Innovating Fraud Prevention and Anti Money Laundering with Automated Pattern Recognition: A Framework-Based Approach

Abstract

This paper has investigated how effective the automated pattern-recognition methods are in strengthening the Anti-Money Laundering (AML) systems in financial institutions to combat fraud. The research design was a mixed-methods, which entailed the use of machine-learning simulation and expert opinion to create and test an AI-based fraud-detection system. The model was trained and tested on anonymized data on transactions, and qualitative data were collected with semi-structured interviews with industry professionals. The results showed that the automated structure was much more effective in detecting frauds, decreasing false-positive notifications, and in the possibility of recognizing anomalies than the traditional rule-based systems. The Random Forest model proved to be the most effective, and expert feedback proved the relevance of the regulatory aspect and applicability of the system. The study found that smart automated models had high potential to enhance the system of compliance and improve the precision of detection but deployment in real time and expansion of datasets validation were suggested to improve further development.