论文标题
零售银行中付款欺诈系统的基于Q-Network的深度自适应警报阈值选择政策
Deep Q-Network-based Adaptive Alert Threshold Selection Policy for Payment Fraud Systems in Retail Banking
论文作者
论文摘要
机器学习模型已被广泛用于欺诈检测系统。大多数研发工作都集中在提高欺诈评分模型的性能上。但是，下游欺诈警报系统仍然不限于不采用模型，并依靠手动步骤。警报系统在零售银行中的所有支付渠道中普遍使用，并在整体欺诈检测过程中发挥重要作用。当前的欺诈检测系统最终由于无法说明警报处理能力而导致大量删除警报。理想情况下，警报阈值选择使系统能够在平衡上游欺诈分数和警报处理团队的可用带宽时最大化欺诈检测。但是，实际上，用于简单性的固定阈值没有这种能力。在本文中，我们提出了欺诈警报系统的增强阈值选择策略。提出的方法将阈值选择作为顺序决策问题提出，并使用基于Q-Network的深度强化学习。实验结果表明，这种自适应方法通过减少欺诈损失以及提高警报系统的运行效率来超过当前的静态解决方案。
Machine learning models have widely been used in fraud detection systems. Most of the research and development efforts have been concentrated on improving the performance of the fraud scoring models. Yet, the downstream fraud alert systems still have limited to no model adoption and rely on manual steps. Alert systems are pervasively used across all payment channels in retail banking and play an important role in the overall fraud detection process. Current fraud detection systems end up with large numbers of dropped alerts due to their inability to account for the alert processing capacity. Ideally, alert threshold selection enables the system to maximize the fraud detection while balancing the upstream fraud scores and the available bandwidth of the alert processing teams. However, in practice, fixed thresholds that are used for their simplicity do not have this ability. In this paper, we propose an enhanced threshold selection policy for fraud alert systems. The proposed approach formulates the threshold selection as a sequential decision making problem and uses Deep Q-Network based reinforcement learning. Experimental results show that this adaptive approach outperforms the current static solutions by reducing the fraud losses as well as improving the operational efficiency of the alert system.
