mlsanityData to 14 Oct 2026

Explainable Machine Learning for Fraud Detection

arXiv 2105.06314 ↗First posted 13 May 2021cs.LG cs.AIcited by 9

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Abstract (arXiv)

The application of machine learning to support the processing of large datasets holds promise in many industries, including financial services. However, practical issues for the full adoption of machine learning remain with the focus being on understanding and being able to explain the decisions and predictions made by complex models. In this paper, we explore explainability methods in the domain of real-time fraud detection by investigating the selection of appropriate background datasets and runtime trade-offs on both supervised and unsupervised models.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 7 of its 20 references that are in the map. First posted on arXiv 13 May 2021.

Builds on

7 of 20 references.

References · 7

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  3. 2019GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detectioncited by 36
  4. 2019Explaining Anomalies Detected by Autoencoders Using SHAPcited by 27
  5. 2017Contextual Outlier Interpretationcited by 19
  6. 2019Shapley Values of Reconstruction Errors of PCA for Explaining Anomaly Detectioncited by 19
  7. 2018Instance-Level Explanations for Fraud Detection: A Case Studycited by 10

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Cited by year

Citing papers per year: 2022: 5; 2023: 2; 2024: 1; 2025: 1.

9 papers.

  1. 2023TempME: Towards the Explainability of Temporal Graph Neural Networks via Motif Discoverycited by 27
  2. 2022Foiling Explanations in Deep Neural Networkscited by 14
  3. 2025Explainability in Practice: A Survey of Explainable NLP Across Various Domainscited by 11
  4. 2022RESHAPE: Explaining Accounting Anomalies in Financial Statement Audits by enhancing SHapley Additive exPlanationscited by 2
  5. 2024Error Analysis of Shapley Value-Based Model Explanations: An Informative Perspectivecited by 1
  6. 2022Evaluating Local Model-Agnostic Explanations of Learning to Rank Models with Decision Pathscited by 0
  7. 2022ExMo: Explainable AI Model using Inverse Frequency Decision Rulescited by 0
  8. 2022Explanation Method for Anomaly Detection on Mixed Numerical and Categorical Spacescited by 0
  9. 2023IsoEx: an explainable unsupervised approach to process event logs cyber investigationcited by 0