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.

全球金融犯罪活动推动了欺诈预防中对机器学习解决方案的需求。然而，由于对意外泄露和对抗性攻击的担忧，预防系统通常被独立地提供给金融机构，并且几乎没有数据共享的规定。我们在本文中提出了一个从隐私角度设计、在最近的 PETs 奖励竞赛中获奖的合作深度学习框架，用于欺诈预防。我们利用不同长度交易序列的潜在嵌入表示以及局部差分隐私来构建一个安全地通知外部托管的欺诈和异常检测模型的数据发布机制。我们在由大型支付网络捐赠的两个分布式数据集上评估了我们的贡献，并展示了对流行的推断时攻击的鲁棒性，以及类似于替代应用领域的效用 - 隐私权衡。