Hybrid Ensemble Learning for Insurance Fraud Detection Integrating Behavioral Analytics with Anomaly Detection at Enterprise Scale

One of the most intense problems facing the insurance business today is how to spot insurance fraud. Lost from these fraudulent claims yearly amounts are in billions of dollars. Conventional approaches that are used to battle fraud issues include rule-based systems as well as decision trees, which just do not work to detect sophisticated and dynamic fraudulent activities. Introduction Behavior analytics with anomaly-detective techniques is a powerful strategy for boosting the detection and prevention of fraudulent claims at an enterprise level, as follows. A Hybrid Ensemble-Based Approach to Improved Fraudulent Transactions Detection Using Anomaly Detector Techniques Introduction Isolation Forrest and One-class SVM behavioral analytics for data-driven inputs combining information from advanced anomaly detection into such a hybrid model. This work applies the model to real-world insurance transaction data and demonstrates an improvement in fraud detection accuracy and scalability over traditional models. It gives a robust, adaptive, and scalable solution for fraud identification and hints towards new avenues for further research in the area of fraud detection systems.