mlsanityData to 7 Oct 2026

Graph Computing for Financial Crime and Fraud Detection: Trends, Challenges and Outlook

arXiv 2103.03227 ↗First posted 2 Mar 2021cs.CR cs.AI cs.CYcited by 8

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Abstract (arXiv)

The rise of digital payments has caused consequential changes in the financial crime landscape. As a result, traditional fraud detection approaches such as rule-based systems have largely become ineffective. AI and machine learning solutions using graph computing principles have gained significant interest in recent years. Graph-based techniques provide unique solution opportunities for financial crime detection. However, implementing such solutions at industrial-scale in real-time financial transaction processing systems has brought numerous application challenges to light. In this paper, we discuss the implementation difficulties current and next-generation graph solutions face. Furthermore, financial crime and digital payments trends indicate emerging challenges in the continued effectiveness of the detection techniques. We analyze the threat landscape and argue that it provides key insights for developing graph-based solutions.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 36 of its 127 references that are in the map. First posted on arXiv 2 Mar 2021.

Builds on

36 of 127 references.

References · 36

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24 more
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Cited by year

Citing papers per year: 2021: 2; 2022: 0; 2023: 1; 2024: 4; 2025: 1.

8 papers.

  1. 2021A Review on Graph Neural Network Methods in Financial Applicationscited by 77
  2. 2024Graph Neural Networks for Financial Fraud Detection: A Reviewcited by 24
  3. 2021Towards Representation Identical Privacy-Preserving Graph Neural Network via Split Learningcited by 10
  4. 2023A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detectioncited by 10
  5. 2024Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental Evaluationcited by 7
  6. 2024Privately Learning from Graphs with Applications in Fine-tuning Large Language Modelscited by 1
  7. 2024Privacy-Preserving Graph-Based Machine Learning with Fully Homomorphic Encryption for Collaborative Anti-Money Launderingcited by 1
  8. 2025Advances in Continual Graph Learning for Anti-Money Laundering Systems: A Comprehensive Reviewcited by 0