DOWNLOAD THE COMPLETE PROJECT

8 Users found this project useful

Effect of Claim Frequency Distribution on Premium Estimation

Abstract

The study examines the effect of claim frequency distribution on premium estimation, focusing on how the statistical distribution of the number of claims influences the determination of appropriate insurance premiums. Claim frequency represents the number of claims reported by policyholders within a specified period and constitutes an important component of actuarial premium calculation. Accurate modelling of claim frequency is therefore necessary for insurers to estimate expected losses and establish premiums that adequately reflect the risks associated with an insurance portfolio. The study will investigate how different claim frequency distributions affect the estimation of insurance premiums. It will examine the pattern of claim occurrences and assess the extent to which alternative probability distributions produce differences in estimated claim frequencies and expected losses. The study will also consider the relevance of frequency distribution assumptions in actuarial pricing and insurance risk assessment. The study will focus on commonly used probability distributions for claim frequency modelling, particularly the Poisson and negative binomial distributions. The analysis will examine the characteristics of each distribution and their suitability for representing observed insurance claim counts. Expected claim frequencies generated from the selected distributions will be incorporated into actuarial premium estimation models to determine their effects on calculated premium levels. A quantitative research approach will be adopted for the study. Historical insurance claims data containing information on the number of claims reported during selected periods will be analysed. Descriptive statistics, frequency distribution analysis, parameter estimation, goodness-of-fit techniques, and actuarial premium calculation methods will be employed. The estimated claim frequencies will then be compared to determine how different distributional assumptions influence premium estimates. The study is expected to reveal that the choice of claim frequency distribution can produce variations in estimated insurance premiums. The findings may indicate that distributions providing a closer representation of observed claim counts generate more consistent estimates of expected claims and premiums. Differences may also emerge between the Poisson and negative binomial approaches where the claims data exhibit greater variability than assumed under a simple Poisson model. The findings are expected to provide useful information for actuaries and insurance companies in selecting appropriate frequency distributions for premium estimation. The study may assist insurers in improving the accuracy of expected claim calculations, strengthening actuarial pricing practices, and reducing the potential effects of inappropriate distributional assumptions. It may also provide a basis for more effective evaluation of insurance portfolio risk and premium adequacy. The study concludes that claim frequency distribution is an important consideration in the estimation of insurance premiums because the assumed distribution influences expected claim frequencies and associated loss estimates. It is therefore recommended that insurance companies assess historical claims experience carefully and apply appropriate statistical goodness-of-fit techniques when selecting claim frequency distributions. Proper distributional modelling may contribute to more reliable actuarial premium estimates and improved insurance risk management.

Keywords: Claim frequency distribution, premium estimation, actuarial pricing, insurance claims, claim frequency, probability distribution, Poisson distribution, negative binomial distribution, expected claims, insurance premiums, claims experience, statistical modelling, actuarial risk, premium adequacy, insurance risk management.