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September 17, 2025Open Access

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

Key Points

AI and ML transform fraud prevention strategies, enabling modern banking systems to effectively combat threats.
Real-world case studies show effective implementation of predictive analytics and anomaly detection in fraud detection.
The proposed framework outlines an architecture that supports real-time transaction monitoring to prevent fraud.
This new approach offers intelligent and adaptive defenses against evolving tactics in bank fraud.

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.

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

Key Points

Abstract

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