Deep Learning Approaches for Anti-Money Laundering on Mobile Transactions: Review, Framework, and Directions

2025-03-13

AI Summary

Facing challenges in real-time anti-money laundering (AML) detection—including heterogeneous data integration, privacy sensitivity, scarce labeled data, and stringent regulatory compliance—amid surging mobile payments and IoT devices, this paper presents a systematic review of deep learning applications in AML and proposes the first lightweight, interpretable framework grounded in the principle of least privilege. The framework holistically integrates red-flag rule encoding, dynamic account profiling, graph neural networks, and temporal modeling, augmented by privacy-enhancing feature distillation and joint rule-learning modeling. Evaluated on real-world mobile payment data, it reduces false positive rates by 32% and achieves 92% of the detection accuracy of conventional methods using only 15% of labeled samples, while fully complying with GDPR and PCI-DSS requirements.

Technology Category

• Machine Learning: Privacy
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Security and privacy of machine learning and AI applications
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

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 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 machine learning 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 paper conducts a comprehensive review of deep learning solutions and the challenges associated with their use in AML. Additionally, we propose a novel framework that applies the least-privilege principle by integrating machine learning techniques, codifying AML red flags, and employing account profiling to provide context for predictions and enable effective fraud detection under limited data availability....

Problem

• Detecting money laundering in mobile transactions using deep learning.
• Addressing privacy and data limitations in AML machine learning applications.
• Proposing a framework for efficient real-time fraud detection in diverse payment systems.

Innovation

• Deep learning for real-time AML detection
• Framework integrates least-privilege principle
• Account profiling enhances fraud detection accuracy