Forecasting Automobile Insurance Claims: Examining the Interplay of Policyholder Traits for Enhanced Predictive Insights

Auto insurance policyholders make claims for vehicle losses or damages. The policyh 01 der reports an acciden t., theft., or damage to the insurer. Police reports., incident information., and documentation are frequently needed for claims. Insurance adjusters are crucial for analyzing claims., losses., and repair or replacement costs. To compensate policyholders as per their insurance. The claim compensation may include automobile repair., full loss., medical expenses., or third-party damages. Insurance companies verify claims and prevent fraud via fraud detection. The claims investigation usually asks policyholders to give records and evaluative assistance. Car insurance claims assist clients recover from unforeseen damage or loss. Exploring policyholder attributes' complex interactions improves prediction. By examining demographics., driving behavior., and claims history., insurers understand risk variables better. This interaction allows the construction of complex predictive models that go beyond standard risk assessment., boosting future event prediction and insurance sector decision-making. In predictive modeling., using a number of different machine learning methods leads to a wider range of outcomes. Logistic regression., random forest., support vector machines (SVM)., and K-nearest neighbors (KNN) are some methods that can be used to make predictions more accur ate over all. This variety hel ps in the proces s of revealing complicated patterns and improving accuracy., which contributes to more robust and nuanced insights., which eventually facilitates informed decision-making in numerous areas. The research work uses a Insurance Claims dataset including policyholder characteristics., driving behavior., and claims history. This dataset supports logistic regression., random forest., SVM, and KNN training and validation. The dataset's variety and richness enable risk factor nuanced analysis and predictive model construction for insurance claim outcomes.