Engineering Trustworthy Autonomy in Insurance AI

This technical report discusses the challenges and strategies for achieving autonomy in AI applications within the insurance industry. It outlines the current state of AI adoption among insurers, highlighting that while some companies have made progress, many are still hindered by legacy systems and governance issues. The report introduces two strategic pathways: the pursuit of artificial general intelligence (AGI) and the implementation of reinforcement learning (RL). It details a novel reinforcement-switch framework that combines continuous learning with human oversight to facilitate accountable autonomy. The framework aims to enhance resilience in dynamic environments by embedding trust and oversight into AI operations. The report emphasizes the need for insurers to modernize their data infrastructure and governance models to fully leverage AI capabilities. It concludes that while AGI represents a long-term goal, RL offers a more immediate solution for developing intelligent autonomy in insurance workflows.