Multi-Agentic Automation for Evaluating Property Claims in Underwriting

Insurance underwriting for property damage demands extensive human labor efforts while consuming large amounts of time and presents challenges from document variety and complexity. This paper develops a Multi-Agentic Automation Framework by using Multi-Agent Systems with Large Language Models to improve property claim underwriting. Each individual task within the underwriting procedure receives autonomous AI processing from self-modular agents working in coordinated teams to process documents for risk assessment insights generation. Furthermore, the framework offers three main components which are its modular design along with its task split and coordination features and its use of LLMs for context-driven document interpretation. The outcomes of our experiments prove that with this framework we can achieve an accuracy of up to 92.9% while getting responses in under a minute. This comes with consistency and proning to deviation of about 94% while handling complex and edge-cased scenarios. We test this in a commercial and production-ready setting using Streamlit and CrewAI alongside Pydantic. This shows the practical usage of the framework to enhance scalability and underwriting workflow efficiency and cost reduction. The insurance industry is envisioned to benefit from LLM-driven multi-agent systems creating opportunities for quicker and better underwriting processes that build lasting and reliable insurance solutions.