mlsanityData to 14 Oct 2026

Fighting Money Laundering with Statistics and Machine Learning

arXiv 2201.04207 ↗First posted 11 Jan 2022stat.ML cs.LGcited by 5

Not on the map. Why?

Abstract (arXiv)

Money laundering is a profound global problem. Nonetheless, there is little scientific literature on statistical and machine learning methods for anti-money laundering. In this paper, we focus on anti-money laundering in banks and provide an introduction and review of the literature. We propose a unifying terminology with two central elements: (i) client risk profiling and (ii) suspicious behavior flagging. We find that client risk profiling is characterized by diagnostics, i.e., efforts to find and explain risk factors. On the other hand, suspicious behavior flagging is characterized by non-disclosed features and hand-crafted risk indices. Finally, we discuss directions for future research. One major challenge is the need for more public data sets. This may potentially be addressed by synthetic data generation. Other possible research directions include semi-supervised and deep learning, interpretability, and fairness of the results.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 61 of its 183 references that are in the map. First posted on arXiv 11 Jan 2022.

Builds on

61 of 183 references.

References · 61

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  4. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  5. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  6. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  7. 2016Densely Connected Convolutional Networkscited by 6,720
  8. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  9. 2016Layer Normalizationcited by 5,669
  10. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  11. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  12. 2017Squeeze-and-Excitation Networkscited by 4,789
38 more
  1. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,548
  2. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  3. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,611
  4. 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,371
  5. 2019RandAugment: Practical automated data augmentation with a reduced search spacecited by 2,269
  6. 2014Conditional Generative Adversarial Netscited by 2,162
  7. 2014Unsupervised Domain Adaptation by Backpropagationcited by 1,917
  8. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
  9. 2014How transferable are features in deep neural networks?cited by 1,225
  10. 2015Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)cited by 1,070
  11. 2015Effective Approaches to Attention-based Neural Machine Translationcited by 1,026
  12. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  13. 2017Self-Normalizing Neural Networkscited by 598
  14. 2015Empirical Evaluation of Rectified Activations in Convolutional Networkcited by 519
  15. 2019EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphscited by 447
  16. 2018Techniques for Interpretable Machine Learningcited by 238
  17. 2020Time Series Data Augmentation for Deep Learning: A Surveycited by 226
  18. 2015The Variational Fair Autoencodercited by 223
  19. 2016Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Modelscited by 213
  20. 2015Simultaneous Deep Transfer Across Domains and Taskscited by 206
  21. 2016Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learningcited by 195
  22. 2014Recurrent Neural Network Regularizationcited by 189
  23. 2019Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensicscited by 164
  24. 2014Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognitioncited by 111
  25. 2019Explainable AI for Trees: From Local Explanations to Global Understandingcited by 65
  26. 2017Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysiscited by 46
  27. 2020DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Modelscited by 38
  28. 2020ViCE: Visual Counterfactual Explanations for Machine Learning Modelscited by 34
  29. 2018Scalable Graph Learning for Anti-Money Laundering: A First Lookcited by 33
  30. 2021ECINN: Efficient Counterfactuals from Invertible Neural Networkscited by 26
  31. 2020Time Series Data Augmentation for Neural Networks by Time Warping with a Discriminative Teachercited by 22
  32. 2020Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcitycited by 21
  33. 2021Generative adversarial networks in time series: A survey and taxonomycited by 21
  34. 2021Using social network analysis to prevent money launderingcited by 14
  35. 2021GuiltyWalker: Distance to illicit nodes in the Bitcoin networkcited by 13
  36. 2016Detection of money laundering groups using supervised learning in networkscited by 9
  37. 2013Dynamic Network Models for Forecastingcited by 8
  38. 2020Supervised Domain Adaptation: A Graph Embedding Perspective and a Rectified Experimental Protocolcited by 8

50 most cited of 61 All 61 (JSON)

Built on by

Cited by year

Citing papers per year: 2024: 2; 2025: 3.

5 papers.

  1. 2025Engineering the Law-Machine Learning Translation Problem: Developing Legally Aligned Modelscited by 3
  2. 2024Leveraging Ensemble-Based Semi-Supervised Learning for Illicit Account Detection in Ethereum DeFi Transactionscited by 1
  3. 2025Deep Learning Approaches for Anti-Money Laundering on Mobile Transactions: Review, Framework, and Directionscited by 1
  4. 2025Advances in Continual Graph Learning for Anti-Money Laundering Systems: A Comprehensive Reviewcited by 1
  5. 2024Combating Financial Crimes with Unsupervised Learning Techniques: Clustering and Dimensionality Reduction for Anti-Money Launderingcited by 0