Sağlık Sigortası Primi Tahmininde Regresyon Ağacı Yaklaşımı

Abstract

Bu çalışma, geleneksel genelleştirilmiş doğrusal modelleri (GLM) gelişmiş makine öğrenimi odaklı regresyon ağacı analizi ile birleştirerek sağlık sigortasında sigorta primlerini tahmin etmeye yönelik bir yaklaşım önermektedir. Çalışmada ilk olarak değişkenlerin önemini incelemek için gerçek tamamlayıcı sağlık sigortası verileri üzerine GLM uygulanmakta ve prim tahminleri üzerinde büyük etkisi olan değişkenlere odaklanılmaktadır. Daha sonra, GLM tarafından önemli olarak tanımlanan değişkenlerin regresyon ağacı analizi ile de önemli olarak tanımlanıp tanımlanamayacağı araştırılmaktadır. Makine öğrenmesi uygulamasında, prim tahminlerinde dikkate alınan risk değişkenleri açısından veri yapısına uygun olarak tabakalı örneklemenin etkisi de analiz edilmektedir. Bu çalışma, prim tahminine ilişkin aktüeryal anlayışa katkıda bulunmakta ve sigortacılara sağlık sigortası verilerinin karmaşık dünyasında müzakere etmelerine yardımcı olacak somut bir çerçeve sunmaktadır. GLM ve regresyon ağaçlarının avantajlarını bir araya getiren bu çalışma, sigortacıların değişen risk faktörlerine uyum sağlamaları için kapsamlı bir karşılaştırma sunmakta ve sigorta analizi alanında prim tutarlarının yeni ve doğru bir şekilde tahmin edilmesini sağlayan bir regresyon ağacı metodolojisini içeren yenilikçi bir çalışmayı temsil etmektedir.

Keywords

• Aktüeryal prim tahmini
• Regresyon ağacı
• Makine öğrenme teknikleri
• Genelleştirilmiş doğrusal modeller

Regression Tree Approach to Estimation of Health Insurance Premium

Abstract

This paper proposes an approach to predicting insurance premiums in health insurance by combining traditional generalized linear models (GLM) with advanced machine learning-driven regression tree analysis. The study first uses GLM on real complementary health insurance data to examine the importance of variables, focusing on those variables that have a large impact on premium estimates. Subsequently, it is investigated whether the variables identified as significant by GLM can also be identified as significant by regression tree analysis. In the application of machine learning, the effect of stratified sampling in accordance with the data structure in terms of the risk variables considered in premium forecasts is also analyzed. This study contributes to the actuarial understanding of premium estimation and provides insurers with a concrete framework to help them negotiate the complex world of health insurance data. By integrating the advantages of GLM and regression trees, this study provides a comprehensive comparison for insurers to adapt to changing risk factors. This study represents a innovative attempt to incorporate a regression tree methodology, providing a novel and accurate estimation of premium amounts in the realm of insurance analysis.

Keywords

• Actuarial premium estimation
• Regression tree
• Machine learning techniques
• Generalized linear models

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