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Effect of Model Validation Sample Sizes on Insurance Prediction Accuracy

Abstract

Model validation sample size refers to the number of observations used to evaluate the predictive performance of an insurance model after it has been developed using available data. An appropriate validation sample is important because insufficient observations may produce unstable estimates of prediction accuracy, while excessively large validation samples may reduce the amount of data available for model development. In actuarial and insurance applications, the choice of validation sample size may therefore influence the reliability of predictive modelling results. This study will examine the effect of model validation sample sizes on insurance prediction accuracy. It will assess how different validation sample sizes influence the accuracy and stability of insurance prediction models and determine whether changes in the proportion of data allocated for validation produce differences in predictive performance. The study will also compare prediction results obtained from models evaluated using different validation sample sizes. The study will focus on model validation sample sizes, insurance prediction accuracy, predictive modelling, validation data, training data, prediction errors, model performance, claims experience, actuarial modelling, and insurance analytics. Different validation sample proportions will be considered, while prediction accuracy will be assessed using appropriate statistical performance measures. The study will also examine how sample size affects the consistency and reliability of model validation results. A quantitative research approach will be adopted for the study. Historical insurance policy and claims data will be analysed using descriptive statistics, predictive modelling, sample allocation techniques, validation procedures, comparative analysis, error analysis, and sensitivity analysis. Insurance prediction models will be developed using alternative training and validation sample sizes, and their predictive performance will be compared to determine the effect of validation sample size on prediction accuracy. The study is expected to reveal that validation sample size may have a significant effect on insurance prediction accuracy and the stability of model performance estimates. Very small validation samples may produce greater variation in measured prediction accuracy, while appropriately selected samples may provide more reliable assessments of predictive performance. The magnitude of the effect may depend on the total dataset size, claims distribution, data variability, model complexity, and characteristics of the insurance portfolio. The study will be useful to actuaries, insurance companies, underwriters, claims analysts, data scientists, pricing analysts, regulators, and researchers. It may provide useful information for determining appropriate data allocation strategies, improving predictive model validation, reducing uncertainty in model performance assessment, and strengthening data-driven insurance decision-making. The findings may also assist insurers in establishing practical approaches to evaluating predictive models before their operational application. The study concludes that model validation sample size is an important consideration in insurance prediction because the number of observations used for validation can influence the reliability of measured model accuracy. It is therefore recommended that insurers and actuarial analysts carefully determine validation sample sizes based on dataset characteristics, evaluate prediction performance using appropriate statistical measures, and conduct sensitivity analysis to support reliable model validation and insurance prediction.

Keywords: Model validation sample size, insurance prediction accuracy, predictive modelling, validation data, training data, insurance claims, model performance, prediction errors, actuarial modelling, statistical validation, insurance analytics, claims prediction, data allocation, model reliability, predictive accuracy.