mlsanityData to 14 Oct 2026

Graph Neural Networks for Financial Fraud Detection: A Review

arXiv 2411.05815 ↗First posted 1 Nov 2024q-fin.ST cs.LGcited by 25

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Abstract (arXiv)

The landscape of financial transactions has grown increasingly complex due to the expansion of global economic integration and advancements in information technology. This complexity poses greater challenges in detecting and managing financial fraud. This review explores the role of Graph Neural Networks (GNNs) in addressing these challenges by proposing a unified framework that categorizes existing GNN methodologies applied to financial fraud detection. Specifically, by examining a series of detailed research questions, this review delves into the suitability of GNNs for financial fraud detection, their deployment in real-world scenarios, and the design considerations that enhance their effectiveness. This review reveals that GNNs are exceptionally adept at capturing complex relational patterns and dynamics within financial networks, significantly outperforming traditional fraud detection methods. Unlike previous surveys that often overlook the specific potentials of GNNs or address them only superficially, our review provides a comprehensive, structured analysis, distinctly focusing on the multifaceted applications and deployments of GNNs in financial fraud detection. This review not only highlights the potential of GNNs to improve fraud detection mechanisms but also identifies current gaps and outlines future research directions to enhance their deployment in financial systems. Through a structured review of over 100 studies, this review paper contributes to the understanding of GNN applications in financial fraud detection, offering insights into their adaptability and potential integration strategies.

Key facts. Cited by 25 papers in the map (data through 14 Oct 2026). Builds on 49 of its 152 references that are in the map. First posted on arXiv 1 Nov 2024.

Builds on

49 of 152 references.

References · 49

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  3. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
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37 more
  1. 2016Learning Convolutional Neural Networks for Graphscited by 314
  2. 2019Representation Learning for Dynamic Graphs: A Surveycited by 265
  3. 2020Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsterscited by 250
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  5. 2020GNNGuard: Defending Graph Neural Networks against Adversarial Attackscited by 195
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  9. 2019Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensicscited by 164
  10. 2021GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networkscited by 147
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  14. 2021Towards Self-Explainable Graph Neural Networkcited by 75
  15. 2022Hierarchical Graph Transformer with Adaptive Node Samplingcited by 72
  16. 2020Certified Robustness of Graph Neural Networks against Adversarial Structural Perturbationcited by 57
  17. 2023Realistic Synthetic Financial Transactions for Anti-Money Laundering Modelscited by 50
  18. 2023Financial Fraud Detection using Quantum Graph Neural Networkscited by 35
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 8; 2026: 17.

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  1. 2025Grad: Guided Relation Diffusion Generation for Graph Augmentation in Graph Fraud Detectioncited by 4
  2. 2025AuditVotes: Elevating Provable Defense for GNNs with Efficient Augmentation and Conditional Smoothingcited by 2
  3. 2025GraphUniverse: Synthetic Graph Generation for Evaluating Inductive Generalizationcited by 2
  4. 2026Deja Vu in Plots: Leveraging Cross-Session Evidence with Retrieval-Augmented LLMs for Live Streaming Risk Assessmentcited by 2
  5. 2026Live or Lie: Action-Aware Capsule Multiple Instance Learning for Risk Assessment in Live Streaming Platformscited by 2
  6. 2026Explainable Graph Neural Networks for Interbank Contagion Surveillance: A Regulatory-Aligned Framework for the U.S. Banking Sectorcited by 1
  7. 2026Outsmarting the Chameleon: Counterfactual Decoupling for Tactical OOD Shifts in Live Streaming Risk Assessmentcited by 1
  8. 2026SAGE: An LLM-driven Self Reflective Agentic Framework for Fraud Detectioncited by 1
  9. 2025Towards Anomaly-Aware Pre-Training and Fine-Tuning for Graph Anomaly Detectioncited by 0
  10. 2025Generative Dynamic Graph Representation Learning for Conspiracy Spoofing Detectioncited by 0
  11. 2025Explainable Heterogeneous Anomaly Detection in Financial Networks via Adaptive Expert Routingcited by 0
  12. 2025FGC-Comp: Adaptive Neighbor-Grouped Attribute Completion for Graph-based Anomaly Detectioncited by 0
13 more
  1. 2025Quantum Topological Graph Neural Networks for Detecting Complex Fraud Patternscited by 0
  2. 2026Knowledge-Integrated Representation Learning for Crypto Anomaly Detection under Extreme Label Scarcity; Relational Domain-Logic Integration with Retrieval-Grounded Context and Path-Level Explanationscited by 0
  3. 2026BadImplant: Injection-based Multi-Targeted Graph Backdoor Attackcited by 0
  4. 2026Graph-Based Fraud Detection with Dual-Path Graph Filteringcited by 0
  5. 2026GRAPHLCP: Structure-Aware Localized Conformal Prediction on Graphscited by 0
  6. 2026TSAI-MetaFraud: A Benchmark Dataset for Financial Fraud Transaction and Behavioral Risk Detection in Metaverse Ecosystemscited by 0
  7. 2026Quantum-Inspired Contextual Learning for Sparse-Ring Fraud Detection in Dynamic Transaction Graphscited by 0
  8. 2026SAGA: Synthetic Agentic Graph Architecture for Temporal Benchmark Generationcited by 0
  9. 2026Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigationcited by 0
  10. 2026Empowering Credit Risk Detection in Weixin Pay with Billion-Scale Deep Graph Learningcited by 0
  11. 2026Temporal Graph Prototype-conditioned Conformal Prediction for Fraud Detectioncited by 0
  12. 2026Behavior-Grounded Semantic Enrichment for Financial Fraud Modeling and Reasoningcited by 0
  13. 2026FRACTION: Fraud Explanation Generation via Agentic Graph Context Navigationcited by 0