Abstract
Mitigating financial crime risk (e.g., fraud, theft, money laundering) is a large and growing problem. In some way it touches almost every financial institution, as well as many individuals, and in some cases, entire societies. 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. In this chapter, we describe a methodology to share key information across institutions by using a federated graph learning platform that enables us to train more accurate detection models by leveraging federated learning as well as graph learning approaches. We demonstrate that our federated model outperforms a local model by 20% with the UK FCA TechSprint data set. This new platform opens up the door to efficiently detect global money laundering activity.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
2019 global AML and financial crime techsprint (2019). https://www.fca.org.uk/events/techsprints/2019-global-aml-and-financial-crime-techsprint
Akoglu L, Tong H, Koutra D (2015) Graph based anomaly detection and description: a survey. Data Min Knowl Discov 29(3):626–688
Alexandre C (2018) A multi-agent system based approach to fight financial fraud: an application to money laundering. ArXiv
Chen Z, Van Khoa LD, Teoh EN, Nazir A, Karuppiah E, Lam KS (2018) Machine learning techniques for anti-money laundering (AML) solutions in suspicious transaction detection: a review. Knowl Inf Syst 57:245–285
Colladon AF, Remondi E (2017) Using social network analysis to prevent money laundering. Expert Syst Appl 67:49–58
Han J, Barman U, Hayes J, Du J, Burgin E, Wan D (2018) NextGen AML: distributed deep learning based language technologies to augment anti money laundering investigation. In: Proceedings of ACL 2018, system demonstrations. Association for Computational Linguistics, pp 37–42
Hanai M, Suzumura T, Tan WJ, Liu ES, Theodoropoulos G, Cai W (2019) Distributed edge partitioning for trillion-edge graphs. CoRR abs/1908.05855, http://arxiv.org/abs/1908.05855, 1908.05855
Jamshidi MB, Gorjiankhanzad M, Lalbakhsh A, Roshani S (2019) A novel multiobjective approach for detecting money laundering with a neuro-fuzzy technique. In: 2019 IEEE 16th international conference on networking, sensing and control (ICNSC), pp 454–458. https://doi.org/10.1109/ICNSC.2019.8743234
Liu W, Liu Z, Yu F, Chen P, Suzumura T, Hu G (2019) A scalable attribute-aware network embedding system. Neurocomputing 339:279–291
Ludwig H, Baracaldo N, Thomas G, Zhou Y, Anwar A, Rajamoni S, Ong Y, Radhakrishnan J, Verma A, Sinn M, Purcell M, Rawat A, Minh T, Holohan N, Chakraborty S, Whitherspoon S, Steuer D, Wynter L, Hassan H, Laguna S, Yurochkin M, Agarwal M, Chuba E, Abay A (2020) IBM federated learning: an enterprise framework white paper v0.1. 2007.10987
Molloy I, Chari S, Finkler U, Wiggerman M, Jonker C, Habeck T, Park Y, Jordens F, Schaik R (2016) Graph analytics for real-time scoring of cross-channel transactional fraud
Nayak K, Wang XS, Ioannidis S, Weinsberg U, Taft N, Shi E (2015) GraphSC: parallel secure computation made easy. In: 2015 IEEE symposium on security and privacy, pp 377–394. https://doi.org/10.1109/SP.2015.30
Page L, Brin S, Motwani R, Winograd T (1999) The PageRank citation ranking: bringing order to the web. Technical Report 1999-66, Stanford InfoLab. http://ilpubs.stanford.edu:8090/422/, previous number = SIDL-WP-1999-0120
Savage D, Wang Q, Chou PL, Zhang X, Yu X (2016) Detection of money laundering groups using supervised learning in networks. ArXiv abs/1608.00708
Truex S, Baracaldo N, Anwar A, Steinke T, Ludwig H, Zhang R (2018) A hybrid approach to privacy-preserving federated learning
Ueno K, Suzumura T, Maruyama N, Fujisawa K, Matsuoka S (2017) Efficient breadth-first search on massively parallel and distributed-memory machines. Data Sci Eng 2(1):22–35. https://doi.org/10.1007/s41019-016-0024-y
Weber M, Chen J, Suzumura T, Pareja A, Ma T, Kanezashi H, Kaler T, Leiserson CE, Schardl TB (2018) Scalable graph learning for anti-money laundering: a first look. CoRR abs/1812.00076, http://arxiv.org/abs/1812.00076, 1812.00076
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Suzumura, T., Zhou, Y., Kawahara, R., Baracaldo, N., Ludwig, H. (2022). Federated Learning for Collaborative Financial Crimes Detection. In: Ludwig, H., Baracaldo, N. (eds) Federated Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-96896-0_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-96896-0_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-96895-3
Online ISBN: 978-3-030-96896-0
eBook Packages: Computer ScienceComputer Science (R0)