AI-driven payment security: Enhancing fraud detection in digital transactions

Sukumaran, Sutheesh (2025) AI-driven payment security: Enhancing fraud detection in digital transactions. World Journal of Advanced Research and Reviews, 26 (1). pp. 3017-3024. ISSN 2581-9615

This article examines the transformative impact of artificial intelligence on payment security frameworks in an increasingly digital transaction environment. The article explores how machine learning models, deep neural networks, and behavioral analytics have revolutionized fraud detection capabilities, enabling financial institutions to identify sophisticated attack patterns in real-time while minimizing false positives. The article analyzes the evolution from rule-based systems to adaptive AI architectures, highlighting quantifiable performance improvements in detection accuracy, operational efficiency, and customer experience. Through the article's examination of implementation methodologies, integration challenges, and emerging technologies, the article demonstrates how AI-enhanced security systems complement traditional safeguards, including tokenization, encryption, and biometric authentication, to create comprehensive defense mechanisms. The article reveals that organizations implementing advanced AI security frameworks achieve fraud reduction rates higher than traditional approaches while simultaneously decreasing customer friction. The article concludes with an analysis of future directions, including federated learning, quantum-resistant algorithms, and predictive prevention models that promise to further strengthen payment ecosystems against evolving threats.