Driving Innovation in Insurance Products with Intelligent Technologies and Machine Learning

Abstract

The insurance sector is undergoing a significant transformation with the integration of intelligent technologies and machine learning (ML), enabling the development of innovative, customer-centric products. This study explores how ML models evaluate extensive datasets, including customer behavior, market trends, and risk profiles, to design customized insurance policies that address evolving consumer needs. Predictive analytics, driven by Gradient Boosting and Random Forest models, achieved high accuracy rates of 97.2% and 96.5%, respectively, showcasing their effectiveness in tasks such as risk assessment, dynamic pricing, and fraud detection. Clustering models segmented customers into distinct groups, revealing insights such as high-risk business owners with an average risk score of 0.88 and premiums of $4,800, and retirees with low claims, exhibiting a risk score of 0.15 and premiums of $900, enabling tailored policy recommendations. NLP analysis identified key customer concerns, including premium affordability (35.2%) and claims processing speed (21.4%), leading to actionable solutions such as flexible payment options and streamlined claims approval. Anomaly detection models identified operational risks like fraudulent claims (12.5%) and policy misuse (8.3%), guiding insurers to implement robust resolution strategies. This research underscores the critical role of ML in transforming insurance product design, improving operational efficiency, and enhancing customer satisfaction, paving the way for sustained innovation and competitiveness in a dynamic market landscape.