Advanced Applications of Generative AI in Actuarial Science: Case Studies Beyond ChatGPT

In this presentation, we will explore the transformative impact of Generative AI (GenAI) on actuarial science, illustrated by case studies including a live demo. GenAI offers significant opportunities to enhance, innovate, and augment traditional actuarial work. We begin with a brief historical overview of AI, followed by four practical case studies that demonstrate how Large Language Models (LLMs) can enhance actuarial processes. For two of the case studies, we will also present a live demo showcasing the functionality and results.First, we show how LLMs improve claims cost prediction by engineering features from unstructured textual data. Second, we investigate the automation of market comparisons using Retrieval-Augmented Generation, a GenAI concept designed to identify and process relevant information from documents. The third case study highlights the capabilities of fine-tuned vision-enabled LLMs in classifying car damage types and extracting contextual information. Lastly, we present a multi-agent system that autonomously analyzes datasets to generate comprehensive reports detailing key findings. We conclude by outlining further potential applications of GenAI within the insurance industry and discussing ethical and technical challenges and considerations associated with its use.