arXiv Open Access 2025

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

Leonardo Aldo Alejandro Barberi Linda Maria De Cave

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Abstrak

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.

Topik & Kata Kunci

cs.LG cs.CG

Penulis (2)

Leonardo Aldo Alejandro Barberi

Linda Maria De Cave

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APA MLA BibTeX

Barberi, L.A.A., Cave, L.M.D. (2025). Topological Data Analysis for Unsupervised Anomaly Detection and Customer Segmentation on Banking Data. https://arxiv.org/abs/2508.14136

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Informasi Jurnal

Tahun Terbit: 2025
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓