Preventing Bank Fraud Using AI/ML: A Strategic Approach to Financial Security

Author(s)	Amit Jha
Country	United States
Abstract	Banking fraud is a persistent threat that undermines customer trust and financial system integrity. With the rise in digital banking and online transactions, traditional rule-based fraud detection systems are proving insufficient. This article explores how Artificial Intelligence (AI) and Machine Learning (ML) are transforming fraud prevention strategies in the banking sector. It proposes a strategic AI/ML fraud prevention framework, outlines the architecture of modern fraud detection systems, and provides real-world case studies demonstrating the efficacy of predictive analytics, anomaly detection, and real-time transaction monitoring. This new paradigm offers a scalable, intelligent, and adaptive defense against evolving fraud tactics.
Keywords	Bank Fraud Prevention, AI/ML in Finance, Predictive Analytics, Transaction Monitoring, Financial Cybersecurity, Fraud Detection Framework, Risk Scoring, Supervised Learning, Unsupervised Anomaly Detection, Model Governance, Real-time AI, AI Ethics in Finance.
Field	Engineering
Published In	Volume 6, Issue 9, September 2025
Published On	2025-09-14
Cite This	Preventing Bank Fraud Using AI/ML: A Strategic Approach to Financial Security - Amit Jha - IJLRP Volume 6, Issue 9, September 2025. DOI 10.70528/IJLRP.v6.i9.1724
DOI	https://doi.org/10.70528/IJLRP.v6.i9.1724
Short DOI	https://doi.org/g93mnk

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