# SEFraud: Graph-based Self-Explainable Fraud Detection via Interpretative Mask Learning

arXiv: https://arxiv.org/abs/2406.11389 · HTML: https://mlsanity.com/paper/2406.11389/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 44 references that are in the map.
- First posted on arXiv 17 Jun 2024.

## Builds on (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [GNNExplainer: Generating Explanations for Graph Neural Networks](https://mlsanity.com/paper/1903.03894/) (2019)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [Heterogeneous Graph Transformer](https://mlsanity.com/paper/2003.01332/) (2020)
- [Parameterized Explainer for Graph Neural Network](https://mlsanity.com/paper/2011.04573/) (2020)
- [MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding](https://mlsanity.com/paper/2002.01680/) (2020)
- [Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters](https://mlsanity.com/paper/2008.08692/) (2020)
- [A Comprehensive Survey on Graph Anomaly Detection with Deep Learning](https://mlsanity.com/paper/2106.07178/) (2021)
- [XGNN: Towards Model-Level Explanations of Graph Neural Networks](https://mlsanity.com/paper/2006.02587/) (2020)
- [A Semi-supervised Graph Attentive Network for Financial Fraud Detection](https://mlsanity.com/paper/2003.01171/) (2020)
- [Heterogeneous Graph Neural Networks for Malicious Account Detection](https://mlsanity.com/paper/2002.12307/) (2020)
- [Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection](https://mlsanity.com/paper/2005.00625/) (2020)
- [Simple and Efficient Heterogeneous Graph Neural Network](https://mlsanity.com/paper/2207.02547/) (2022)
- [Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Space](https://mlsanity.com/paper/2107.03767/) (2021)
- [GeniePath: Graph Neural Networks with Adaptive Receptive Paths](https://mlsanity.com/paper/1802.00910/) (2018)
- [GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection](https://mlsanity.com/paper/2005.10150/) (2020)
- [HRCF: Enhancing Collaborative Filtering via Hyperbolic Geometric Regularization](https://mlsanity.com/paper/2204.08176/) (2022)
- [Graph Pointer Neural Networks](https://mlsanity.com/paper/2110.00973/) (2021)
- [Friend or Faux: Graph-Based Early Detection of Fake Accounts on Social Networks](https://mlsanity.com/paper/2004.04834/) (2020)
- [MATA*: Combining Learnable Node Matching with A* Algorithm for Approximate Graph Edit Distance Computation](https://mlsanity.com/paper/2311.02356/) (2023)
- [Smoke Screener or Straight Shooter: Detecting Elite Sybil Attacks in User-Review Social Networks](https://mlsanity.com/paper/1709.06916/) (2017)
- [Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active Learning](https://mlsanity.com/paper/2308.08823/) (2023)

## Built on by (most cited first, 12 of 12)

- [Dual-channel Heterophilic Message Passing for Graph Fraud Detection](https://mlsanity.com/paper/2504.14205/) (2025)
- [Understanding Structured Financial Data with LLMs: A Case Study on Fraud Detection](https://mlsanity.com/paper/2512.13040/) (2025)
- [Foundations and Frontiers of Graph Learning Theory](https://mlsanity.com/paper/2407.03125/) (2024)
- [Addressing Noise and Stochasticity in Fraud Detection for Service Networks](https://mlsanity.com/paper/2505.00946/) (2025)
- [OpenFinGym: A Verifiable Multi-Task Gym Environment for Evaluating Quant Agents](https://mlsanity.com/paper/2606.26350/) (2026)
- [Harnessing Mixed Features for Imbalance Data Oversampling: Application to Bank Customers Scoring](https://mlsanity.com/paper/2503.22730/) (2025)
- [Reinforcement Learning of Large Language Models for Interpretable Credit Card Fraud Detection](https://mlsanity.com/paper/2601.05578/) (2026)
- [SLogic: Subgraph-Informed Logical Rule Learning for Knowledge Graph Completion](https://mlsanity.com/paper/2510.00279/) (2025)
- [CleverCatch: A Knowledge-Guided Weak Supervision Model for Fraud Detection](https://mlsanity.com/paper/2510.13205/) (2025)
- [Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation](https://mlsanity.com/paper/2607.19266/) (2026)
- [Temporal Graph Prototype-conditioned Conformal Prediction for Fraud Detection](https://mlsanity.com/paper/2608.15768/) (2026)
- [From Prediction to Explainable Provider Behavior Profiles for Fraud, Waste, and Abuse Review](https://mlsanity.com/paper/2609.28477/) (2026)

## Abstract (arXiv)

Graph-based fraud detection has widespread application in modern industry scenarios, such as spam review and malicious account detection. While considerable efforts have been devoted to designing adequate fraud detectors, the interpretability of their results has often been overlooked. Previous works have attempted to generate explanations for specific instances using post-hoc explaining methods such as a GNNExplainer. However, post-hoc explanations can not facilitate the model predictions and the computational cost of these methods cannot meet practical requirements, thus limiting their application in real-world scenarios. To address these issues, we propose SEFraud, a novel graph-based self-explainable fraud detection framework that simultaneously tackles fraud detection and result in interpretability. Concretely, SEFraud first leverages customized heterogeneous graph transformer networks with learnable feature masks and edge masks to learn expressive representations from the informative heterogeneously typed transactions. A new triplet loss is further designed to enhance the performance of mask learning. Empirical results on various datasets demonstrate the effectiveness of SEFraud as it shows considerable advantages in both the fraud detection performance and interpretability of prediction results. Moreover, SEFraud has been deployed and offers explainable fraud detection service for the largest bank in China, Industrial and Commercial Bank of China Limited (ICBC). Results collected from the production environment of ICBC show that SEFraud can provide accurate detection results and comprehensive explanations that align with the expert business understanding, confirming its efficiency and applicability in large-scale online services.