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This work has been supported by the Investigo Program of the Generalitat Valenciana (INVEST/2023/304).

Analysis of institutional authors

Osorio, CeliaCorresponding AuthorFuster, NoeliaAuthorPerez-Bernabeu, ElenaAuthor

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Machine Learning Applications for Classification in Insurance: A Case Study

Publicated to:Lecture Notes In Computer Science. 14779 83-92 - 2025-01-01 14779(), DOI: 10.1007/978-3-031-78241-1_8

Authors: Osorio, Celia; Tsertsvadze, Veronika; Fuster, Noelia; Perez-Bernabeu, Elena; Segura-Gisbert, Jorge

Affiliations

Union Alcoyana, SA Seguros & Reaseguro, Alcoy 03801, Spain - Author
Univ Politecn Valencia, Res Ctr Prod Management & Engn, Ferrandiz Carbonell, Alcoy 03801, Spain - Author

Abstract

Within the financial sector, insurance companies generate significant amounts of data on a daily basis, from policy transactions to customer interactions and risk evaluation. This accumulation of data presents an important opportunity for all of these companies to use it for their own strategic advantage. Therefore, through a detailed analysis, this paper presents a case study on how several supervised classification machine learning algorithms (Logistic Regression, Random Forest, Support Vector Machine, etc.) can help in identifying and classifying new clients whether they are potential or non-potential clients for a company in the insurance sector. Moreover, this paper also considers a hybrid ensemble mechanism with the best-performing models among all evaluated models, alongside a decision threshold variable to balance accuracy and minority class recall. The results demonstrate the ability to choose different models to enhance the company's decision-making in different market scenarios.

Keywords

Client classificationInsurancInsuranceMachine learning

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Lecture Notes In Computer Science due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2025, it was in position , thus managing to position itself as a Q2 (Segundo Cuartil), in the category .

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Osorio Muñoz, Celia) .

the author responsible for correspondence tasks has been Osorio Muñoz, Celia.