Use AI to Improve Underwriting and Risk Assessment Accuracy

Independent agencies typically rely on their AMS, like Vertafore or Applied Epic, as a system of record. These platforms are excellent for managing policies and client data but offer limited intelligence for underwriting. They cannot connect to external data sources in real-time to enrich an application. An underwriter is left to manually assemble a risk profile by logging into multiple, separate portals for property records, vehicle history, or commercial credit reports.

Consider an agency quoting a commercial policy for a new restaurant. The underwriter logs into the county clerk's website to check property history, a second portal for health department scores, and a third for business credit information. They copy-paste dozens of data points into a spreadsheet, visually scan for red flags, and make a judgment call. This process can take 45 minutes for a single application, is highly susceptible to human error, and creates an inconsistent assessment process across the team.

The core architectural problem is that AMS platforms are not designed for data fusion. Their job is to store structured information, not to orchestrate API calls to external services and synthesize unstructured responses. Off-the-shelf rating tools might integrate with a few major carriers, but they cannot incorporate the niche, local, or specialized data sources that give a small agency its competitive edge. You are stuck with a manual, error-prone workflow because your primary tool was never built to solve this problem.

Syntora would begin with a discovery phase to map every data point your underwriters currently gather manually. We would document each source, from public web portals to third-party data APIs, and define the key risk indicators for your book of business. This audit produces a clear technical specification and confirms the data needed to build an effective model. You see the complete plan before any code is written.

The proposed system would be a FastAPI service hosted on AWS Lambda. When a new application is created in your AMS, a webhook would trigger the service. The service would use asynchronous Python clients like httpx to query all external data sources in parallel, typically in under 5 seconds. The Claude API would then receive all of this structured and unstructured data, synthesizing it into a concise risk summary with a preliminary score. This summary and score are then written back into a custom field on the client record in your AMS.

The delivered system is a lightweight, serverless application that integrates directly into your existing workflow. Underwriters see the AI-generated risk summary inside the AMS they already use, without needing to learn a new tool. You receive the full Python source code, a deployment runbook, and a Supabase dashboard for logging and monitoring every assessment. The system could process over 1,000 applications a month for a hosting cost under $50.