Analysis of Claim Frequency and Severity in Auto Insurance Using Generalized Linear Models in Philippines

Michael Angelo ()

Journal of Statistics and Actuarial Research, 2024, vol. 8, issue 3, 34 - 46

Abstract: Abstract Purpose: The aim of the study was to analyze the analysis of claim frequency and severity in auto insurance using generalized linear models in Philippines. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The analysis of claim frequency and severity in auto insurance using Generalized Linear Models (GLMs) in the Philippines revealed that various factors, including driver demographics, vehicle characteristics, and geographic location, significantly impact the likelihood and severity of claims. The GLM approach effectively modeled these relationships, providing insights into risk factors that contribute to higher claim rates and costs. The findings suggest that younger drivers and those in urban areas tend to have higher claim frequencies, while larger vehicles and those with higher engine capacities are associated with more severe claims. Unique Contribution to Theory, Practice and Policy: Utility Theory, Actuarial Fairness Theory & Risk Theory may be used to anchor future studies on analyze the analysis of claim frequency and severity in auto insurance using generalized linear models in Philippines. From a practical perspective, insurers should increasingly adopt telematics and real-time driving data within their GLM frameworks. Policymakers should encourage and potentially mandate the integration of telematics data in auto insurance modeling.

Date: 2024
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