用稳健方法高效检测高维银行账户余额异常，提升金融风控能力
Anomaly Detection in High-Dimensional Bank Account Balances via Robust Methods
- 采用高鲁棒性统计方法，抗干扰能力强
- 在260万条数据上实现低计算耗时与高准确率
- 适合金融风控场景中大规模账户异常检测
检测银行账户余额中的点异常对金融机构至关重要，有助于识别潜在欺诈、操作问题或其他异常。稳健统计方法能有效标记离群值，并提供不受污染观测影响的分布参数估计。然而，在高维数据下，这类方法常效率较低且计算成本高。本文提出并实证评估了若干适用于中高维数据集的稳健方法，具备高破壞性点和低计算时间特性。研究基于约260万条匿名用户每日账户余额记录。
原文摘要 · Abstract (English)
Detecting point anomalies in bank account balances is essential for financial institutions, as it enables the identification of potential fraud, operational issues, or other irregularities. Robust statistics is useful for flagging outliers and for providing estimates of the data distribution parameters that are not affected by contaminated observations. However, such a strategy is often less efficient and computationally expensive under high dimensional setting. In this paper, we propose and evaluate empirically several robust approaches that may be computationally efficient in medium and high dimensional datasets, with high breakdown points and low computational time. Our application deals with around 2.6 million daily records of anonymous users' bank account balances.
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