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Effect of Data Imputation Techniques on Insurance Premium Estimates

Abstract

Insurance pricing and premium estimation depend heavily on the availability of complete and reliable policy and claims data. Missing observations in insurance datasets can affect the estimation of risk characteristics, claim frequency, claim severity, and other variables used in actuarial models. Data imputation techniques provide methods for replacing missing values with estimated values, but different techniques may produce different datasets and consequently influence insurance premium estimates. This study will examine the effect of data imputation techniques on insurance premium estimates. It will assess how alternative methods of handling missing insurance data influence the estimated risk measures and premium values generated from actuarial pricing models. The study will also examine the differences in premium estimates obtained when different imputation techniques are applied to the same insurance dataset. The study will focus on data imputation techniques, insurance premium estimates, missing data, actuarial pricing, claim frequency, claim severity, policy characteristics, risk classification, premium rating, and pricing accuracy. Techniques such as mean imputation, median imputation, regression imputation, and multiple imputation will be considered in preparing insurance datasets for actuarial analysis. The effects of these approaches on estimated insurance risk and premium values will then be assessed. A quantitative research approach will be adopted for the study. Historical insurance policy and claims data containing relevant missing observations will be analysed using selected data imputation techniques, descriptive statistics, actuarial pricing models, parameter estimation, prediction error measures, and sensitivity analysis. Premium estimates generated from datasets treated using different imputation methods will be compared to determine their effects on insurance pricing outcomes. The study is expected to reveal that data imputation techniques may have a significant effect on insurance premium estimates. Simpler imputation methods may produce different risk and premium estimates from more advanced techniques, particularly where missing observations are substantial or concentrated within important pricing variables. The magnitude of the differences may depend on the proportion and pattern of missing data, the characteristics of the insurance dataset, the imputation technique used, and the actuarial pricing model applied. The study will be useful to actuaries, insurance companies, underwriters, pricing analysts, data scientists, risk managers, regulators, and researchers. It may provide useful information for selecting appropriate approaches to handling missing insurance data and improving the reliability of premium estimates. The findings may also support better data management and validation practices in actuarial pricing and insurance risk assessment. The study concludes that data imputation techniques are important considerations in insurance premium estimation because the treatment of missing observations can influence actuarial model inputs and resulting premium values. It is therefore recommended that insurers and actuaries carefully assess the nature and extent of missing data and validate alternative imputation techniques before applying them to insurance pricing models.

Keywords: Data imputation, insurance premium estimates, missing data, actuarial pricing, premium estimation, claim frequency, claim severity, risk classification, premium rating, pricing accuracy, insurance data, actuarial models, data quality, statistical imputation, insurance risk.