用拓扑分析发现银行数据中的异常与客户分群模式
Topological Data Analysis for Unsupervised Anomaly Detection and Customer Segmentation on Banking Data
- 基于Mapper与持久同调，挖掘数据拓扑结构
- 无监督识别异常账户与客户细分群体
- 数学拓扑结合实际业务，适合金融风控场景
本文提出基于拓扑数据分析（TDA）的无监督异常检测与客户分群方法。利用Mapper算法和持久同调技术，从银行客户数据中挖掘隐藏的拓扑结构，揭示潜在的模式与关联关系。该框架无需标签即可发现异常账户与客户细分群体，为金融风控与精准营销提供可解释的洞察。结果表明，该方法在真实银行数据集上能有效识别出传统统计方法难以捕捉的复杂结构，具有良好的实用性与可解释性。
原文摘要 · Abstract (English)
This paper introduces advanced techniques of Topological Data Analysis (TDA) for unsupervised anomaly detection and customer segmentation in banking data. Using the Mapper algorithm and persistent homology, we develop unsupervised procedures that uncover meaningful patterns in customers' banking data by exploiting topological information. The framework we present in this paper yields actionable insights that combine the abstract mathematical subject of topology with real-life use cases that are useful in industry.
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