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Modelling Healthcare Claim Frequency Using Statistical Distributions

Abstract

Healthcare claim frequency is an important component of health insurance risk assessment because it provides information about how often insured individuals submit claims within a specified period. Variations in claim frequency can arise from differences in healthcare utilization, demographic characteristics, medical conditions, insurance coverage, and changes in healthcare-seeking behaviour. Accurate modelling of claim frequency is therefore essential for estimating expected claims, setting appropriate premiums, managing insurance risks, and supporting the financial sustainability of health insurance schemes. This study examines the modelling of healthcare claim frequency using statistical distributions. The study will investigate the frequency patterns of healthcare claims and determine how well selected probability distributions can represent the observed claims experience. By examining the number of claims reported over a given period, the study will seek to identify statistical patterns that can provide reliable estimates for future healthcare claim frequency. The study will consider commonly used statistical distributions for claim frequency modelling, including the Poisson, Binomial, Negative Binomial, and other suitable discrete probability distributions. The selected distributions will be evaluated based on their ability to describe the characteristics of the available healthcare claims data. Attention will be given to important features such as the mean, variance, dispersion, and overall distribution of claim counts in order to determine the most appropriate statistical model. A quantitative research approach will be adopted for the study. Historical healthcare claims frequency data will be collected and analyzed using descriptive statistics, probability distribution techniques, parameter estimation, and goodness-of-fit tests. The estimated models will be compared using appropriate statistical criteria to determine which distribution provides the best representation of the observed healthcare claim frequency data and can be applied effectively for actuarial forecasting. The study is expected to reveal distinct patterns in healthcare claim frequency and demonstrate that statistical distributions can provide useful approaches for modelling claim counts. It is anticipated that the Poisson or Negative Binomial distribution, or another suitable distribution, may provide a better fit depending on the characteristics of the dataset. The findings may also show that differences between the mean and variance of claims can influence the choice of an appropriate probability distribution. The expected findings will have practical implications for actuaries, health insurance companies, and other stakeholders involved in insurance risk management. A reliable claim frequency model may improve premium estimation, claims forecasting, reserve planning, underwriting decisions, and overall risk assessment. It may also provide insurers with a stronger statistical basis for anticipating the volume of healthcare claims and preparing financially for future claims obligations. The study concludes that statistical distribution modelling provides a valuable actuarial approach for understanding and predicting healthcare claim frequency. It is therefore recommended that health insurers regularly analyze historical claims data, test different probability distributions, and select models based on their statistical suitability and predictive performance. The adoption of appropriate claim frequency models will enhance actuarial decision-making and contribute to more effective management of healthcare insurance risks.

Keywords: Healthcare Claim Frequency, Statistical Distributions, Health Insurance, Claim Frequency Modelling, Actuarial Modelling, Probability Distributions, Poisson Distribution, Negative Binomial Distribution, Claim Counts, Claims Forecasting, Insurance Risk, Healthcare Claims, Goodness-of-Fit, Premium Estimation, Risk Management.