Fatemeh, Hosseini and Leonardo, Costa and Emily, Carter (2024) AI/ML-Powered Anti-Money Laundering Pipelines: Architecting Real-Time Risk Detection Systems Using Hadoop, PySpark, and Distributed Graph-Based Algorithms. American Journal of Technology Advancement, 1 (7). pp. 75-91. ISSN 2997-9382

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Abstract

The exponential growth of financial transactions in the global Banking, Financial Services, and Insurance (BFSI) sector has intensified the challenge of detecting money laundering, which accounts for an estimated 2–5% of global GDP annually (≈ USD 800 billion – 2 trillion) according to the United Nations Office on Drugs and Crime (UNODC). Traditional rule-based Anti-Money Laundering (AML) systems suffer from high false positive rates—often exceeding 95%—and limited scalability when confronted with big data transaction streams. To address these limitations, this paper proposes an AI/ML-powered real-time AML pipeline designed on distributed architectures leveraging Hadoop, PySpark, and graph-based algorithms for suspicious activity detection.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Postgraduate > Master's of Islamic Education
Depositing User:	Journal Editor
Date Deposited:	18 Sep 2025 07:18
Last Modified:	18 Sep 2025 07:18
URI:	http://eprints.umsida.ac.id/id/eprint/16354

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