Analysis of Machine Learning Algorithms for Insurance Fraud Detection

Insurance fraud poses a significant challenge to the insurance industry, resulting in substantial financial losses and increased policyholder rates. This paper addresses the issue of auto insurance fraud through the application of machine learning models, including Random Forest Classifier, Support Vector Machine, and K-Nearest Neighbors. By leveraging various factors such as age, policy details, event information, and claims data etc. These models aim to accurately identify fraudulent claims while expediting the processing of legitimate ones.