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Modelling Healthcare Claim Severity Using Probability Distributions

Abstract

Healthcare claim severity is an important aspect of health insurance risk assessment because it measures the financial size of individual claims incurred by insured persons. Differences in treatment requirements, hospitalization, medical procedures, healthcare provider charges, medication costs, and the complexity of medical services can result in substantial variations in claim amounts. Accurate modelling of claim severity is therefore essential for estimating expected insurance losses, setting appropriate premiums, determining reserves, and maintaining the financial stability of health insurance schemes. This study examines the modelling of healthcare claim severity using probability distributions. The study will investigate the distributional characteristics of healthcare claim amounts and determine how well selected probability distributions can represent the observed claims experience. By analysing the size and variation of individual healthcare claims, the study will seek to identify suitable statistical models that can provide reliable estimates of future claim severity. The study will consider probability distributions commonly applied in insurance loss modelling, including the Gamma, Lognormal, Weibull, Exponential, and other appropriate continuous distributions. The selected distributions will be fitted to historical healthcare claim amounts, with attention given to characteristics such as skewness, variability, minimum and maximum claim values, and the concentration of claims across different cost levels. The suitability of each distribution will be assessed to determine the model that most appropriately describes the observed healthcare claim severity. A quantitative research approach will be adopted for the study. Historical healthcare claims data containing individual claim amounts will be collected and analyzed using descriptive statistics, parameter estimation techniques, probability distribution modelling, and goodness-of-fit tests. The performance of the selected distributions will be compared using appropriate statistical measures to identify the distribution that provides the best representation of healthcare claim severity and supports reliable actuarial estimation. The study is expected to reveal considerable variation and possible positive skewness in healthcare claim amounts, with a relatively small number of high-value claims potentially accounting for a substantial proportion of total claims expenditure. It is also expected that some probability distributions will provide a better fit to the observed claims data than others. The findings may demonstrate that selecting an appropriate distribution is important for accurately estimating expected claim costs and understanding the financial characteristics of healthcare losses. The expected findings will have important implications for actuaries, health insurance companies, and other stakeholders involved in insurance financial management. An appropriate claim severity model may improve premium pricing, claims reserving, loss forecasting, risk assessment, and financial planning. It may also assist insurers in identifying the likelihood and financial impact of high-value healthcare claims and in developing more effective strategies for managing claims-related risks. The study concludes that probability distribution modelling provides a useful actuarial approach for analysing healthcare claim severity and estimating potential insurance losses. It is therefore recommended that health insurers regularly examine their claims experience, compare several probability distributions, and select models based on their statistical suitability and predictive performance. The effective application of claim severity models will strengthen actuarial decision-making and contribute to more accurate pricing, reserving, and management of healthcare insurance risks.

Keywords: Healthcare Claim Severity, Probability Distributions, Health Insurance, Claim Severity Modelling, Actuarial Modelling, Insurance Losses, Healthcare Claims, Gamma Distribution, Lognormal Distribution, Weibull Distribution, Claim Amounts, Loss Modelling, Premium Pricing, Claims Reserving, Risk Assessment.