Data Analytics and Digital Forensics in Financial Fraud Detection: Enhancing Accuracy, Timeliness, and Governance
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Abstract
The high rate of digitalization of financial services has changed fraud into a structurally intricate and dynamically adaptive phenomenon which is integrated in relational networks and high-velocity transaction ecosystems. This paper constructs a composite conceptual framework that places financial fraud detection at the nexus of advanced data analytics and digital forensics, with the emphasis on the simultaneous maximization of accuracy, timeliness and governance. The paper, which focuses solely on recent developments in the fields of deep learning, graph neural networks, streaming architectures, and risk-based anti-money laundering (AML) systems, summarizes the evidence of the beneficial role of generative models, multimodal attention mechanisms, and relational graph learning in boosting predictive accuracy in imbalanced and adversarial environments. It also identifies how real time and federated monitoring systems can improve on the latency of detection without compromising the interpretability and regulatory defensibility. The discussion demonstrates that explainable artificial intelligence, alarm qualification systems and false-positive minimization strategies are important in enhancing audit traceability and institutional responsibility. The combination of analytical complexity and forensic preparedness and alignment of compliance is a step forward in the proposed three-layer model since it promotes a multidimensional approach to fraud detection performance. The results indicate that sustainable fraud management needs vertically aligned advanced analytics, adaptive monitoring, and governance assurance capabilities and not the model optimization on its own.
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© 2026 Horizons Intermediary Journal of Business Research. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). All rights reserved by the journal.
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