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Effect of Data Imputation Methods on Insurance Claim Prediction

Abstract

The study examines the effect of data imputation methods on insurance claim prediction, focusing on how different techniques for handling missing insurance data influence the estimation and prediction of future claims. Complete and reliable insurance datasets are important for developing accurate actuarial and statistical models. When important claims or policy information is missing, the quality of predictions may be affected, making the selection of an appropriate data imputation method an important consideration in insurance analytics. The study will investigate how different data imputation methods affect insurance claim prediction. It will assess the extent to which alternative techniques for replacing missing observations influence predicted claim frequency, claim severity, and expected claim costs. The study will also compare prediction outcomes obtained from datasets processed using different imputation approaches. The analysis will focus on data imputation methods, missing insurance data, claim prediction, claim frequency, claim severity, expected claims, and prediction accuracy. Selected imputation techniques such as mean or median imputation, regression imputation, and multiple imputation will be considered. Actuarial and statistical prediction models will then be applied to determine how the choice of imputation method affects insurance claim estimates. A quantitative research approach will be adopted for the study. Relevant insurance claims and policy data will be analysed, with missing observations introduced or identified within the dataset for comparative assessment. Different imputation methods will be applied to the affected data, after which appropriate statistical and actuarial claim prediction models will be developed. Prediction errors and other accuracy measures will be used to compare the resulting claim estimates. The study is expected to reveal that data imputation methods have a measurable effect on insurance claim prediction. Different approaches to replacing missing observations may produce variations in predicted claim frequency, severity, and expected claim costs. The magnitude of these differences is expected to depend on the extent of missing data, the characteristics of the insurance dataset, and the assumptions underlying each imputation method. The findings are expected to provide useful information for actuaries, insurers, and insurance analysts in selecting appropriate techniques for handling missing observations in claims datasets. The study may support improved data preparation and enhance the reliability of actuarial claim prediction models. It may also contribute to better estimation of expected insurance losses and more informed decisions in pricing, reserving, and claims management. The study concludes that the choice of data imputation method is an important consideration in insurance claim prediction. It is therefore recommended that insurers and actuaries evaluate alternative imputation techniques and select methods that are appropriate to the characteristics of their datasets before developing claim prediction models.

Keywords: Data imputation methods, insurance claim prediction, missing data, insurance claims, claim frequency, claim severity, expected claims, actuarial modelling, statistical prediction, prediction accuracy, insurance data, claims analysis, actuarial estimation, data quality, insurance risk assessment.