mlsanityData to 7 Oct 2026

Towards Federated Graph Learning for Collaborative Financial Crimes Detection

arXiv 1909.12946 ↗First posted 19 Sep 2019cs.CY cs.CR cs.LGcited by 33

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Abstract (arXiv)

Financial crime is a large and growing problem, in some way touching almost every financial institution. Financial institutions are the front line in the war against financial crime and accordingly, must devote substantial human and technology resources to this effort. Current processes to detect financial misconduct have limitations in their ability to effectively differentiate between malicious behavior and ordinary financial activity. These limitations tend to result in gross over-reporting of suspicious activity that necessitate time-intensive and costly manual review. Advances in technology used in this domain, including machine learning based approaches, can improve upon the effectiveness of financial institutions' existing processes, however, a key challenge that most financial institutions continue to face is that they address financial crimes in isolation without any insight from other firms. Where financial institutions address financial crimes through the lens of their own firm, perpetrators may devise sophisticated strategies that may span across institutions and geographies. Financial institutions continue to work relentlessly to advance their capabilities, forming partnerships across institutions to share insights, patterns and capabilities. These public-private partnerships are subject to stringent regulatory and data privacy requirements, thereby making it difficult to rely on traditional technology solutions. In this paper, we propose a methodology to share key information across institutions by using a federated graph learning platform that enables us to build more accurate machine learning models by leveraging federated learning and also graph learning approaches. We demonstrated that our federated model outperforms local model by 20% with the UK FCA TechSprint data set. This new platform opens up a door to efficiently detecting global money laundering activity.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Sep 2019.

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Citing papers per year: 2021: 9; 2022: 6; 2023: 5; 2024: 8; 2025: 4; 2026: 1.

33 papers.

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  1. 2022Federated Learning on Non-IID Graphs via Structural Knowledge Sharingcited by 88
  2. 2021Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modelingcited by 69
  3. 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 65
  4. 2021OpenFL: An open-source framework for Federated Learningcited by 58
  5. 2022FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learningcited by 43
  6. 2021Federated Graph Learning -- A Position Papercited by 34
  7. 2021FedGraph: Federated Graph Learning with Intelligent Samplingcited by 34
  8. 2021SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networkscited by 30
  9. 2023Federated Generative Learning with Foundation Modelscited by 23
  10. 2021Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challengescited by 17
  11. 2022More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networkscited by 15
  12. 2022Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applicationscited by 14
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  2. 2024Safety in Graph Machine Learning: Threats and Safeguardscited by 11
  3. 2021Enabling SQL-based Training Data Debugging for Federated Learningcited by 8
  4. 2022Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Datacited by 7
  5. 2025A Comprehensive Data-centric Overview of Federated Graph Learningcited by 7
  6. 2023Graph Mining for Cybersecurity: A Surveycited by 5
  7. 2024HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learningcited by 4
  8. 20232SFGL: A Simple And Robust Protocol For Graph-Based Fraud Detectioncited by 3
  9. 2024Starlit: Privacy-Preserving Federated Learning to Enhance Financial Fraud Detectioncited by 3
  10. 2023Equipping Federated Graph Neural Networks with Structure-aware Group Fairnesscited by 2
  11. 2022Asynchronous Collaborative Learning Across Data Siloscited by 1
  12. 2024FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reductioncited by 1
  13. 2025VGFL-SA: Vertical Graph Federated Learning Structure Attack Based on Contrastive Learningcited by 1
  14. 2025Personalized Subgraph Federated Learning with Differentiable Auxiliary Projectionscited by 1
  15. 2025GLIDR: Graph-Like Inductive Logic Programming with Differentiable Reasoningcited by 1
  16. 2023A Framework for Exploring Federated Community Detectioncited by 0
  17. 2024AI versus AI in Financial Crimes and Detection: GenAI Crime Waves to Co-Evolutionary AIcited by 0
  18. 2024Query-Efficient Adversarial Attack Against Vertical Federated Graph Learningcited by 0
  19. 2024Federated Contrastive Learning of Graph-Level Representationscited by 0
  20. 2024FedGAT: A Privacy-Preserving Federated Approximation Algorithm for Graph Attention Networkscited by 0
  21. 2026Synapse: Federated Tool Routing via Typed Compendium Artifactscited by 0