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Effect of Feature Selection Methods on Insurance Loss Prediction

Abstract

Feature selection is an important stage in predictive modelling because it involves identifying the most relevant variables for explaining or predicting an outcome. In insurance, the number and type of variables available in policy and claims datasets can influence the accuracy, efficiency, and interpretability of loss prediction models. Appropriate feature selection can reduce unnecessary information and improve model performance, while inappropriate selection may result in the loss of useful predictive information. This study will examine the effect of feature selection methods on insurance loss prediction. It will assess how different approaches to selecting predictive variables influence the accuracy and performance of models used to estimate insurance losses. The study will also examine variations in loss predictions resulting from the use of alternative feature selection techniques. The study will focus on feature selection methods, insurance loss prediction, predictive variables, claim costs, loss severity, loss frequency, insurance data, model accuracy, model complexity, predictive performance, and actuarial modelling. Feature selection techniques such as correlation-based selection, statistical significance methods, recursive feature elimination, and other suitable approaches will be applied to insurance datasets. Selected variables will then be incorporated into predictive models to assess their influence on insurance loss estimates. A quantitative research approach will be adopted for the study. Historical insurance policy and claims data will be analysed using descriptive statistics, feature selection techniques, predictive modelling, model validation, prediction error measures, and sensitivity analysis. Models developed using different feature selection methods will be compared based on their predictive accuracy, error rates, and other appropriate performance measures to determine the effect of variable selection on insurance loss prediction. The study is expected to reveal that feature selection methods may have a significant effect on insurance loss prediction. Selecting relevant variables may improve prediction accuracy and reduce model complexity, while retaining irrelevant or redundant variables may increase estimation errors or reduce predictive efficiency. The magnitude of the effect may depend on the quality of the insurance data, the number of available variables, the characteristics of the loss data, and the predictive model employed. The study will be useful to actuaries, insurance companies, underwriters, data analysts, statisticians, risk managers, regulators, and researchers. It may provide useful information for identifying relevant predictors, improving insurance loss estimation, reducing unnecessary model complexity, and strengthening data-driven actuarial modelling. The findings may also assist insurers in developing more efficient predictive models for insurance risk assessment and pricing applications. The study concludes that feature selection is an important consideration in insurance loss prediction because the variables included in a predictive model can influence its accuracy and reliability. It is therefore recommended that insurers and actuaries evaluate alternative feature selection methods and validate selected variables before applying predictive models to insurance loss estimation and actuarial decision-making.

Keywords: Feature selection, insurance loss prediction, predictive modelling, insurance losses, predictive variables, claim frequency, claim severity, model accuracy, model complexity, actuarial modelling, data selection, model validation, prediction error, insurance data, risk prediction.