Venezuelan Insurance Clusters with Unsupervised Machine Learning

Luis David Lara Rodríguez; Jorge Aquino Olmos; Elzabeth López Meléndez

doi:10.56327/jtksi.v7i1.1634

Venezuelan Insurance Clusters with Unsupervised Machine Learning

Luis David Lara Rodríguez⁽¹⁾, Jorge Aquino Olmos⁽²⁾, Elzabeth López Meléndez⁽³⁾,

(1) Polytechnic University of Puebla
(2) Latin American Technological University Online
(3) Technological University of Huejotzingo

Corresponding Author

Abstract

Insurance companies play an important role in a healthy economy, as they provide a security service to goods and people. The Venezuelan insurance market has faced great challenges in the last decades in a narrow environment, where knowing the closest competitors is of utmost importance. The public governing body that regulates the comparison between insurers has only made use of the premiums charged as a rating factor, however this governing body makes public an additional range of indicators, for this research we have selected five of these indicators from the last three years and contrast whether the premiums charged represent a unique rating characteristic. Several machine learning methods have been applied for this study, from dimensionality reduction techniques, unsupervised clustering and optimal clustering performance criteria; all of them have allowed us to discover the hidden patterns of the data and present a clustering that reaches 100% accuracy, of the same number of classes as the government entity; which reflect the naturalness of the data as opposed to the arbitrary one given by this same entity. From its derived groupings, it is possible to affirm that the parameter of premiums collected does not represent a determining grouping characteristic, allowing the Venezuelan insurance market to compare itself efficiently with its competitors.

Keywords

Machine Learning, PCA, Mixture of Gaussian, K-means, BIC, AIC, Insures.

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