Shouhui Group AI Underwriting Full-Chain Solution

📌 Background

InsurTech has transitioned from point-based tools to a full-chain AI phase, with intelligent underwriting evolving from auxiliary review to an integrated solution covering consultation, review, and recommendation. As health insurance companies push for IPOs amidst plateauing traffic, cost reduction and efficiency improvement have become core demands, making AI underwriting a key differentiator. Shouhui Group has launched its AI underwriting full-chain solution, Shou Xiaozhi (as disclosed by the company), targeting precisely this segment. This entry breaks down the revenue model of its full-chain underwriting services.

👤 Target Customers

The payers are small and medium-sized insurance companies, insurance brokerage platforms, and health insurance companies. The scenarios include pre-insurance consultation, automated underwriting rule review, and product recommendation conversion. Clients pay based on underwriting order volume or the number of seats; specific contract counts and order scales are subject to official disclosure (contract scale not verified).

💰 Revenue Streams

1) SaaS subscription fees: Charged annually based on underwriting order volume or number of seats; 2) Private deployment fees: A one-time implementation fee for clients with data residency requirements, plus annual maintenance fees; 3) Performance-based fees: Revenue sharing based on underwriting pass rates or labor substitution effects; 4) Data value-added services (opportunity items—revenue from rule library updates and operational analysis is not yet disclosed at scale): Service fees for underwriting rule library updates and operational analysis, charged via reports or annual fees.

🧮 Cost Structure

Fixed costs include AI model training and inference computing power, as well as the continuous maintenance of the underwriting rule library. Secondary costs include personnel for sales and on-site implementation teams. Inference computing costs are diluted per unit as order volume increases, while the most volatile cost is the investment in customized implementation for large clients.

🛡️ Moat

The barrier lies in the integrated capability across consultation, review, and recommendation, as well as the rule library and risk control experience accumulated from real-world underwriting cases. Switching underwriting systems involves business process re-engineering, leading to high replacement costs. It is not about proprietary algorithms, but the binding of data and processes.

🔑 Keys to Success

• Full-chain scenario coverage capability
• Benchmark cooperation cases with leading insurance companies
• Compliance and data security capabilities

⚠️ Risks

• Long procurement cycles in the insurance industry
• Claims disputes caused by model misjudgment

🏢 Cases

• Shouhui Group launched the AI underwriting full-chain solution Shou Xiaozhi (as disclosed by the company, independent verification pending)

📊 SWOT Analysis