Deep Learning Approaches for Anti-Money Laundering on Mobile Transactions: Review, Framework, and Directions
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Abstract (arXiv)
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....
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 39 of its 141 references that are in the map. First posted on arXiv 13 Mar 2025.
Builds on
39 of 141 references.
References · 39
- 2017Graph Attention Networkscited by 8,923
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
- 2017Inductive Representation Learning on Large Graphscited by 6,320
- 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
- 2018How Powerful are Graph Neural Networks?cited by 4,393
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
- 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,694
- 2018Relational inductive biases, deep learning, and graph networkscited by 1,360
- 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 993
- 2019Strategies for Pre-training Graph Neural Networkscited by 971
- 2020A Survey on Visual Transformercited by 654
- 2018Explaining Explanations: An Overview of Interpretability of Machine Learningcited by 508
27 more
- 2019EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphscited by 447
- 2020MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embeddingcited by 304
- 2021Identity-aware Graph Neural Networkscited by 198
- 2019Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensicscited by 164
- 2022ROLAND: Graph Learning Framework for Dynamic Graphscited by 132
- 2017Topology Adaptive Graph Convolutional Networkscited by 113
- 2020Deep Learning for Financial Applications : A Surveycited by 91
- 2019Approximation Ratios of Graph Neural Networks for Combinatorial Problemscited by 69
- 2023Realistic Synthetic Financial Transactions for Anti-Money Laundering Modelscited by 50
- 2024Generative AI Agents with Large Language Model for Satellite Networks via a Mixture of Experts Transmissioncited by 35
- 2024Interactive AI with Retrieval-Augmented Generation for Next Generation Networkingcited by 34
- 2022Privacy-Preserving Aggregation in Federated Learning: A Surveycited by 27
- 2023Provably Powerful Graph Neural Networks for Directed Multigraphscited by 27
- 2023Generative AI-enabled Vehicular Networks: Fundamentals, Framework, and Case Studycited by 24
- 2020Detecting Mixing Services via Mining Bitcoin Transaction Network with Hybrid Motifscited by 23
- 2021Reinforcement Learning for IoT Security: A Comprehensive Surveycited by 23
- 2022LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Launderingcited by 20
- 2019edGNN: a Simple and Powerful GNN for Directed Labeled Graphscited by 18
- 2021GuiltyWalker: Distance to illicit nodes in the Bitcoin networkcited by 13
- 2022Inspection-L: Self-Supervised GNN Node Embeddings for Money Laundering Detection in Bitcoincited by 13
- 2023Transparency and Privacy: The Role of Explainable AI and Federated Learning in Financial Fraud Detectioncited by 13
- 2023Generative AI for Space-Air-Ground Integrated Networkscited by 9
- 2024Deep Learning for Cross-Border Transaction Anomaly Detection in Anti-Money Laundering Systemscited by 9
- 2022Fighting Money Laundering with Statistics and Machine Learningcited by 5
- 2021Application of Deep Reinforcement Learning to Payment Fraudcited by 3
- 2022Understanding Security in Smart City Domains From the ANT-centric Perspectivecited by 2
- 2024Decentralized Multimedia Data Sharing in IoV: A Learning-based Equilibrium of Supply and Demandcited by 2
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