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

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Hauptverfasser: Barberi, Leonardo Aldo Alejandro, De Cave, Linda Maria
Format: Preprint
Veröffentlicht: 2025
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author Barberi, Leonardo Aldo Alejandro
De Cave, Linda Maria
author_facet Barberi, Leonardo Aldo Alejandro
De Cave, Linda Maria
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2508_14136
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Topological Data Analysis for Unsupervised Anomaly Detection and Customer Segmentation on Banking Data
Barberi, Leonardo Aldo Alejandro
De Cave, Linda Maria
Machine Learning
Computational Geometry
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.
title Topological Data Analysis for Unsupervised Anomaly Detection and Customer Segmentation on Banking Data
topic Machine Learning
Computational Geometry
url https://arxiv.org/abs/2508.14136