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Application of Generalized Linear Models in Insurance Risk Classification

Abstract

Generalized Linear Models (GLMs) provide flexible statistical techniques for analysing relationships between a response variable and one or more explanatory variables. In insurance, risk classification involves grouping policyholders according to characteristics associated with differences in the likelihood or cost of insurance claims. Accurate risk classification is important because it enables insurers to assess heterogeneous risks, support appropriate premium determination, and improve underwriting decisions. This study therefore examines the application of Generalized Linear Models in insurance risk classification. Generalized Linear Models extend ordinary linear regression by allowing different types of response variables and probability distributions to be modelled using an appropriate link function. In insurance applications, GLMs can be used to model claim frequency, claim severity, or other risk-related outcomes using factors such as age, exposure, vehicle characteristics, policy features, and previous claims experience. The flexibility of GLMs makes them suitable for insurance datasets in which the response variable may not follow a normal distribution and may require specialized modelling assumptions. Insurance risk classification involves identifying and differentiating groups of policyholders according to their expected level of risk. Effective classification requires statistical models capable of identifying meaningful relationships between risk characteristics and insurance outcomes. The application of GLMs can provide a systematic approach to estimating expected claims and comparing the relative influence of different rating factors. Model outputs can therefore assist actuaries and insurers in developing more consistent, evidence-based approaches to risk assessment and classification. The study will adopt a quantitative research design based on the analysis of relevant insurance data. The study population will comprise appropriate insurance policy and claims records from which a suitable sample or dataset will be selected using an appropriate sampling procedure. Data will be analysed using suitable Generalized Linear Model specifications based on the characteristics of the insurance outcome being investigated. Model estimation, diagnostic assessment, goodness-of-fit measures, and relevant statistical tests will be employed to evaluate the suitability and predictive performance of the models for insurance risk classification. The study is expected to establish that Generalized Linear Models can provide useful statistical estimates for distinguishing insurance risks according to relevant policyholder and exposure characteristics. It is expected that appropriate model specifications will identify significant risk factors and provide estimates that can support the classification of policyholders into meaningful risk groups. The findings may also demonstrate that model selection, distributional assumptions, and diagnostic evaluation are important considerations in obtaining reliable results for insurance risk classification. The study will conclude that the application of Generalized Linear Models can strengthen statistical approaches to insurance risk classification by providing a structured framework for analysing claim-related outcomes and differences in risk levels. It will recommend that insurers and actuaries apply appropriate GLM specifications, conduct regular model diagnostics, validate model performance, and update risk classification models as relevant data and portfolio characteristics change. Effective application of these models can support more informed underwriting, risk assessment, and actuarial decision-making.

Keywords: Generalized Linear Models, Insurance Risk, Risk Classification, Actuarial Science, Insurance Claims, Claim Frequency, Claim Severity, Statistical Modelling, Risk Assessment, Underwriting, Insurance Pricing, Predictive Modelling, Rating Factors, Model Diagnostics, Actuarial Modelling