The recent decade witnessed a surge of increase in financial crimes across the public and private sectors, with an average cost of scams of \$102m to financial institutions in 2022. Developing a mechanism for battling financial crimes is an impending task that requires in-depth collaboration from multiple institutions, and yet such collaboration imposed significant technical challenges due to the privacy and security requirements of distributed financial data. For example, consider the Society for Worldwide Interbank Financial Telecommunications (SWIFT) system, which generates 42 million transactions per day across its 11,000 global institutions. Training a detection model of fraudulent transactions requires not only secured SWIFT transactions but also the private account activities of those involved in each transaction from corresponding bank systems. The distributed nature of both samples and features prevents most existing learning systems from being directly adopted to handle the data mining task. In this paper, we collectively address these challenges by proposing a hybrid federated learning system that offers secure and privacy-aware learning and inference for financial crime detection. We conduct extensive empirical studies to evaluate the proposed framework's detection performance and privacy-protection capability, evaluating its robustness against common malicious attacks of collaborative learning. We release our source code at https://github.com/illidanlab/HyFL .

翻译：最近十年,公共和私营部门的金融犯罪急剧增加,2022年金融机构的诈骗平均成本为10.2亿美元。制定打击金融犯罪的机制是一项即将到来的任务,需要多个机构进行深入合作,然而,由于分布式金融数据的隐私和安全要求,这种合作带来了巨大的技术挑战。例如,考虑环球银行间金融电信协会(SWIFT)系统(SWIFT),该系统在11 000个全球机构每天产生4 200万笔交易。培训欺诈交易的侦查模式不仅需要环球银行间金融电信协会(SWIFT)的可靠交易,还需要对应银行系统参与每项交易的人的私人账户活动。两种样本和特征的分布性质都使大多数现有学习系统无法直接用于处理数据挖掘任务。在本文件中,我们共同应对这些挑战,提出一个混合的联结式学习系统,为金融犯罪侦查提供安全和隐私意识的学习和推断。我们进行了广泛的实证研究,以评估拟议框架的检测业绩和隐私保护能力,评估其对于常见恶意袭击的合作性学习的稳健性。我们在http://HILA/FLA节中公布我们的源码。