Publication Type

Journal Article

Version

acceptedVersion

Publication Date

3-2026

Abstract

Money laundering is a financial crime that obscures the origin of illicit funds, necessitating the development and enforcement of anti-money laundering (AML) policies by governments and organizations. The proliferation of mobile payment platforms and smart IoT devices has significantly complicated AML investigations. As payment networks become more interconnected, there is an increasing need for efficient real-time detection to process large volumes of transaction data on heterogeneous payment systems by different operators such as digital currencies, cryptocurrencies, and account-based payments. Most of these mobile payment networks are supported by connected devices, many of which are considered loT devices in the FinTech space that constantly generate data. Furthermore, the growing complexity and unpredictability of transaction patterns across these networks contribute to a higher incidence of false positives. While machine learning (ML) solutions have the potential to enhance detection efficiency, their application in AML faces unique challenges, such as addressing privacy concerns tied to sensitive financial data and managing the real-world constraint of limited data availability due to data regulations. Existing surveys in the AML literature broadly review ML approaches for money laundering detection, but they often lack an in-depth exploration of advanced deep learning techniques-an emerging field with significant potential. To address this gap, this article conducts a comprehensive review of deep learning solutions and the challenges associated with their use in AML. In addition, we propose a novel framework that applies the least-privilege principle by integrating ML techniques, codifying AML red flags, and employing account profiling to provide context for predictions and enable effective fraud detection under limited data availability. Specifically, our approach defines AML-relevant financial profile characteristics and risk indicators to contextualize transactions and assess their associated risks. The proposed context-risk-predict AML (CRP-AML) model demonstrates notable success, achieving an ${F}1$ score of 82.51% on the minority class and nearly doubling the performance of other pattern detection models when the proportion of money laundering records in the dataset drops as low as 0.0005.

Keywords

Automated machine learning, Deep learning, Reviews, Machine learning, Surveys, Regulation, Data privacy, Online banking, Internet of Things, General Data Protection Regulation, Account profiling, anti-money laundering (AML), deep learning, financial crime, privacy

Discipline

Artificial Intelligence and Robotics | Finance and Financial Management | Information Security

Research Areas

Data Science and Engineering

Publication

IEEE Internet of Things Journal

Volume

13

Issue

6

First Page

10407

Last Page

10431

ISSN

2327-4662

Identifier

10.1109/JIOT.2026.3653434

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/JIOT.2026.3653434

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