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Analysis of Claim Frequency Distributions in Property Insurance

Abstract

The study analyses claim frequency distributions in property insurance, focusing on the statistical pattern of claims occurring within a specified property insurance portfolio and period. Claim frequency represents the number of claims reported by policyholders and is an important component of actuarial analysis because variations in claim occurrence can influence expected losses, premium estimation, reserve requirements, and overall insurance risk. Accurate modelling of claim frequency is therefore essential for effective property insurance pricing and risk management. The study will examine the frequency of claims arising from property insurance policies and assess the statistical characteristics of the observed claim counts. It will investigate patterns in the occurrence of property insurance claims and determine how well selected probability distributions represent the observed claims experience. The study will also consider the usefulness of claim frequency distributions in estimating expected claims and supporting actuarial decision-making. The study will focus on probability distributions commonly applied to insurance claim frequency, particularly the Poisson and negative binomial distributions. Measures such as mean claim frequency, variance, dispersion, and variation in claim counts will be examined. The study will assess whether the observed claims data satisfy the assumptions of the selected distributions and determine the suitability of each distribution for modelling property insurance claim frequency. A quantitative research approach will be adopted for the study. Historical property insurance claims data containing information on the number of claims reported over selected periods will be analysed. Descriptive statistics, frequency analysis, parameter estimation, probability distribution fitting, and goodness-of-fit tests will be employed to examine the claims data. The estimated distributions will then be compared to determine their ability to represent the observed claim frequency patterns. The study is expected to reveal that property insurance claim frequency varies across periods and may exhibit different levels of dispersion. The findings may indicate that the Poisson distribution provides a suitable representation where the mean and variance of claim counts are relatively similar, while the negative binomial distribution may provide a better representation where greater variability is present. The analysis is also expected to demonstrate the importance of selecting an appropriate frequency distribution for reliable actuarial estimates. The findings are expected to provide useful information for actuaries and property insurance companies in understanding patterns of claim occurrence. The study may assist insurers in improving expected claim estimation, premium calculation, reserve assessment, and portfolio risk analysis. It may also provide a statistical basis for selecting appropriate probability models for property insurance claim frequency and improving the reliability of actuarial pricing decisions. The study concludes that the analysis of claim frequency distributions provides an important basis for understanding the occurrence of losses in property insurance. It is therefore recommended that insurance companies regularly evaluate their historical claims experience and apply suitable statistical and goodness-of-fit techniques when selecting frequency distributions. Accurate modelling of claim frequency may contribute to improved premium estimation, reserve planning, and effective property insurance risk management.

Keywords: Claim frequency distributions, property insurance, claim frequency, actuarial modelling, insurance claims, probability distributions, Poisson distribution, negative binomial distribution, claims experience, frequency analysis, goodness-of-fit, expected claims, premium estimation, insurance risk, actuarial risk management.