Fraud Detection in Medical Insurance Claim Systems using Machine Learning

Fraud detection in medical insurance claim systems is crucial for preserving healthcare service integrity and minimizing financial losses. This study explores the application of Support Vector Machines (SVM) enhanced by GridSearchCV for hyperparameter optimization, aiming to detect fraudulent claims effectively. The research methodology involves preprocessing a comprehensive medical insurance claims dataset, focusing on extensive feature selection and engineering to improve model performance. GridSearchCV is utilized to conduct an exhaustive search over specified parameter ranges, identifying the optimal hyperparameters for the SVM model. To evaluate the model’s effectiveness, metrics such as accuracy, precision, recall, and F1-score are employed. The results indicate that the optimized SVM model significantly improves the detection of fraudulent claims, outperforming baseline models. This study underscores the efficacy of integrating SVM with GridSearchCV in developing robust fraud detection systems, contributing to more reliable and efficient processing of medical insurance claims.