Intelligent Underwriting Assistant: A Prototype System for Insurance Risk Assessment Based on LLM Agent

This article introduces the Agentic Underwriting prototype system developed by QiaoJiang123. Combining technologies such as LLM agents, MCP context selection, and RAG retrieval, the system enables end-to-end automated assistance for commercial cyber insurance underwriting, demonstrating the application potential of AI agents in the vertical insurance domain. The project was created in April 2026 and updated in May.

The traditional model of commercial cyber insurance underwriting has labor-intensive issues: underwriters spend a lot of time on tasks such as multi-channel document search, broker email verification, evidence material inspection, and historical claim review. This system addresses these pain points by integrating scattered underwriting activities into a unified workspace to improve efficiency.

Architecture and Functional Modules

Uses Python backend (FastAPI) + front-end web interface. Core modules include submission search and entry, work queue, chat workspace, underwriting workbench, decision flow gating, task management, and analysis dashboard.

Technical Highlights

1. MCP-style context selection: Retrieve data such as documents/models/claims on demand to save token costs;
2. Multi-step agent orchestration: Planner → tool execution → confidence check → retry expansion, with trace recording for audit;
3. Permission-aware skills: Check user permissions before execution to ensure data security.

Storage and Entities

Uses JSON files for storage in the demo. Core entities: submission metadata, chat history, guide notes, task stages, claim records, broker database.

Model Governance

Includes a model registry to record approval status/usage/restrictions; outputs are attached with source references to enhance interpretability.

Core Value Levers

Impact Lever	Example Annual Value
Underwriting Productivity	$213,750
Rework Reduction	$22,800
Binding Improvement	$148,500
Loss Leakage Reduction	$99,000
Total Value	$484,050

ROI Calculation

Assuming an annual cost of $120k, the ROI is 303% (($484k - $120k)/$120k). The author emphasizes this is a planning assumption.

Key Users

Underwriters, underwriting assistants, underwriting managers, broker teams.

High-Value Workflows

New submission entry, evidence review, underwriting Q&A, broker follow-up, quotation binding support.

Demo Limitations

1. JSON storage is not a production database; 2. Simulated data; 3. Demo model; 4. Simplified document extraction; 5. Manual final decision required.

Future Improvements

Migrate to production database, integrate real systems, enhance document extraction, improve model governance, expand product lines.

This system is an excellent example of AI agents in insurance underwriting, with core innovations being MCP context selection and multi-step orchestration. Developers can learn: agent orchestration for complex workflows, on-demand retrieval strategies, permission design, model governance, and value quantification methods. The project documentation provides a reference for the industry.