Authors : MAMUNUR R RAJA, Md Anwar Hosen, Md Farhad Kabir, Sharmin Sultana, Shah Ahammadullah Ashraf, Rakibul Islam
Publication date : 2025/6/1
Volume : 16
Issue : 6
Description : 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 graphstructured anomaly detection and the temporal strengths of LSTM for sequence modeling. With excessive traditional ML and stand-alone Deep Learning (DL) techniques, the Hybrid LSTMGraphSAGE 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 …
Total citations : Cited by 30
Scholar articles :
MR RAJA, MA Hosen, MF Kabir, S Sultana, SA Ashraf… – International Journal of Advanced Computer Science & …, 2025