Volume 5 • Issue 1 • PP: 01–09 • 2025
Explainable Artificial Intelligence for Real-Time Financial Fraud Detection: A Systematic Literature Review
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Financial fraud detection systems increasingly rely on machine learning to identify suspicious transactions at scale. However, the opacity of many high-performing models raises significant concerns regarding trust, regulatory compliance, and practical deployment in real-time financial environments. Explainable Artificial Intelligence (XAI) has emerged as a promising solution to enhance transparency and accountability, yet its feasibility under real-time constraints remains unclear. This systematic literature review examines empirical studies on explainable AI approaches for financial fraud detection, with explicit focus on real-time applicability. Following PRISMA guidelines, nineteen peer-reviewed empirical studies were selected and analyzed based on fraud domain, model type, explainability technique, evaluation metrics, and evidence of real-time performance. Results show that posthoc explanation methods, particularly SHAP and LIME, dominate the literature, while intrinsic explainability and deployment-level latency reporting remain limited. Despite frequent claims of real-time applicability, only one study provides quantitative runtime evidence. The findings highlight critical gaps: absence of explanation latency evaluation, lack of deployment-oriented validation, and insufficient regulatory compliance integration. This review reveals a systematic disconnect between real-time claims and empirical evidence, establishing the need for standardized latency benchmarking in explainable fraud detection research.
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References
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