Ai-powered fraud detection and prevention

Abstract

Given the rapid commoditization of generative artificial intelligence and high-velocity digital transactions, the current rules-based models for detecting fraud have become obsolete. Scientific writings which already exists focuses on highly focused and uni-modal approaches such as graph neural networks for detecting camouflage in specific contexts or hybrid models for clustering data in tabular form. These models, though mathematically sound, lack the required multimodal capabilities and lowlatency response times required for execution within digital transaction environments. To overcome these encounters, a new four-pillar architecture is proposed, integrating generative artificial intelligence defense mechanisms, machine learning models, threat intelligence systems, and alert management systems. The research investigates whether integrating various open-source machine learning models will outperform existing academic models for detecting fraud in transaction environments. The methodology involves a comparative analysis of the existing academic models and the proposed architecture in terms of processing latency, explainability, and the extent of the protection offered. Results indicate that while academic models effectively detect fraud rings in digital transactions offline, the proposed multimodal model achieves higher throughput and protects the authentication perimeter of the transaction system from synthetic media and prompt injection attacks. Through this research, a critical gap in the literature between academic models and real-world implementations is bridged. This paper provides a comprehensive structure for enterprise systems to develop multimodal, explainable machine learning models for fraud detection in high-velocity digital transactions.