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The recent decade witnessed a surge of increase in financial crimes acrossthe public and private sectors, with an average cost of scams of $102m tofinancial institutions in 2022. Developing a mechanism for battling financialcrimes is an impending task that requires in-depth collaboration from multipleinstitutions, and yet such collaboration imposed significant technicalchallenges due to the privacy and security requirements of distributedfinancial data. For example, consider the modern payment network systems, whichcan generate millions of transactions per day across a large number of globalinstitutions. Training a detection model of fraudulent transactions requiresnot only secured transactions but also the private account activities of thoseinvolved in each transaction from corresponding bank systems. The distributednature of both samples and features prevents most existing learning systemsfrom being directly adopted to handle the data mining task. In this paper, wecollectively address these challenges by proposing a hybrid federated learningsystem that offers secure and privacy-aware learning and inference forfinancial crime detection. We conduct extensive empirical studies to evaluatethe proposed framework's detection performance and privacy-protectioncapability, evaluating its robustness against common malicious attacks ofcollaborative learning. We release our source code athttps://github.com/illidanlab/HyFL .

A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection