# Towards Federated Graph Learning for Collaborative Financial Crimes Detection

arXiv: https://arxiv.org/abs/1909.12946 · HTML: https://mlsanity.com/paper/1909.12946/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2019.

## Built on by (most cited first, 30 of 33)

- [Federated Learning on Non-IID Graphs via Structural Knowledge Sharing](https://mlsanity.com/paper/2211.13009/) (2022)
- [Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling](https://mlsanity.com/paper/2106.05223/) (2021)
- [From Distributed Machine Learning to Federated Learning: A Survey](https://mlsanity.com/paper/2104.14362/) (2021)
- [OpenFL: An open-source framework for Federated Learning](https://mlsanity.com/paper/2105.06413/) (2021)
- [FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning](https://mlsanity.com/paper/2204.05562/) (2022)
- [Federated Graph Learning -- A Position Paper](https://mlsanity.com/paper/2105.11099/) (2021)
- [FedGraph: Federated Graph Learning with Intelligent Sampling](https://mlsanity.com/paper/2111.01370/) (2021)
- [SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks](https://mlsanity.com/paper/2106.02743/) (2021)
- [Federated Generative Learning with Foundation Models](https://mlsanity.com/paper/2306.16064/) (2023)
- [Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2102.01375/) (2021)
- [More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks](https://mlsanity.com/paper/2202.03195/) (2022)
- [Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications](https://mlsanity.com/paper/2207.11812/) (2022)
- [Failure Prediction in Production Line Based on Federated Learning: An Empirical Study](https://mlsanity.com/paper/2101.11715/) (2021)
- [Safety in Graph Machine Learning: Threats and Safeguards](https://mlsanity.com/paper/2405.11034/) (2024)
- [Enabling SQL-based Training Data Debugging for Federated Learning](https://mlsanity.com/paper/2108.11884/) (2021)
- [Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Data](https://mlsanity.com/paper/2202.03758/) (2022)
- [A Comprehensive Data-centric Overview of Federated Graph Learning](https://mlsanity.com/paper/2507.16541/) (2025)
- [Graph Mining for Cybersecurity: A Survey](https://mlsanity.com/paper/2304.00485/) (2023)
- [HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learning](https://mlsanity.com/paper/2406.10616/) (2024)
- [2SFGL: A Simple And Robust Protocol For Graph-Based Fraud Detection](https://mlsanity.com/paper/2310.08335/) (2023)
- [Starlit: Privacy-Preserving Federated Learning to Enhance Financial Fraud Detection](https://mlsanity.com/paper/2401.10765/) (2024)
- [Equipping Federated Graph Neural Networks with Structure-aware Group Fairness](https://mlsanity.com/paper/2310.12350/) (2023)
- [Asynchronous Collaborative Learning Across Data Silos](https://mlsanity.com/paper/2203.12637/) (2022)
- [FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction](https://mlsanity.com/paper/2409.11509/) (2024)
- [VGFL-SA: Vertical Graph Federated Learning Structure Attack Based on Contrastive Learning](https://mlsanity.com/paper/2502.16793/) (2025)
- [Personalized Subgraph Federated Learning with Differentiable Auxiliary Projections](https://mlsanity.com/paper/2505.23864/) (2025)
- [GLIDR: Graph-Like Inductive Logic Programming with Differentiable Reasoning](https://mlsanity.com/paper/2508.06716/) (2025)
- [A Framework for Exploring Federated Community Detection](https://mlsanity.com/paper/2312.09023/) (2023)
- [AI versus AI in Financial Crimes and Detection: GenAI Crime Waves to Co-Evolutionary AI](https://mlsanity.com/paper/2410.09066/) (2024)
- [Query-Efficient Adversarial Attack Against Vertical Federated Graph Learning](https://mlsanity.com/paper/2411.02809/) (2024)

## 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.