The Future of Banking Fraud Detection: Emerging AI Technologies and Trends
Keywords:
Banking Fraud Detection, AI-based Banking system, Machine Learning, Real-Time Transaction Monitoring, Explainable AI.Abstract
Banking fraud has changed significantly as a result of the financial services industry's rapid digitization, and hackers are using more complex strategies to create new difficulties. Due to the dynamic and ever-changing nature of contemporary threats, traditional rule-based fraud detection systems that depend on static thresholds and manual oversight have proven insufficient. Through an analysis of the integration of machine learning, deep learning, and adaptive learning systems, this study critically evaluates the paradigm shift toward artificial intelligence (AI)-driven fraud detection in banking. The study assesses real-world implementations across three worldwide banks, covering 148 million transactions in 12 jurisdictions, using a multi-stage AI architecture. In comparison to older systems, the suggested hybrid ensemble model increased detection rates by 63% and decreased false positives by 81%, resulting in an AUC score of 99.8%. Explainable AI, adversarial training, and federated learning are important breakthroughs that improve model robustness, privacy, and regulatory compliance. The operational framework processes 28,000 transactions per second with a latency of less than 100 milliseconds, enabling real-time detection at an industrial scale. Investigators can confirm 92% of AI-driven judgments thanks to the incorporation of SHAP-based explainability, which also solves ethical and transparency concerns. The results highlight AI's revolutionary potential in lowering fraud losses, enhancing operational effectiveness, and preserving consumer confidence. In order to further develop the field of banking fraud detection, the paper's conclusion highlights a number of ongoing issues, including regulatory obstacles, antagonistic threats, and the necessity of constant human oversight. It also suggests future directions for industry collaboration and study.
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