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Effect of Actuarial Model Calibration on Insurance Pricing Accuracy

Abstract

The study examines the effect of actuarial model calibration on insurance pricing accuracy, focusing on how the adjustment of actuarial model parameters to reflect observed insurance experience influences the accuracy of estimated premiums. Actuarial pricing models depend on assumptions and parameters derived from historical claims, exposure, and policy data. Proper calibration is therefore important for ensuring that model outputs adequately reflect the underlying risk characteristics of an insurance portfolio. The study will investigate how actuarial model calibration affects insurance pricing accuracy. It will assess whether calibrating model parameters using updated insurance experience produces differences in estimated premium levels and expected losses. The study will also examine how variations in calibration assumptions influence the consistency between predicted insurance costs and observed claims experience. The analysis will focus on actuarial model calibration, insurance pricing accuracy, model parameters, claims experience, expected losses, premium estimates, and pricing errors. Calibration techniques will be applied to selected actuarial models using relevant historical insurance data. The study will compare model outputs before and after calibration to determine the extent to which parameter adjustments influence premium estimates. A quantitative research approach will be adopted for the study. Relevant insurance claims, exposure, policy, and premium data will be analysed using actuarial pricing models and statistical calibration techniques. Model parameters will be estimated and adjusted using observed insurance experience, while prediction errors and differences between estimated and actual claims costs will be assessed. Comparative analysis will be used to evaluate pricing accuracy under calibrated and uncalibrated model conditions. The study is expected to reveal that actuarial model calibration has a measurable effect on insurance pricing accuracy. Properly calibrated models may produce premium estimates that more closely reflect observed claims experience, while poorly calibrated models may generate larger pricing deviations. The magnitude of the effect is expected to depend on the quality of the available data, the stability of claims experience, and the appropriateness of the calibration procedures used. The findings are expected to provide useful information for actuaries and insurance companies in improving the reliability of actuarial pricing models. The study may support regular updating of model parameters, improved alignment between expected losses and premium estimates, and more effective management of pricing risk. It may also contribute to better actuarial decision-making by demonstrating the importance of maintaining models that reflect current insurance experience. The study concludes that actuarial model calibration is an important consideration in achieving accurate insurance pricing. It is therefore recommended that insurers and actuaries regularly review and calibrate pricing models using reliable claims and exposure data to ensure that premium estimates remain consistent with the underlying risk experience.

Keywords: Actuarial model calibration, insurance pricing accuracy, actuarial pricing, premium estimates, model parameters, claims experience, expected losses, pricing errors, actuarial models, insurance premiums, model adjustment, risk assessment, statistical calibration, insurance pricing models, actuarial risk management.