Accelerate Actuarial Model Development with Code Generation

Building and maintaining actuarial models often involves significant coding effort in languages like Python or R. LLMs trained on vast code repositories can act as coding assistants, generating code snippets, suggesting functions, or even drafting entire model structures based on natural language descriptions of the desired analysis (e.g., "Write Python code for a Chain Ladder reserving method"). This significantly speeds up development and reduces manual coding time, though careful review of generated code is essential.