Optimizing Claim Assessment Processes in Property Insurance: A Case Study

Abstract

Predicting the property insurance claim has become very important due to the increasing demand for property and the fluctuating number of monthly claims. However, current prediction methods suffer from limited feature important information and imbalanced claim data. This research introduces a machine learning approach by considering demographic factors of insured objects and marketing strategies to predict claim trends. The study also tackles data imbalance using the SMOTE technique. Results reveal that random forest outperforms logistic regression, achieving a recall value of 96%. The research findings enable insurance companies to better analyze insured risk profiles and manage the number of claims.