Artificial Intelligence in Insurance: Leveraging Machine Learning for Fraud Detection and Risk Evaluation
Keywords:
Machine Learning, Fraud Detection, Risk Assessment, Insurance Technology, Artificial IntelligenceAbstract
The insurance industry faces significant challenges in fraud detection and risk assessment, with fraudulent claims costing billions annually. This research presents a comprehensive framework utilizing advanced machine learning algorithms to enhance fraud detection accuracy and improve risk assessment capabilities. We implemented and compared multiple AI models including Random Forest, Support Vector Machines, Neural Networks, and Gradient Boosting on a dataset of 50,000 insurance claims. Our proposed ensemble model achieved 94.7% accuracy in fraud detection with a false positive rate of 3.2%, significantly outperforming traditional rule-based systems. The risk assessment module demonstrated 89.3% accuracy in premium prediction, leading to improved underwriting decisions. This study contributes to the growing body of knowledge in AI-driven insurance solutions and provides practical insights for industry implementation.
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