Financial institutions increasingly rely upon data-driven methods for developing fraud detection systems, which are able to automatically detect and block fraudulent transactions. From a machine learning perspective, the task of detecting suspicious transactions is a binary classification problem and therefore many techniques can be applied. Interpretability is however of utmost importance for the management to have confidence in the model and for designing fraud prevention strategies. Moreover, models that enable the fraud experts to understand the underlying reasons why a case is flagged as suspicious will greatly facilitate their job of investigating the suspicious transactions. Therefore, we propose several data engineering techniques to improve the performance of an analytical model while retaining the interpretability property. Our data engineering process is decomposed into several feature and instance engineering steps. We illustrate the improvement in performance of these data engineering steps for popular analytical models on a real payment transactions data set.

金融机构越来越依赖数据驱动的方法来开发欺诈检测系统，该系统能够自动检测和阻止欺诈交易。从机器学习的角度来看，检测可疑交易的任务是一个二元分类问题，因此可以应用许多技术。然而，可解释性对于管理层对模型的信心和设计欺诈预防策略至关重要。此外，使欺诈专家能够了解案件被标记为可疑的根本原因的模型将极大地促进他们调查可疑交易的工作。因此，我们提出了几种数据工程技术来提高分析模型的性能，同时保留可解释性属性。我们的数据工程过程被分解为几个特征和实例工程步骤。我们说明了这些数据工程步骤在真实支付交易数据集上流行分析模型的性能改进。