Detecting and Preventing Money Laundering Using Deep Learning and Graph Analysis

Money laundering is a major worldwide issue facing financial organizations, with its increasingly complicated and changing methods. Conventional rule-based anti-money laundering (AML) systems can fail to identify advanced fraudulent activity. This study shows a new hybrid model to detect suspicious transaction patterns precisely by efficiently combining GraphSAGE, a graph-based Machine Learning (ML) technique, with Long Short-Term Memory (LSTM) networks. The suggested approach uses GraphSAGE's relational capabilities for graph-structured anomaly detection and the temporal strengths of LSTM for sequence modeling. With excessive traditional ML and stand-alone Deep Learning (DL) techniques, the Hybrid LSTM-GraphSAGE model achieves an accuracy of 95.4% using a simulated dataset reflecting real-world financial transactions. The findings show how well our combined strategy lowers false positives and improves the identification of advanced AML operations. This work opens the path for creating real-time, intelligent, flexible money laundering detection systems appropriate for current financial situations.