AI-Enhanced ETL for Modernizing Data Warehouses in Insurance and Risk Management

The insurance and risk management sectors are experiencing a significant transformation driven by the need for more sophisticated data analysis and predictive modeling. This paper explores the critical role of artificial intelligence (AI) in enhancing Extract, Transform, Load (ETL) processes for modernizing data warehouses within these industries. We examine how AI-enhanced ETL addresses key challenges such as data quality, integration of diverse data sources, and real-time processing. The study investigates various applications and proposes a framework for successful implementation. Our findings suggest that AI-driven ETL offers unprecedented opportunities for operational efficiency, improved risk assessment, and competitive advantage in insurance and risk management.