Global financial crime activity is driving demand for machine learning solutions in fraud prevention. However, prevention systems are commonly serviced to financial institutions in isolation, and few provisions exist for data sharing due to fears of unintentional leaks and adversarial attacks. Collaborative learning advances in finance are rare, and it is hard to find real-world insights derived from privacy-preserving data processing systems. In this paper, we present a collaborative deep learning framework for fraud prevention, designed from a privacy standpoint, and awarded at the recent PETs Prize Challenges. We leverage latent embedded representations of varied-length transaction sequences, along with local differential privacy, in order to construct a data release mechanism which can securely inform externally hosted fraud and anomaly detection models. We assess our contribution on two distributed data sets donated by large payment networks, and demonstrate robustness to popular inference-time attacks, along with utility-privacy trade-offs analogous to published work in alternative application domains.

翻译：全球金融犯罪活动正在推动欺诈预防领域对机器学习解决方案的需求。然而，预防系统通常以孤立方式服务于金融机构，由于担心意外泄露和对抗性攻击，数据共享的机制极少。金融领域的协作学习进展稀少，且难以从隐私保护数据处理系统中获得真实世界的洞见。本文提出了一种面向隐私设计的欺诈预防协作深度学习框架，该方案在近期PETs奖挑战赛中获奖。我们利用不同长度交易序列的潜在嵌入表示，结合局部差分隐私，构建了一种数据发布机制，可安全地支持外部部署的欺诈及异常检测模型。我们在两个由大型支付网络捐赠的分布式数据集上评估了贡献，展示了模型对主流推断时攻击的鲁棒性，并验证了与替代应用领域已发表工作类似的效用-隐私权衡结果。