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This article claims that Generative AI (GenAI) is revolutionizing actuarial science, as demonstrated in four case studies. Large Language Models enhance claims cost prediction by extracting features from unstructured text, reducing errors. Retrieval-Augmented Generation automates market comparisons by processing document data. Fine-tuned, vision-enabled LLMs excel in classifying car damage and extracting contextual details. A multi-agent system autonomously analyzes datasets and generates detailed reports. GenAI also shows promise in automating claims processing, fraud detection, and document compliance verification. Challenges include regulatory compliance, ethical concerns, and technical limitations, emphasizing the need for careful integration of GenAI in insurance workflows.

This paper finds that 38.9% of tasks in jobs involve large language models, with 80% of workers spending 20% of their time on such tasks.Its mapping of risk exposure shows that LLMs directly expose 12.4% of tasks to privacy risks, 13.7% to cybersecurity risks, 13.6% to breach in professional standards risks, 14.1% to unethical or harmful bias risks, 10.6% to misinformation and manipulation risks, 26.4% to safety and physical harm risks, 26% to liability and accountability risks and 9.8% to intellectual property risks.