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。

分布式欺诈预防系统中的局部差分隐私嵌入模型

Locally Differentially Private Embedding Models in Distributed Fraud  Prevention Systems

Rules are widely used in Fintech institutions to make fraud prevention
decisions, since rules are highly interpretable thanks to their intuitive
if-then structure. In practice, a two-stage framework of fraud prevention
decision rule set mining is usually employed in large Fintech institutions.
This paper is concerned with finding high-quality rule subsets in a
bi-objective space (such as precision and recall) from an initial pool of
rules. To this end, we adopt the concept of Pareto optimality and aim to find a
set of non-dominated rule subsets, which constitutes a Pareto front. We propose
a heuristic-based framework called PORS and we identify that the core of PORS
is the problem of solution selection on the front (SSF). We provide a
systematic categorization of the SSF problem and a thorough empirical
evaluation of various SSF methods on both public and proprietary datasets. We
also introduce a novel variant of sequential covering algorithm called
SpectralRules to encourage the diversity of the initial rule set and we
empirically find that SpectralRules further improves the quality of the found
Pareto front. On two real application scenarios within Alipay, we demonstrate
the advantages of our proposed methodology compared to existing work.

在金融科技机构中，规则广泛用于欺诈预防决策。本文关注通过在初始规则池中找到高质量规则子集（如精确度和召回率），采用 Pareto 最优的概念，以找到一组非支配的规则子集，构成 Pareto 前沿。我们提出了一种名为 PORS 的基于启发式的框架，并确定了 PORS 的核心是前沿解选择问题。同时，我们对公开和专有数据集上的各种前沿解选择方法进行了系统分类和彻底的实证评估。我们还引入了一种名为 SpectralRules 的新颖的序列覆盖算法变体，鼓励初始规则集的多样性，并经实验证明 SpectralRules 进一步提高了找到的 Pareto 前沿的质量。在支付宝的两个实际应用场景中，我们展示了我们提出的方法与现有工作相比的优势。

寻找金融技术应用中双目标帕累托最优欺诈预防规则集

On Finding Bi-objective Pareto-optimal Fraud Prevention Rule Sets for  Fintech Applications

We consider the problem of estimating the false-/ true-positive-rate
(FPR/TPR) for a binary classification model when there are incorrect labels
(label noise) in the validation set. Our motivating application is fraud
prevention where accurate estimates of FPR are critical to preserving the
experience for good customers, and where label noise is highly asymmetric.
Existing methods seek to minimize the total error in the cleaning process - to
avoid cleaning examples that are not noise, and to ensure cleaning of examples
that are. This is an important measure of accuracy but insufficient to
guarantee good estimates of the true FPR or TPR for a model, and we show that
using the model to directly clean its own validation data leads to
underestimates even if total error is low. This indicates a need for
researchers to pursue methods that not only reduce total error but also seek to
de-correlate cleaning error with model scores.

在存在有错误标签（标签噪声）的验证集的情况下，我们考虑对二分类模型的伪阳性率（FPR）和真阳性率（TPR）进行估计的问题。我们的动机应用是防欺诈，其中准确估计 FPR 对于保护好顾客的经验至关重要，并且标签噪声是高度不对称的。现有方法旨在最小化清理过程中的总误差 - 以避免清理非噪声的示例，并确保清理噪声示例。这是一个重要的准确性度量，但不足以确保模型的真实 FPR 或 TPR 的良好估计，我们证明了即使总误差较低，使用模型直接清理其自身的验证数据也会导致低估。这表明研究人员需要追求不仅降低总误差，而且寻求将清理误差与模型评分去相关化的方法。