ADVANCED AI-DRIVEN AUTO INSURANCE FRAUD DETECTION: A TECHNICAL OVERVIEW

Abstract

This technical article explores the comprehensive implementation of advanced fraud detection systems in auto insurance, focusing on the integration of artificial intelligence and machine learning technologies. It examines various components including supervised and unsupervised learning models, telematics integration, computer vision analysis, natural language processing, and optical character recognition systems. The article investigates the effectiveness of collaborative database systems and real-time processing mechanisms in identifying fraudulent activities while maintaining operational efficiency. It analyzes the implementation of behavioral analytics and fraud scoring systems, demonstrating their impact on claim processing accuracy and customer satisfaction. Through detailed examination of multiple technological approaches and their integration, the article presents a holistic framework for modern insurance fraud detection. It highlights the significance of combining automated systems with human expertise to create robust fraud detection mechanisms while ensuring efficient claims processing and maintaining customer trust.