Counterfactual Methods for Detecting Unfairness in Anti-Money Laundering Algorithms

• URL: http://arxiv.org/abs/2607.05101v1
• Date: Mon, 06 Jul 2026 13:57:21 GMT
• Title: Counterfactual Methods for Detecting Unfairness in Anti-Money Laundering Algorithms
• Abstract summary: The application of machine learning-based predictive algorithms to Anti-Money Laundering has grown rapidly.<n>These algorithms are typically trained on transactional data and on sensitive client information.<n>Despite this, AML detection systems remain largely underexplored from a fairness perspective.
• Score: 37.131058866458964
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The application of machine learning-based predictive algorithms to Anti-Money Laundering (AML) has grown rapidly, driven by the vast volume of financial transaction data available to banks. These algorithms are typically trained not only on transactional data but also on sensitive client information, which may raise fairness concerns. Despite this, AML detection systems remain largely underexplored from a fairness perspective, even though deeper analytical methods based on counterfactuals are now available. Such techniques enable the decomposition of the direct and indirect effects of potentially sensitive features on model predictions, thereby supporting the evaluation of whether their influence is acceptable from a fairness perspective. Closing this gap, we consider the synthetic IBM AMLSim transaction dataset and construct additional features of the country of an account and its average behaviour. This improves the predictive performance of diverse machine learning models, ranging from baseline decision trees to state-of-the-art graph neural networks. We assess the potential unfairness associated with these features through a counterfactual, path-specific effect analysis. This reveals that fairness violations tend to be more pronounced for models whose predictive performance benefits the most from the extended features. Such a finding highlights a concrete instance of the trade-off between predictive accuracy and fairness in AML applications, thus underscoring the urgency of a systematic fairness analysis in such critical domains.

Related papers

• Fraud Detection System for Banking Transactions [0.0]
  This research introduces a machine learning-based fraud detection framework utilizing the PaySim synthetic financial transaction dataset.<n>The proposed framework provides a robust and scalable solution to enhance fraud prevention capabilities in FinTech transaction systems.
  arXiv  Detail & Related papers  (2026-04-09T08:17:27Z)
• Improving Credit Card Fraud Detection with an Optimized Explainable Boosting Machine [0.0]
  The study proposes an enhanced workflow based on the Explainable Boosting Machine (EBM)<n>The optimized EBM achieves an effective balance between accuracy and interpretability, enabling precise detection of fraudulent transactions.<n> Experimental evaluation on benchmark credit card data yields an ROC-AUC of 0.983, surpassing prior EBM baselines.
  arXiv  Detail & Related papers  (2026-02-06T18:56:17Z)
• Evaluating Supervised Learning Models for Fraud Detection: A Comparative Study of Classical and Deep Architectures on Imbalanced Transaction Data [1.9426782472131299]
  Fraud detection remains a critical task in high-stakes domains such as finance and e-commerce.<n>We systematically compare the performance of four supervised learning models on a large-scale, highly imbalanced online transaction dataset.
  arXiv  Detail & Related papers  (2025-05-28T16:08:04Z)
• Deep Learning Approaches for Anti-Money Laundering on Mobile Transactions: Review, Framework, and Directions [51.43521977132062]
  Money laundering is a financial crime that obscures the origin of illicit funds.<n>The proliferation of mobile payment platforms and smart IoT devices has significantly complicated anti-money laundering investigations.<n>This paper conducts a comprehensive review of deep learning solutions and the challenges associated with their use in AML.
  arXiv  Detail & Related papers  (2025-03-13T05:19:44Z)
• Advancing Anomaly Detection: Non-Semantic Financial Data Encoding with LLMs [49.57641083688934]
  We introduce a novel approach to anomaly detection in financial data using Large Language Models (LLMs) embeddings. Our experiments demonstrate that LLMs contribute valuable information to anomaly detection as our models outperform the baselines.
  arXiv  Detail & Related papers  (2024-06-05T20:19:09Z)
• On the Potential of Network-Based Features for Fraud Detection [3.0846824529023382]
  This article explores using the personalised PageRank (PPR) algorithm to capture the social dynamics of fraud. The primary objective is to compare the performance of traditional features with the addition of PPR in fraud detection models. Results indicate that integrating PPR enhances the model's predictive power, surpassing the baseline model.
  arXiv  Detail & Related papers  (2024-02-14T13:20:09Z)
• Learning for Counterfactual Fairness from Observational Data [62.43249746968616]
  Fairness-aware machine learning aims to eliminate biases of learning models against certain subgroups described by certain protected (sensitive) attributes such as race, gender, and age. A prerequisite for existing methods to achieve counterfactual fairness is the prior human knowledge of the causal model for the data. In this work, we address the problem of counterfactually fair prediction from observational data without given causal models by proposing a novel framework CLAIRE.
  arXiv  Detail & Related papers  (2023-07-17T04:08:29Z)
• Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [48.87381259980254]
  We document the capability of large language models (LLMs) like ChatGPT to predict stock market reactions from news headlines without direct financial training.<n>Using post-knowledge-cutoff headlines, GPT-4 captures initial market responses, achieving approximately 90% portfolio-day hit rates for the non-tradable initial reaction.
  arXiv  Detail & Related papers  (2023-04-15T19:22:37Z)
• LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering [5.478764356647437]
  LaundroGraph is a novel self-supervised graph representation learning approach. It provides insights to assist the anti-money laundering reviewing process. To the best of our knowledge, this is the first fully self-supervised system within the context of AML detection.
  arXiv  Detail & Related papers  (2022-10-25T21:58:02Z)
• Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets [84.90242084523565]
  Traditional time-series econometric methods often appear incapable of capturing the true complexity of the multi-level interactions driving the price dynamics. By adopting a state-of-the-art second-order optimization algorithm, we train a Bayesian bilinear neural network with temporal attention. By addressing the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecasts, we thoroughly compare our Bayesian model with traditional ML alternatives.
  arXiv  Detail & Related papers  (2022-03-07T18:59:54Z)
• Accurate and Robust Feature Importance Estimation under Distribution Shifts [49.58991359544005]
  PRoFILE is a novel feature importance estimation method. We show significant improvements over state-of-the-art approaches, both in terms of fidelity and robustness.
  arXiv  Detail & Related papers  (2020-09-30T05:29:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.