Recommender Systems in the Insurance Sector: Personalizing Customer Experiences

Abstract

This research article examines the transformative role of recommender systems in the insurance sector, focusing on their impact on personalized customer experiences and operational efficiency. The article analyzes how machine learning algorithms and artificial intelligence have revolutionized insurance product recommendations, risk assessment, and fraud detection. Through comprehensive analysis of recent implementations, the article explores various techniques including collaborative filtering, content-based filtering, and hybrid approaches in insurance recommendation systems. It addresses the challenges faced during implementation, particularly in data protection, system integration, and model transparency, while highlighting the significant improvements in customer engagement, risk assessment accuracy, and operational efficiency. The article also demonstrates how AI-powered recommender systems have enhanced fraud detection capabilities and risk profiling, leading to more precise insurance product matching and improved customer satisfaction.