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Financial crime is a large and growing problem, in some way touching almostevery financial institution. Financial institutions are the front line in thewar against financial crime and accordingly, must devote substantial human andtechnology resources to this effort. Current processes to detect financialmisconduct have limitations in their ability to effectively differentiatebetween malicious behavior and ordinary financial activity. These limitationstend to result in gross over-reporting of suspicious activity that necessitatetime-intensive and costly manual review. Advances in technology used in thisdomain, including machine learning based approaches, can improve upon theeffectiveness of financial institutions' existing processes, however, a keychallenge that most financial institutions continue to face is that theyaddress financial crimes in isolation without any insight from other firms.Where financial institutions address financial crimes through the lens of theirown firm, perpetrators may devise sophisticated strategies that may span acrossinstitutions and geographies. Financial institutions continue to workrelentlessly to advance their capabilities, forming partnerships acrossinstitutions to share insights, patterns and capabilities. These public-privatepartnerships are subject to stringent regulatory and data privacy requirements,thereby making it difficult to rely on traditional technology solutions. Inthis paper, we propose a methodology to share key information acrossinstitutions by using a federated graph learning platform that enables us tobuild more accurate machine learning models by leveraging federated learningand also graph learning approaches. We demonstrated that our federated modeloutperforms local model by 20% with the UK FCA TechSprint data set. This newplatform opens up a door to efficiently detecting global money launderingactivity.

Towards Federated Graph Learning for Collaborative Financial Crimes Detection