Kanada Otomobil Sigortasında Risk Faktörlerinin Doğrudan Tazmin Edilen Maddi Hasar Üzerindeki Etkisinin Değerlendirilmesi

Year 2024, , 103 - 127, 30.06.2024

Pervin Baylan Neslihan Demirel

https://doi.org/10.51541/nicel.1397941

Abstract

Bu çalışma, özel binek araç kazalarında çeşitli risk faktörlerinin doğrudan tazmin edilen maddi hasarlar (Direct Compensation Property Damage - DCPD) üzerindeki etkisini değerlendiren istatistiksel bir analiz sunmaktadır. 2003 ile 2012 yılları arasındaki on yıllık döneme ait Ontario, Kanada'daki otomobil sigortası verileri kullanılarak, genelleştirilmiş doğrusal ikili logit karma model aracılığıyla maddi hasarın istatistiksel bir modeli araştırılmış ve sigortalıların sınıfları arasındaki dengesizlik dikkate alınmıştır. Sonuçlar, kullanım amacı, sürücü eğitimi, muallak hasar ve gerçekleşen hasar dahil olmak üzere çeşitli risk faktörlerinin DCPD hasarlarının olasılığı üzerinde önemli bir etkiye sahip olduğunu göstermektedir. Bu risk faktörlerinin etkileri, farklı sigortalı sınıflarındaki ağırlıklar — her bir başarı oranını oluşturmak için kullanılan deneme sayısı — altında gözlemlenmiştir. Genelleştirilmiş doğrusal karma modeller (GLMMs) analizi, risk faktörlerinin üçüncü şahıs sorumluluk (TPL) sigortası kapsamındaki DCPD hasarları ve maddi hasarlar (PD) olarak adlandırılan ikili sonuçlar üzerindeki etkisinin değerlendirilmesinde güçlü bir araçtır. Bu modeller, en önemli risk faktörlerini belirlemeye odaklanarak sigorta risk değerlendirmesine ve poliçe tasarımına da bilgi sağlayabilir. İkili sonuçlardaki sınıf dengesizliği dikkate alınarak hesaplanan performans ölçümleri, elde edilen modelin sınıfları doğru tahmin etme yeteneğini doğrulamaktadır. Sınıflandırma performansını ölçmeye yönelik değerlendirme ölçümü olan F1 skoru 0,934 olarak hesaplanmıştır. Ayrıca, Kesinlik-Duyarlılık (Precision-Recall (PR)) eğrisinin altında kalan alan olan PR AUC ise 0,953 olarak elde edilmiştir. Bu yüksek skorlar, elde edilen modelin sınıflandırmada iyi performans gösterdiğine işaret etmektedir. Diğer ölçümler de, bu modelin sınıflandırma doğruluğunu desteklemektedir. Analizin bulguları, sigortacıların maddi hasarların altında yatan nedenleri daha iyi anlamalarına ve risk azaltımı için daha doğru ve etkili stratejiler geliştirmelerine yardımcı olabilir. Ayrıca bu çalışma, farklı sınıflar arasındaki dengesizliği hesaba katmak için sınıfa özgü risk değerlendirme modellerinin geliştirilmesinin önemini vurgulamaktadır.

Keywords

İkili Logit Model, Doğrudan Tazmin Edilen Maddi Hasar, Genelleştirilmiş Doğrusal Karma Model, Üçüncü Şahıs Sorumluluk Sigortası, Dengesiz Panel Veri

Quantifying the Impact of Risk Factors on Direct Compensation Property Damage in Canadian Automobile Insurance

Abstract

This study presents a statistical analysis assessing the impact of various risk factors on direct compensation property damage (DCPD) claims in private passenger vehicle accidents. Using automobile insurance data in Ontario, Canada for the decade years period between 2003 and 2012, a statistical model of property damage was explored via a generalized linear binary logit mixed model and considered the imbalance between the classes of insureds. The results indicate that several risk factors have a significant impact on the likelihood of DCPD claims, including usage, training, outstanding loss, and incurred loss. The effects of these risk factors were observed under the weights — the number of trials used to generate each success proportion — in the different classes of insureds. The generalized linear mixed models (GLMMs) analysis provides a powerful tool for quantifying the impact of risk factors on binary outcomes, which are called DCPD claims and property damage (PD) claims covered by third-party liability (TPL) insurance. These models can also inform insurance underwriting and policy design, focusing on identifying the most significant risk factors. The performance metrics calculated by considering the class imbalance in binary outcomes verify the resulting model’s ability to accurately predict classes. The F1 score, an evaluation metric to measure the performance of classification, was calculated as 0.934. In addition, PR AUC, which is the area under the Precision-Recall (PR) curve, was computed as 0.953. These high scores indicate that the resulting model performs well in the classification. The other metrics also support the classification accuracy of this model. The findings of the analysis can help insurers better understand the underlying drivers of property damages and develop more accurate and effective strategies for risk mitigation. Furthermore, this study highlights the importance of developing class-specific risk assessment models to account for the imbalance across different classes.

Keywords

Binary Logit Model, Direct Compensation Property Damage, Generalized Linear Mixed Model, Third-Party Liability Insurance, Unbalanced Panel Data

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