AI-Powered Fraud Detection: Moving from Reactive Investigation to Real-Time Prevention
Author(s): Jalees Ahmad
Publication #: 2602026
Date of Publication: 13.08.2025
Country: United States
Pages: 1-7
Published In: Volume 11 Issue 4 August-2025
DOI: https://doi.org/10.62970/IJIRCT.v11.i4.2602026
Abstract
The global financial ecosystem is currently undergoing an unprecedented digital metamorphosis, characterized by the rapid adoption of decentralized finance, mobile banking, and real-time payment systems. While these advancements have significantly enhanced consumer convenience and operational efficiency, they have simultaneously expanded the attack surface for sophisticated fraudulent actors. Traditional fraud detection methodologies, predominantly reliant on static rule-based frameworks and retrospective manual auditing, are increasingly proving inadequate in the face of modern, high-velocity deceptive maneuvers. This research paper provides an exhaustive analysis of the transition from reactive fraud investigation to proactive, AI-powered real-time prevention. By synthesizing a vast array of peer-reviewed research and industrial case studies, the study explores the implementation of advanced machine learning architectures, including Graph Neural Networks (GNNs) for relational intelligence, Long Short-Term Memory (LSTM) networks for temporal sequence analysis, and Isolation Forests for unsupervised anomaly detection. The analysis further delves into the architectural requirements for real-time processing, highlighting the role of cloud-native microservices, stream-processing engines, and edge intelligence. Furthermore, the paper addresses the critical intersection of technical efficacy, regulatory compliance, and ethical accountability through the lens of Explainable AI (XAI) and Federated Learning. The findings suggest that a multi-layered, synergistic approach - integrating AI with cybersecurity protocol is essential for reducing detection latency from hours to milliseconds, thereby safeguarding the integrity of the global financial network.
Keywords: Artificial Intelligence, Real-Time Fraud Prevention, Machine Learning, Deep Learning, Graph Neural Networks, Financial Cybersecurity, Explainable AI, Digital Banking, Federated Learning, Transaction Monitoring.
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