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Effect of Model Specification Choices on Insurance Premium Estimates

Abstract

Model specification choices refer to the decisions made when defining the variables, functional relationships, distributional assumptions, and structural form of an actuarial or statistical model. In insurance pricing, these choices influence how risk factors are represented and how expected losses and other pricing components are estimated. Appropriate model specification is therefore important because differences in model structure may lead to differences in estimated insurance premiums. This study will examine the effect of model specification choices on insurance premium estimates. It will assess how alternative model specifications influence estimated premium levels and determine the extent to which changes in selected model components affect insurance pricing results. The study will also compare premium estimates obtained from alternative specifications using the same insurance data. The study will focus on model specification choices, insurance premium estimates, actuarial pricing, risk factors, claims experience, predictive variables, probability distributions, functional forms, premium rating, and insurance risk modelling. Alternative specifications will be developed by varying relevant model assumptions and explanatory variables, while the resulting premium estimates will be examined to identify differences in pricing outcomes. Statistical and actuarial measures will be used to assess the consistency and sensitivity of the estimated premiums. A quantitative research approach will be adopted for the study. Historical insurance policy, premium, claims, and risk-factor data will be analysed using descriptive statistics, regression analysis, actuarial pricing models, comparative analysis, model specification testing, and sensitivity analysis. Alternative model specifications will be applied to the same dataset, and the resulting premium estimates will be compared to determine the effect of specification choices on insurance pricing. The study is expected to reveal that model specification choices may have a significant effect on insurance premium estimates. Differences in selected variables, functional forms, distributional assumptions, and model structures may produce variations in estimated premiums and risk classifications. The magnitude of the effect may depend on the characteristics of the insurance data, claims variability, risk-factor relationships, model complexity, and the assumptions incorporated into the pricing model. The study will be useful to actuaries, insurance companies, underwriters, pricing analysts, data scientists, financial analysts, regulators, and researchers. It may provide useful information for evaluating alternative model specifications, improving premium estimation, identifying sensitive pricing assumptions, and strengthening actuarial pricing practices. The findings may also assist insurers in understanding the financial implications of model specification decisions when developing risk-based premium structures. The study concludes that model specification choices are important considerations in insurance premium estimation because alternative model structures can produce different pricing results from the same underlying data. It is therefore recommended that insurers and actuarial analysts carefully evaluate model specifications, test alternative assumptions and structures, and conduct sensitivity analysis to support consistent, reliable, and appropriate insurance premium estimates.

Keywords: Model specification, insurance premium estimates, actuarial pricing, insurance pricing, risk modelling, claims experience, predictive variables, functional forms, probability distributions, premium rating, actuarial models, model assumptions, risk factors, premium estimation, insurance analytics.