Topological Data Analysis for Unsupervised Anomaly Detection and Customer Segmentation on Banking Data
This work addresses anomaly detection and segmentation problems for banking industry applications, but it appears incremental as it applies existing TDA methods to a specific domain.
The paper tackled unsupervised anomaly detection and customer segmentation in banking data using Topological Data Analysis, resulting in actionable insights by combining topology with industry applications.
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.