Mastering Fraudulent Schemes: A Unified Framework for AI-Driven US Banking Fraud Detection and Prevention
Abstract
This research paper presents a comprehensive framework for AI-driven banking fraud detection and prevention in the United States. The proposed system integrates advanced artificial intelligence techniques to enhance the efficiency of identifying and mitigating fraudulent schemes within the banking sector. Leveraging machine learning algorithms, anomaly detection, and predictive modeling, the framework aims to provide a unified and proactive approach to combat various forms of fraud. Key elements include real-time transaction monitoring, behavior analysis, and adaptive learning mechanisms. The research emphasizes the significance of an integrated AI solution in addressing the evolving landscape of banking fraud, contributing to a more secure and resilient financial ecosystem.
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