Healthcare Insurance Fraud Detection Using Machine Learning

Healthcare insurance fraud is a serious problem with huge amount of financial losses and a loss of trust in the healthcare system itself. Traditional methods of detecting fraudulent and inflated claims are increasingly incapable of overcoming the growing sophistication of the fraudster amd struggle to adapt to evolving fraudulent schemes. A framework for detecting fraud in healthcare insurance claims using Machine Learning techniques The data used in this study contains members' details, patients data, medical causes, fees charged, number of claims and the target variable represents if the claim is fraudulent or not. As a model to accomplish this task, we select CatBoost, a potent gradient boosting model with a strong capacity for dealing with categorical data and learning high-level correlations directly on the raw dataset. Isolation Forest, one of such anomaly detection techniques, is employed to find possible outliers and rare fraudulent events, usually not captured by traditional means. They are assessed on real-world data and standard performance metrics: accuracy, precision, recall, and F1-score. Experimental Results demonstrate the promise of machine learning models to improve fraud detection capabilities, reduce loss ratios, increase operational efficiency, and protect the integrity of the healthcare system.