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Effect of Machine Learning Calibration on Insurance Claim Probability Estimates

Abstract

Machine learning models are increasingly applied in insurance to estimate the probability that policyholders will submit claims. These models can process large volumes of insurance data and identify complex relationships among policy, exposure, demographic, and claims-related variables. However, accurate probability estimates depend not only on the predictive ability of a model but also on the calibration of its predicted probabilities. Proper calibration is therefore important for ensuring that estimated claim probabilities appropriately reflect observed claims experience. This study will examine the effect of machine learning calibration on insurance claim probability estimates. It will assess how different calibration techniques influence the accuracy and reliability of probabilities generated by machine learning models for insurance claims. The study will also compare uncalibrated and calibrated probability estimates to determine how calibration affects the consistency between predicted claim probabilities and observed claim frequencies. The study will focus on machine learning calibration, insurance claim probability estimates, probability calibration, claims prediction, actuarial modelling, predictive accuracy, insurance risk assessment, claim frequency, model performance, and probability estimation. Machine learning and statistical techniques will be used to develop claim probability models and evaluate the effect of calibration methods on their predicted probabilities. Calibration approaches such as Platt scaling, isotonic regression, or other suitable methods may be considered. A quantitative research approach will be adopted for the study. Historical insurance policy and claims data will be used to develop machine learning models for estimating the probability of claims. Descriptive statistics, predictive modelling, calibration analysis, discrimination measures, calibration error measures, comparative analysis, and model validation techniques will be applied to assess model performance before and after calibration. The resulting probability estimates will be compared with observed claim frequencies across relevant risk groups. The study is expected to reveal that machine learning 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 the likelihood of claims. The magnitude of the effect may depend on the machine learning algorithm, calibration technique, quality of training data, sample size, class distribution, and characteristics of the insurance portfolio. The study will be useful to actuaries, insurance companies, data scientists, pricing analysts, underwriters, claims managers, regulators, and researchers. It may provide useful information for improving the reliability of machine learning-based claim probability estimates, strengthening actuarial pricing and risk classification, and supporting more accurate insurance decision-making. The findings may also assist insurers in selecting appropriate calibration techniques when applying machine learning models to claims prediction. The study concludes that machine learning calibration is an important consideration in insurance claim probability estimation because the calibration of predictive models can influence the reliability and interpretability of estimated claim probabilities. It is therefore recommended that insurers regularly evaluate the calibration of machine learning models, compare predicted probabilities with observed claims experience, and apply appropriate calibration techniques to improve the reliability of probability estimates.

Keywords: Machine learning calibration, insurance claim probability, probability estimates, claims prediction, probability calibration, actuarial modelling, predictive accuracy, insurance risk assessment, claim frequency, model performance, calibration error, machine learning, model validation, insurance pricing, actuarial analysis.