REGRESSION BASED RISK ANALYSIS IN LIFE INSURANCE INDUSTRY

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dc.contributor.authorBozyiğit, Fatma
dc.contributor.authorŞahin, Murat
dc.contributor.authorGündüz, Tolga
dc.contributor.authorIşık, Cem
dc.contributor.authorKılınç, Deniz
dc.date.accessioned2025-03-21T07:38:09Z
dc.date.available2025-03-21T07:38:09Z
dc.date.issued2020
dc.departmentİzmir Bakırçay Üniversitesi
dc.description.abstractRisk analysis is a crucial part for classifying applicants in life insurance business. Since the traditional underwriting strategies are time-consuming, recent works have focused on machine learning based methods to make the steps of underwriting more effective and strengthening the supervisory. The aim of this study is to evaluate the linear and non-linear regression-based models to determine the degree of risk. Therefore, four linear and non-linear regression algorithms are trained and evaluated on a life insurance dataset. The parameters of algorithms are optimized using Grid Search approach. The experimental results show that the non-linear regression models achieve more accurate predictions than linear regression models and the LGBM algorithm has the best performance among the all regression models with the highest R2, lowest MAE and RMSE values.
dc.description.sponsorshipAhmet Ali SÜZEN
dc.identifier.doi10.47933/ijeir.745343
dc.identifier.doihttps://doi.org/10.47933/ijeir.745343
dc.identifier.endpage184
dc.identifier.issn2687-2153
dc.identifier.issue3
dc.identifier.startpage178
dc.identifier.urihttps://hdl.handle.net/20.500.14034/2663
dc.identifier.volume2
dc.language.isoen
dc.publisherAhmet Ali SÜZEN
dc.relation.ispartofInternational Journal of Engineering and Innovative Research
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_DergiPark_20250319
dc.subjectlife insurance
dc.subjectpredictive analytics
dc.subjectinsurance analytics
dc.subjectregression-based risk analysis
dc.titleREGRESSION BASED RISK ANALYSIS IN LIFE INSURANCE INDUSTRY
dc.typeArticle

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