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：近年来，公共与私营部门的金融犯罪案件激增，2022年金融机构因诈骗造成的平均损失高达1.02亿美元。构建打击金融犯罪的机制是一项紧迫任务，需要多机构深度协作，然而，由于分布式金融数据的隐私与安全要求，此类协作面临重大技术挑战。例如，考虑环球银行金融电信协会（SWIFT）系统，其每天在全球1.1万家机构间处理4200万笔交易。训练欺诈交易检测模型不仅需要安全的SWIFT交易数据，还需要各交易涉及方在对应银行系统中的私密账户活动信息。样本与特征的分布式特性使得现有大多数学习系统无法直接应用于此类数据挖掘任务。本文通过提出一种混合联邦学习系统，为金融犯罪检测提供安全且具有隐私意识的训练与推理能力，从而共同应对这些挑战。我们开展了大量实证研究，评估所提框架的检测性能与隐私保护能力，并检验其对协作学习常见恶意攻击的鲁棒性。我们在https://github.com/illidanlab/HyFL 公开了源代码。