Análisis Cluster para detectar patrones específicos entre usuarios de la banca de seguros para identificar posibles fraudes
Cluster para detectar patrones específicos entre usuarios de la banca de seguros para identificar posibles fraudes
DOI:
https://doi.org/10.5281/zenodo.18625522Keywords:
K-Means clustering, fraud preventionAbstract
This article presents an analytical approach based on unsupervised learning techniques aimed at the early identification of atypical behaviors associated with possible fraud in insurance banking. The increasing digitalization of financial services has significantly increased the complexity and volume of user-generated data, reducing the effectiveness of traditional detection methods based on fixed rules. In this context, clustering techniques allow users to be segmented based on similarities in their behavior patterns, without requiring prior information about the fraudulent nature of the records. The study uses k-means and hierarchical clustering algorithms to analyze data obtained from a survey applied to more than a thousand users of insurance banking services. The results show the existence of differentiated behavioral groups, where minority clusters far from the main centroids constitute fraud risk signals that can support audit, internal control and specialized analysis processes.
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Copyright (c) 2026 Abraham Jorge Jiménez Alfaro, Norma Karen Valencia Vázquez , Griselda Cortés Barrera, Edgar Corona Organiche
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
