mlsanityData to 14 Oct 2026

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

arXiv 2508.14136 ↗First posted 19 Aug 2025cs.LG cs.CGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 5 of its 23 references that are in the map. First posted on arXiv 19 Aug 2025.

Builds on

5 of 23 references.

References · 5

  1. 2017Statistical Analysis and Parameter Selection for Mappercited by 20
  2. 2023On the Expressivity of Persistent Homology in Graph Learningcited by 19
  3. 2023A Comprehensive Review of Community Detection in Graphscited by 12
  4. 2024Differentiable Mapper For Topological Optimization Of Data Representationcited by 5
  5. 2024AuToMATo: An Out-Of-The-Box Persistence-Based Clustering Algorithmcited by 3

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Quantum Topological Graph Neural Networks for Detecting Complex Fraud Patternscited by 0