Explainable Fraud Detection with GNNExplainer and Shapley Values

2025/09/12 by Ngoc Hieu Dao, Dao, Ngoc Hieu
Computer Science · Mathematics · #Imbalanced Data Classification Techniques #Explainable Artificial Intelligence (XAI) #Benford’s Law and Fraud Detection

paper · pdf · doi:10.48550/arxiv.2509.12262

Abstract

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.

Citations

• Modelling graph dynamics in fraud detection with "Attention"
• Friend or Faux: Graph-Based Early Detection of Fake Accounts on Social\n Networks
• Heterogeneous Graph Transformer
• Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation
• GNNExplainer: Generating Explanations for Graph Neural Networks
• Heterogeneous Graph Neural Networks for Malicious Account Detection
• Graph Attention Networks
• Attention Is All You Need
• A Unified Approach to Interpreting Model Predictions

Related

• Transparency and Privacy: The Role of Explainable AI and Federated Learning in Financial Fraud Detection
• Financial Fraud Detection Using Explainable AI and Stacking Ensemble Methods
• Providing an Intelligent Model to Detect Fraud in Financial Statements
• On some studies of Fraud Detection Pipeline and related issues from the scope of Ensemble Learning and Graph-based Learning
• Explainable AI for Fraud Detection: An Attention-Based Ensemble of CNNs, GNNs, and A Confidence-Driven Gating Mechanism