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Analysis of Health Insurance Claim Severity Using Statistical Models

Abstract

The study examines the analysis of health insurance claim severity using statistical models, focusing on the application of quantitative techniques to understand and estimate the financial cost of individual health insurance claims. Claim severity represents the amount paid or incurred for a particular claim and is an important component of actuarial analysis because variations in claim amounts can significantly influence total claims expenditure, premium adequacy, and the financial performance of health insurance schemes. Accurate analysis of claim severity is therefore essential for effective insurance risk assessment and financial planning. The study will examine patterns in health insurance claim severity and identify factors that may contribute to variations in the cost of individual claims. Factors such as age, healthcare utilization, type of medical service, treatment category, hospitalization, and previous claims experience may be considered in analysing differences in claim amounts. The study will also assess how statistical models can be applied to understand the distribution and characteristics of health insurance claim severity. The study will further evaluate the suitability of different statistical models for estimating health insurance claim severity. Appropriate regression and probability distribution models may be applied to examine the relationship between claim amounts and relevant policyholder or healthcare characteristics. The analysis will provide insight into the ability of statistical modelling techniques to capture variations in claim costs and generate reliable estimates for actuarial applications. A quantitative research approach will be adopted for the study. Relevant health insurance claims data containing information on claim amounts, policyholder characteristics, healthcare services, treatment categories, and other relevant variables will be obtained from appropriate insurance records. Descriptive statistics and suitable statistical modelling techniques will be used to analyse the distribution of claim amounts, identify significant factors influencing claim severity, and assess the predictive performance of the selected models. The study is expected to find that statistical models can provide useful estimates of health insurance claim severity and identify important factors associated with variations in claim amounts. The analysis is expected to reveal that certain healthcare services, treatment types, and policyholder characteristics are associated with higher claim costs. The study may also demonstrate that models capable of accounting for the skewed nature of health insurance claim amounts can provide more reliable estimates of claim severity. The study is further expected to establish that accurate modelling of claim severity can improve actuarial forecasting and support more effective insurance decision-making. Reliable estimates of claim amounts may assist insurers in premium pricing, claims management, reserve estimation, risk classification, and financial planning. Appropriate statistical modelling is therefore expected to contribute to better understanding and management of the financial risks associated with health insurance claims. The study concludes that statistical modelling provides an important approach for analysing and predicting health insurance claim severity. It is therefore recommended that health insurers improve the quality and availability of claims data, apply appropriate statistical models to severity analysis, regularly evaluate model performance, and incorporate reliable claim severity estimates into premium pricing, claims management, and broader actuarial risk management practices.

Keywords: Health Insurance, Claim Severity, Statistical Models, Claims Analysis, Actuarial Modelling, Insurance Claims, Claims Costs, Risk Assessment, Claims Prediction, Premium Pricing, Claims Management, Statistical Analysis, Healthcare Expenditure, Actuarial Forecasting, Insurance Risk.