Car Insurance Fraud Detection using Machine Learning Models

Car insurance fraud is a significant issue that incurs huge monetary losses for insurance companies and increases premiums for their customers. Traditional fraud detection methods often struggle to identify complex and evolving fraudulent behaviors, necessitating more sophisticated solutions. This research analyse the application of various supervised machine learning algorithms to detect fraudulent insurance claims effectively. This paper implements and compares 11 machine learning models, including K-Nearest Neighbors, Support Vector Machines, Decision Trees, Random Forest, and ensemble methods like AdaBoost and Gradient Boosting, among others. The analysis focuses on performance metrics such as recall, precision, F1 score, and overall accuracy, utilizing a labeled dataset of more than 10,000 entries to train and validate these models. The results indicate that ensemble learning techniques, particularly Random Forest and Gradient Boosting, exhibit superior accuracy and generalization capabilities in fraud detection. By employing these advanced machine learning techniques, insurance companies can enhance their ability to detect and prevent fraudulent claims, leading to significant cost savings and more reliable service for legitimate policyholders.