Explainable Fraud Detection with GNNExplainer and Shapley Values
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Abstract (arXiv)
The risk of financial fraud is increasing as digital payments are used more and more frequently. Although the use of artificial intelligence systems for fraud detection is widespread, society and regulators have raised the standards for these systems' transparency for reliability verification purposes. To increase their effectiveness in conducting fraud investigations, fraud analysts also profit from having concise and understandable explanations. To solve these challenges, the paper will concentrate on developing an explainable fraud detector.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 25 references that are in the map. First posted on arXiv 12 Sep 2025.
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11 of 25 references.
References · 11
- 2017Attention Is All You Needcited by 60,361
- 2017Graph Attention Networkscited by 8,923
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
- 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 737
- 2020Heterogeneous Graph Transformercited by 582
- 2020Heterogeneous Graph Neural Networks for Malicious Account Detectioncited by 96
- 2019Spam Review Detection with Graph Convolutional Networkscited by 57
- 2019Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitationcited by 32
- 2020Friend or Faux: Graph-Based Early Detection of Fake Accounts on Social Networkscited by 19
- 2020Uncovering Insurance Fraud Conspiracy with Network Learningcited by 9
- 2022Modelling graph dynamics in fraud detection with "Attention"cited by 5
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