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Effect of Predictive Model Calibration on Insurance Claim Probability Estimates

Abstract

Predictive models are increasingly used in insurance to estimate the probability that policyholders will submit claims. The accuracy of these probability estimates is important for insurance pricing, underwriting, risk classification, and portfolio management. Model calibration refers to the degree to which predicted claim probabilities correspond with the actual frequency of observed claims. Poorly calibrated models may produce probability estimates that systematically overstate or understate the likelihood of claims, making calibration an important consideration in actuarial modelling. This study will examine the effect of predictive model calibration on insurance claim probability estimates. It will assess how different calibration approaches influence the reliability and accuracy of predicted claim probabilities. The study will also examine the extent to which calibrated and uncalibrated predictive models differ in their estimation of insurance claim likelihood across different policyholder risk categories. The study will focus on predictive model calibration, insurance claim probabilities, predictive accuracy, calibration error, claim frequency, risk classification, probability estimation, model performance, insurance data, and actuarial prediction. Calibration techniques such as probability scaling, isotonic regression, and other suitable calibration approaches will be applied to predictive insurance models. The resulting probability estimates will be evaluated to determine how calibration influences the representation of actual claim experience. A quantitative research approach will be adopted for the study. Historical insurance policy and claims data will be analysed using predictive modelling techniques, probability estimation, calibration measures, discrimination measures, and statistical validation methods. The performance of calibrated and uncalibrated models will be compared using appropriate measures of calibration accuracy, prediction error, and correspondence between estimated and observed claim probabilities. The study is expected to reveal that predictive model calibration may have a significant effect on insurance claim probability estimates. Properly calibrated models may produce probability estimates that more closely correspond with observed claim frequencies, while poorly calibrated models may systematically overestimate or underestimate claim likelihood. The magnitude of the differences may depend on the underlying predictive model, quality of insurance data, claim frequency, sample size, and calibration method applied. The study will be useful to actuaries, insurance companies, underwriters, risk managers, data analysts, regulators, and researchers. It may provide useful information for improving the reliability of insurance claim probability estimates, supporting more accurate risk classification, and strengthening predictive modelling practices. The findings may also assist insurers in selecting appropriate calibration techniques for models used in insurance decision-making. The study concludes that predictive model calibration is an important consideration in estimating insurance claim probabilities because it can influence the correspondence between predicted probabilities and actual claim experience. It is therefore recommended that insurers and actuaries assess and validate the calibration of predictive models before applying their probability estimates to insurance pricing, underwriting, risk assessment, and portfolio management.

Keywords: Predictive model calibration, insurance claim probability, probability estimates, predictive modelling, claim frequency, calibration error, risk classification, predictive accuracy, model validation, insurance data, actuarial modelling, probability calibration, claim prediction, statistical validation, insurance risk.