Democratizing Actuarial Expertise Through Fine-Tuned Chain of Thoughts

The actuarial profession faces an unprecedented challenge: while insurance complexity grows exponentially, qualified actuarial expertise remains scarce and concentrated. This research introducesa groundbreaking approach to democratize actuarial reasoning through fine-tuned Large Language Models implementing an innovative ”Actuarial Chain of Thoughts” methodology. Our framework transforms how non-specialists access and utilize advanced actuarial knowledge, enabling natural language interactions with AI systems that replicate expert actuarial reasoning patterns.We present a novel architecture combining domain-specific fine-tuning of advanced reasoning models with automated RMarkdown report generation. The system captures the structuredthinking process of experienced actuaries through specialized training on curated actuarial problem-solving sequences. Users interact through conversational interfaces, posing complex insurance questions in plain language.The training dataset comprises 15,000 carefully curated examples extracted from over 30,000 pages of open-source actuarial science literature, covering all major domains of actuarial practice.The framework’s impact extends beyond efficiency gains, fundamentally reshaping how insurance organizations leverage actuarial insights through transparent, explainable AI reasoning.