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Comparative Analysis of Statistical Methods for Insurance Claim Prediction

Abstract

The study examines the comparative application of statistical methods for predicting insurance claims, with emphasis on the importance of accurate claim prediction in insurance risk management and actuarial decision-making. Insurance claims are influenced by various patterns and uncertainties, making reliable statistical techniques essential for estimating future claim occurrences and supporting effective financial planning. Accurate prediction can assist insurers in improving underwriting decisions, premium determination, claims management, and overall portfolio performance. The study will investigate selected statistical methods used for insurance claim prediction and compare their effectiveness in forecasting future claims. The analysis will focus on the ability of different statistical techniques to identify patterns in historical claims data and generate reliable predictions. The study will consider the suitability of each method in handling variations in claim experience and the extent to which the methods can provide useful information for actuarial applications. The study will further examine statistical methods such as logistic regression, Poisson regression, negative binomial regression, time series techniques, and other appropriate predictive approaches. Attention will be given to important factors such as claim frequency, historical claims experience, exposure levels, and relevant policy characteristics. The comparative analysis will consider the predictive accuracy, consistency, flexibility, and practical applicability of the selected methods in insurance claim prediction. A quantitative research approach will be adopted for the study using relevant historical insurance claims and policy data. Descriptive statistics will be used to examine the characteristics and distribution of the data, while selected statistical models will be applied to generate claim predictions. Model performance will be evaluated using appropriate measures such as mean absolute error, mean squared error, root mean squared error, mean absolute percentage error, or other suitable accuracy measures depending on the nature of the data. The study is expected to reveal differences in the predictive performance of the selected statistical methods. Some methods may provide more accurate predictions for claim frequency, while others may demonstrate greater suitability when dealing with variations, over-dispersion, or complex claim patterns. The findings are expected to show that the choice of statistical method can significantly influence the reliability of insurance claim predictions. The study is further expected to demonstrate the importance of comparing alternative statistical techniques before selecting a model for actuarial applications. Reliable claim prediction may improve premium adequacy, underwriting decisions, claims planning, reserve estimation, and risk management. The findings may also provide useful guidance to insurance companies and actuarial practitioners in selecting statistical methods that are appropriate for their available data and specific prediction objectives. The study concludes that comparative evaluation of statistical methods is essential for improving the accuracy and reliability of insurance claim prediction. It is therefore recommended that insurance companies should evaluate multiple statistical models rather than relying on a single technique, while regularly reviewing model performance as claims experience and data characteristics change.

Keywords: Insurance Claim Prediction, Statistical Methods, Actuarial Modelling, Claims Frequency, Insurance Claims, Predictive Modelling, Statistical Analysis, Logistic Regression, Poisson Regression, Negative Binomial Regression, Time Series Analysis, Model Comparison, Prediction Accuracy, Insurance Risk Management, Actuarial Forecasting.