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Effect of Claim Severity on Expected Aggregate Insurance Losses

Abstract

The study examines the effect of claim severity on expected aggregate insurance losses, focusing on how the size or monetary value of individual insurance claims influences the total losses expected by an insurer over a specified period. Claim severity is an important component of actuarial risk analysis because substantial variations in individual claim amounts can significantly affect aggregate losses, premium adequacy, reserve requirements, and the financial position of insurance companies. Understanding the relationship between claim severity and aggregate losses is therefore essential for effective insurance risk assessment. The study will investigate how changes in claim severity influence expected aggregate insurance losses within an insurance portfolio. It will examine the distribution of individual claim amounts and assess how variations in average and extreme claim sizes affect the total expected loss. The study will also consider the importance of accurately estimating claim severity in actuarial calculations and insurance risk management. The study will focus on actuarial techniques for modelling claim severity and aggregate insurance losses. Probability distributions such as the lognormal, gamma, Weibull, and Pareto distributions may be considered for modelling individual claim amounts. Aggregate loss models will then be applied to assess how changes in claim severity influence the expected total loss. Measures such as average claim amount, variance, expected aggregate loss, and loss variability will be considered. A quantitative research approach will be adopted for the study. Historical insurance claims data containing individual claim amounts and related loss information will be analysed over a defined period. Descriptive statistics, probability distribution analysis, actuarial loss models, and statistical estimation techniques will be employed to examine claim severity patterns and their relationship with aggregate losses. Appropriate statistical tests may also be used to assess the significance of observed relationships. The study is expected to reveal that increases in claim severity are associated with increases in expected aggregate insurance losses. The findings may indicate that portfolios experiencing relatively high individual claim amounts generate greater aggregate losses even when the number of claims remains stable. The analysis may also show that extreme claims and variations in the distribution of claim amounts can substantially influence aggregate loss estimates. The findings are expected to provide useful information for insurance companies and actuaries in estimating potential loss exposure and determining appropriate premium and reserve levels. The study may assist insurers in improving claims forecasting, assessing financial requirements, and identifying portfolios with higher exposure to large losses. It may also contribute to better underwriting and risk management decisions through more accurate modelling of claim severity. The study concludes that claim severity constitutes an important determinant of expected aggregate insurance losses and should be appropriately incorporated into actuarial loss models. It is therefore recommended that insurance companies regularly analyse claim severity patterns and apply suitable probability distributions and actuarial techniques when estimating aggregate losses. Effective monitoring of claim severity may improve premium adequacy, reserve estimation, and the overall management of insurance loss exposure.

Keywords: Claim severity, aggregate insurance losses, actuarial modelling, insurance claims, claim amounts, expected losses, loss distribution, probability distributions, lognormal distribution, gamma distribution, Weibull distribution, Pareto distribution, claims experience, insurance pricing, actuarial risk.