Implement a Tailored AI Risk Assessment Tool

Small commercial lines insurers rely on their Policy Administration System as the core system of record. These platforms are excellent for managing policies, billing, and claims data once it is structured. However, they are not built to interpret the unstructured data that contains the most critical risk signals: PDFs of property inspection reports, supplemental applications, and prior carrier loss runs.

An underwriter at a 20-person firm receives a submission for a contractor's general liability policy. The submission includes a 12-page PDF from a prior carrier. The underwriter must manually read the entire document to find mentions of lapse in coverage, specific claim types that indicate high risk, and other notes. This process takes 30 minutes of focused effort and its quality depends entirely on that individual underwriter's diligence. A key detail on page nine might be missed on a busy afternoon.

This manual bottleneck exists because a PAS is fundamentally a database with a user interface. It cannot read a PDF and identify that a phrase like "evidence of water intrusion near foundation" is a major underwriting red flag. Off-the-shelf document extraction tools often fail because they are trained on generic invoices or receipts, not the specific language and format of insurance documents. They extract text but lack the context to classify it as a specific risk factor.

The structural issue is that existing insurance software is designed for data entry, not data interpretation. Your team is forced to act as a human bridge, reading unstructured documents and translating them into the structured fields the PAS requires. This creates a permanent capacity limit on your underwriting team, directly capping how many policies you can accurately assess and bind.

The engagement would begin with a data and workflow audit. Syntora would work with your underwriters to collect and analyze examples of the 5-10 most common submission documents. We would map the key risk factors your team looks for today and identify where they are found. This initial 2-week phase produces a detailed specification document that outlines the data extraction logic and the integration points with your existing PAS.

The technical solution would be a custom data processing pipeline built on AWS Lambda. When a new submission arrives, a Python script would pass any attached documents to the Claude API. The API would use a carefully engineered prompt to find and extract specific risk factors, outputting them as structured JSON. This JSON data would be stored in a Supabase database and fed to a lightweight scoring model that flags high-risk submissions. We've used this exact pattern to process complex financial documents, and the same architecture applies directly to insurance forms.

The result is a system that presents underwriters with a clear, concise risk summary within 90 seconds of receiving a submission. Instead of reading 12 pages of text, they get a dashboard showing that the submission has, for example, 3 past liability claims and a mention of outdated electrical wiring. The system would expose a secure FastAPI endpoint that allows your PAS to pull this structured risk data, pre-filling the underwriting worksheet and eliminating manual data entry.