.7% as digital payment volumes surge and adversarial tactics evolve in sophistication. Machine learning (ML) has emerged as the pre-eminent technological response to this challenge, offering adaptive, data-driven detection capabilities that far surpass the static rule-based systems that dominated fraud prevention for the preceding three decades. This research paper presents a comprehensive, evidence-based examination of machine learning frameworks for intelligent financial fraud detection, systematically analysing the architecture, performance characteristics, and deployment realities of supervised, unsupervised, semi-supervised, and graph-based ML approaches across credit card fraud, anti-money laundering (AML), insurance fraud, identity theft, account takeover, and real-time payment fraud domains. Through a rigorous mixed-methods approach—encompassing systematic literature synthesis, quantitative benchmarking of six ML model families across industry-standard datasets, and four empirical case studies spanning the United States, United Kingdom, India, and Singapore—this study demonstrates that mature ML fraud detection implementations achieve fraud loss reductions of 28–39%, false positive rate reductions of up to 52%, and mean detection latency improvements of 78% relative to rule-based baselines. The paper further evaluates persistent challenges including class imbalance, concept drift, adversarial manipulation, regulatory explainability requirements, and cross-institutional data scarcity, while proposing a forward-looking framework encompassing federated learning, graph neural networks, transformer-based transaction modelling, and real-time streaming ML for next-generation fraud intelligence. The findings underscore the imperative for financial institutions to adopt ensemble and hybrid ML architectures that balance detection performance, operational scalability, and regulatory compliance.
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References
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