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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.14136 |
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| _version_ | 1866915452106571776 |
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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 |