Data Engineering for Insurance AI: Building Scalable and Reliable Data Pipelines

In recent years companies in all sectors have started recognizing the need for AI-enhanced solutions and have invested in Data Engineering. Within insurance, coverage solutions and pricing algorithms have already used AI to support or completely replace expert decisions. However, companies within the sector have primarily dedicated resources to implementation and productionization of models at the expense of engineering and operations aspects, such as pipeline scalability and reliability.

This study provides a formal, evidence-based, and objective discussion of Data Engineering for Insurance AI solutions with a focus on scalable and reliable data pipelines within batch and streaming processing paradigms using modern data platforms. The main objectives are to present the most relevant aspects of Data Engineering within insurance, highlight evolving needs and processes, identify the primary challenges during the implementation phase, and point out underexplored areas and future directions.