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Analysis of Healthcare Claim Severity Using Actuarial Models

Abstract

The study analyzes healthcare claim severity using actuarial models, focusing on the financial magnitude of individual healthcare insurance claims and the application of quantitative techniques for estimating potential claim costs. Claim severity is an important component of health insurance risk because variations in the size of claims can significantly affect insurers' financial obligations, reserve requirements, premium adequacy, and overall portfolio performance. Accurate analysis of claim severity is therefore essential for effective actuarial decision-making and health insurance risk management. The study will analyze the severity of healthcare insurance claims by examining the distribution and characteristics of claim amounts incurred by insured individuals. It will assess variations in claim sizes and identify patterns that may influence the financial exposure of health insurance providers. The study will also examine the extent to which actuarial models can provide reliable estimates of expected claim severity and support the assessment of future healthcare liabilities. Furthermore, the study will consider the suitability of different probability distributions and actuarial techniques for modelling healthcare claim severity. Models based on distributions such as the Gamma, Lognormal, Weibull, or other appropriate severity distributions may be examined based on the characteristics of the available claims data. Measures such as mean claim size, variance, standard deviation, percentiles, and tail losses may also be considered in assessing the severity distribution and its implications for health insurance risk. A quantitative research approach will be adopted for the study. Historical healthcare claims data containing individual claim amounts will be obtained from relevant health insurance records. Descriptive statistical techniques will initially be used to examine the distribution of claim amounts, while actuarial modelling techniques will be applied to estimate claim severity and assess the goodness of fit of selected probability distributions. Appropriate statistical criteria may be used to compare the performance and predictive reliability of the models. The study is expected to reveal significant variations in healthcare claim severity and demonstrate that actuarial models can effectively describe the distribution of claim amounts. It may show that certain probability distributions provide better representations of healthcare claim severity than others, particularly where claims contain a concentration of moderate losses alongside a smaller number of high-cost claims. The findings may also highlight the importance of accounting for extreme claims when estimating the financial risk associated with healthcare insurance. The findings are expected to provide useful information for insurers in improving premium pricing, claims forecasting, reserve estimation, and financial risk management. Accurate modelling of claim severity may enable insurers to estimate expected losses more reliably, identify potential high-cost claims, strengthen reserve calculations, and develop pricing structures that adequately reflect healthcare risks. The study may also demonstrate the value of actuarial models in supporting evidence-based decisions within health insurance operations. The study concludes that actuarial modelling provides an important framework for analyzing healthcare claim severity and understanding the financial risks associated with variations in claim amounts. It is therefore recommended that health insurance companies should incorporate appropriate actuarial severity models into their claims analysis and risk management practices. Insurers should also regularly review claims data and model performance to ensure that premium pricing, reserving, and financial planning remain responsive to changes in healthcare claim patterns.

Keywords: Healthcare Claim Severity, Actuarial Models, Health Insurance, Insurance Claims, Claim Amount, Claim Severity Modelling, Probability Distributions, Actuarial Analysis, Healthcare Risk, Claims Forecasting, Premium Pricing, Reserve Estimation, Financial Risk, Health Insurance Claims, Loss Modelling.