AI-Driven Web Platform for Anti-Money Laundering Through Transaction Behaviour Analysis

Money laundering is a major financial crime that requires effective transaction monitoring and suspicious activity detection. This research focuses on an AI-driven web platform for Anti-Money Laundering (AML) using transaction behaviour analysis. The proposed approach explores the use of machine learning to identify unusual transaction patterns, assess potential risk, and support the detection of suspicious financial activities. The platform aims to assist analysts in monitoring transactions and prioritising cases for further investigation. Its effectiveness depends on the quality of transaction data, model selection, evaluation, and human review.