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Analysis of Claim Severity Distributions in Motor Insurance

Abstract

The study analyses claim severity distributions in motor insurance, focusing on the statistical characteristics and patterns of individual claim amounts arising from motor insurance policies. Claim severity represents the financial value of an individual insurance claim and is an important component of actuarial analysis because differences in claim amounts can significantly influence expected losses, premium estimation, reserve requirements, and overall portfolio risk. Accurate modelling of claim severity is therefore essential for effective motor insurance pricing and risk management. The study will examine the distribution of claim amounts in motor insurance and determine the extent to which different probability distributions can adequately represent observed claims experience. It will investigate variations in claim severity and assess the statistical characteristics of motor insurance losses. The study will also consider the relevance of severity distributions in estimating expected claims and supporting actuarial decision-making. The study will focus on commonly applied probability distributions for modelling motor insurance claim severity, including the lognormal, gamma, Weibull, and Pareto distributions. Measures such as mean claim amount, variance, skewness, dispersion, and extreme claim values will be examined. Goodness-of-fit techniques will be used to compare the suitability of the selected distributions in representing observed motor insurance claim amounts. A quantitative research approach will be adopted for the study. Historical motor insurance claims data containing individual claim amounts will be analysed over a defined period. Descriptive statistics, distribution fitting, parameter estimation, graphical analysis, and goodness-of-fit tests will be employed to assess the characteristics of the claims data. The fitted probability distributions will then be compared to determine their usefulness in representing motor insurance claim severity. The study is expected to reveal that motor insurance claim amounts exhibit considerable variation and may demonstrate characteristics such as positive skewness and the presence of relatively large claims. The findings may indicate that some probability distributions provide a closer fit to the observed claim severity data than others. The analysis is also expected to show that appropriate severity modelling can improve the estimation of expected insurance losses. The findings are expected to provide useful information for actuaries and motor insurance companies in analysing loss experience and developing more reliable actuarial models. The study may assist insurers in improving premium estimation, claims forecasting, reserve calculations, and risk assessment. It may also support the identification of appropriate probability distributions for modelling motor insurance losses and evaluating exposure to large claims. The study concludes that claim severity distributions are an important component of actuarial analysis in motor insurance because they provide a statistical basis for understanding the financial size of individual claims. It is therefore recommended that motor insurance companies regularly analyse their claims data and apply appropriate distribution-fitting and goodness-of-fit techniques when modelling claim severity. Accurate severity modelling may contribute to improved loss estimation, premium adequacy, and effective motor insurance risk management.

Keywords: Claim severity distributions, motor insurance, claim amounts, actuarial modelling, probability distributions, loss distribution, lognormal distribution, gamma distribution, Weibull distribution, Pareto distribution, claims experience, severity modelling, goodness-of-fit, insurance losses, motor insurance risk.