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Effect of Insurance Portfolio Size on Claims Prediction Accuracy

Abstract

Insurance portfolio size refers to the number and volume of insurance policies or exposures managed by an insurer within a specified period. The size of an insurance portfolio can influence the amount of claims data available for actuarial analysis and the stability of observed claims experience. Since claims prediction depends on reliable historical information, differences in portfolio size may affect the accuracy and consistency of projected insurance claims. This study will examine the effect of insurance portfolio size on claims prediction accuracy. It will assess how variations in the size of an insurer’s portfolio influence the accuracy of predicted claim frequency, claim severity, and aggregate claims. The study will also compare claims prediction results across different portfolio sizes to determine how portfolio size affects the reliability of actuarial claims estimates. The study will focus on insurance portfolio size, claims prediction accuracy, insurance claims, claim frequency, claim severity, aggregate claims, exposure volume, historical claims data, actuarial prediction, insurance risk modelling, and prediction errors. Historical policy, exposure, and claims data will be examined to determine how differences in portfolio size influence the availability of information and the accuracy of claims predictions. A quantitative research approach will be adopted for the study. Historical insurance portfolio, exposure, and claims data will be analysed using descriptive statistics, claim frequency and severity analysis, aggregate claims modelling, predictive statistical techniques, error measurement, correlation analysis, and sensitivity analysis. Different portfolio sizes will be evaluated to determine their effects on predicted claims and the accuracy of actuarial estimates. The study is expected to reveal that insurance portfolio size may have a significant effect on claims prediction accuracy. Larger portfolios may provide greater volumes of claims and exposure data for statistical analysis, while smaller portfolios may produce greater variability in observed claims experience and prediction results. The magnitude of the effect may depend on portfolio composition, exposure volume, claim frequency, claim severity, data quality, and the stability of historical claims patterns. The study will be useful to actuaries, insurance companies, claims analysts, pricing analysts, underwriters, risk managers, data analysts, regulators, and researchers. It may provide useful information for improving claims prediction, evaluating the reliability of actuarial models, strengthening insurance risk assessment, and supporting more informed pricing and reserving decisions. The findings may also assist insurers in understanding the implications of portfolio size for the quality of claims predictions. The study concludes that insurance portfolio size is an important consideration in claims prediction because the volume of available exposure and claims experience can influence the reliability of actuarial estimates. It is therefore recommended that insurers consider portfolio size when selecting and validating claims prediction models, maintain adequate claims data, and apply appropriate statistical and actuarial techniques to improve prediction accuracy.

Keywords: Insurance portfolio size, claims prediction accuracy, insurance claims, claim frequency, claim severity, aggregate claims, exposure volume, historical claims data, actuarial prediction, insurance risk modelling, prediction errors, claims forecasting, actuarial analysis, portfolio composition, claims estimation.