Abstract: 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.

Mamunur R Raja, Md Anwar Hosen, Md Farhad Kabir, Sharmin Sultana, Shah Ahammadullah Ashraf and Rakibul Islam, “Detecting and Preventing Money Laundering Using Deep Learning and Graph Analysis” International Journal of Advanced Computer Science and Applications(IJACSA), 16(6), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160601

@article{Raja2025,
title = {Detecting and Preventing Money Laundering Using Deep Learning and Graph Analysis},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160601},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160601},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {6},
author = {Mamunur R Raja and Md Anwar Hosen and Md Farhad Kabir and Sharmin Sultana and Shah Ahammadullah Ashraf and Rakibul Islam}
}