The Evolution of AI in Fraud Detection: Technical Frameworks and Cross-Sector Applications

Abstract

This article presents a comprehensive analysis of AI-driven fraud detection systems, examining their evolution from gaming industry applications to broader cross-sector implementations. This article explores the architectural framework and methodological approaches that have enabled successful fraud detection in gaming environments, where real-time pattern recognition and behavioral analysis have proven particularly effective. This article extends beyond gaming to investigate parallel implementations in financial services, e-commerce, and healthcare sectors, revealing common patterns and industry-specific adaptations in fraud detection strategies. Through a detailed examination of technical challenges, including data quality requirements, model complexity, and system integration considerations, this article identifies critical factors for the successful deployment of AI-driven fraud detection systems. This article suggests that while each industry presents unique challenges, the fundamental principles of AI-driven fraud detection remain consistent across sectors. This article contributes to the growing body of knowledge on adaptive fraud detection systems and provides practical insights for organizations seeking to enhance their fraud prevention capabilities through artificial intelligence.