在百万级金融数据中发现未知欺诈模式并提供可视化证据。
FRAUDGUESS: Spotting and Explaining New Types of Fraud in Million-Scale Financial Data
- 通过特征空间微聚类识别新型欺诈行为。
- 发现三种新异常行为，其中两种被专家认定为欺诈，捕获数百笔漏检交易。
- 支持专家决策的可视化仪表盘与热力图，提升可解释性。
在已知欺诈类型标注的基础上，如何发现尚未被领域专家认知的新型欺诈行为（检测），并提供可信证据以支持判断（解释）？本文提出FRAUDGUESS，通过在精心设计的特征空间中识别微聚类来检测新型欺诈；同时利用可视化、热力图和交互式仪表盘提供可解释证据。该方法已在真实世界部署，应用于一个匿名金融机构（AFI）的百万级金融交易数据。实验发现了三种新型异常行为，其中两种被专家判定为欺诈或可疑，成功捕捉了数百笔原本会漏检的欺诈交易。
原文摘要 · Abstract (English)
Given a set of financial transactions (who buys from whom, when, and for how much), as well as prior information from buyers and sellers, how can we find fraudulent transactions? If we have labels for some transactions for known types of fraud, we can build a classifier. However, we also want to find new types of fraud, still unknown to the domain experts ('Detection'). Moreover, we also want to provide evidence to experts that supports our opinion ('Justification'). In this paper, we propose FRAUDGUESS, to achieve two goals: (a) for 'Detection', it spots new types of fraud as micro-clusters in a carefully designed feature space; (b) for 'Justification', it uses visualization and heatmaps for evidence, as well as an interactive dashboard for deep dives. FRAUDGUESS is used in real life and is currently considered for deployment in an Anonymous Financial Institution (AFI). Thus, we also present the three new behaviors that FRAUDGUESS discovered in a real, million-scale financial dataset. Two of these behaviors are deemed fraudulent or suspicious by domain experts, catching hundreds of fraudulent transactions that would otherwise go un-noticed.
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