Topological Data Analysis for Unsupervised Anomaly Detection and Customer Segmentation on Banking Data

TL;DR

This paper applies Topological Data Analysis techniques, specifically Mapper and persistent homology, to unsupervised anomaly detection and customer segmentation in banking data, providing industry-relevant insights.

Contribution

It introduces a novel framework combining TDA methods for unsupervised anomaly detection and segmentation in banking data, bridging topology and practical industry applications.

Findings

Uncovered meaningful customer patterns using TDA methods.

Demonstrated effectiveness of Mapper and persistent homology in banking data analysis.

Provided actionable insights for banking industry applications.

Abstract

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.