# Explainable Fraud Detection with GNNExplainer and Shapley Values

arXiv: https://arxiv.org/abs/2509.12262 · HTML: https://mlsanity.com/paper/2509.12262/

## 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.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [GNNExplainer: Generating Explanations for Graph Neural Networks](https://mlsanity.com/paper/1903.03894/) (2019)
- [Heterogeneous Graph Transformer](https://mlsanity.com/paper/2003.01332/) (2020)
- [Heterogeneous Graph Neural Networks for Malicious Account Detection](https://mlsanity.com/paper/2002.12307/) (2020)
- [Spam Review Detection with Graph Convolutional Networks](https://mlsanity.com/paper/1908.10679/) (2019)
- [Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation](https://mlsanity.com/paper/1903.09196/) (2019)
- [Friend or Faux: Graph-Based Early Detection of Fake Accounts on Social Networks](https://mlsanity.com/paper/2004.04834/) (2020)
- [Uncovering Insurance Fraud Conspiracy with Network Learning](https://mlsanity.com/paper/2002.12789/) (2020)
- [Modelling graph dynamics in fraud detection with "Attention"](https://mlsanity.com/paper/2204.10614/) (2022)

## 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.