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Application of Logistic Regression in Insurance Claim Prediction

Abstract

The study examines the application of Logistic Regression in insurance claim prediction, focusing on its usefulness in estimating the likelihood that a policyholder will submit an insurance claim. Insurance companies are exposed to uncertainty regarding the occurrence of claims, making accurate prediction important for effective underwriting, premium determination, claims management, and risk planning. Logistic Regression provides a statistical framework for analysing the relationship between a binary outcome, such as claim occurrence or non-occurrence, and a set of explanatory variables associated with policyholders and insurance policies. The study focuses on the use of Logistic Regression to identify factors that influence the probability of insurance claim occurrence. Variables such as policyholder characteristics, policy type, previous claims experience, policy duration, and other relevant risk indicators can be incorporated into the model to estimate the likelihood of a future claim. The technique is expected to provide insurers with a systematic approach for distinguishing policyholders with relatively higher and lower probabilities of submitting claims. The study further examines the relevance of Logistic Regression to insurance underwriting and risk classification. Accurate prediction of claim occurrence can assist underwriters in evaluating policyholder risk before insurance decisions are made. By estimating individual claim probabilities, insurers can improve risk classification and develop more informed approaches to underwriting. This may also reduce reliance on subjective assessments and provide a quantitative basis for evaluating insurance risks. The study also considers the application of Logistic Regression in claims management and premium determination. Reliable predictions of claim occurrence can help insurers anticipate potential claims and allocate resources more effectively. The information generated from the model may also support premium decisions by providing additional evidence about the likelihood of future claims associated with different categories of policyholders. The study is expected to demonstrate that Logistic Regression can provide an effective statistical approach to insurance claim prediction. Its ability to estimate the probability of claim occurrence from multiple explanatory variables is anticipated to improve insurers' understanding of policyholder risk and support evidence-based decision-making. However, the reliability of predictions is expected to depend on the quality of the available data, the relevance of the explanatory variables, the appropriateness of the model specification, and the accuracy of the assumptions used in the analysis. The study concludes that Logistic Regression provides a valuable statistical technique for predicting insurance claims and supporting risk-based decision-making. Its application can strengthen underwriting, risk classification, claims planning, premium determination, and insurance portfolio management. The study therefore recommends increased application of appropriate Logistic Regression models, improved collection and management of insurance claims data, and continuous development of statistical and actuarial skills to enhance the accuracy and effectiveness of insurance claim prediction.

Keywords: Logistic Regression, Insurance Claims, Claim Prediction, Insurance Risk, Risk Assessment, Risk Classification, Insurance Underwriting, Claim Occurrence, Premium Determination, Claims Management, Statistical Modelling, Actuarial Science, Policyholder Risk, Predictive Analysis, Insurance Management