Use AI to Automate Underwriting and Risk Assessment

Most small agencies run on an AMS like Applied Epic or Vertafore. These systems are great for client management but offer limited underwriting intelligence. Their built-in analytics can report on policy counts and premiums, but they cannot read an inspection report to flag a property with a deteriorating roof or parse a supplemental questionnaire to identify a high-risk business activity.

Consider a 10-person agency specializing in commercial property. A new application arrives for a restaurant. The producer manually reviews the ACORD 125, ACORD 140, a 5-page supplemental questionnaire, and 12 photos from a property inspection. They must cross-reference this against the carrier's 20-page underwriting guide to check for ineligible business types, fire suppression system requirements, and prior claims history. This manual check takes 25-40 minutes per submission and is prone to human error.

The core problem is that AMS platforms are systems of record, not systems of intelligence. Their data models are structured around policies and clients, not the unstructured data inside documents where the real risk lives. They lack the native ability to process images, PDFs, or free-text fields. Add-on modules are generic and cannot be trained on your agency's specific book of business or your top carriers' unique underwriting appetites.

This forces senior underwriters to spend time on low-value triage instead of complex risks. It also creates inconsistency, where one underwriter might approve a submission that another would flag. The result is slower quote times, higher error rates, and a risk profile that may not align with carrier guidelines, jeopardizing commissions and contracts.

The engagement would begin with an audit of your current submission process and underwriting guidelines. Syntora would map your top 5 carriers, their specific data requirements, and the document formats you receive (PDFs, JPEGs, emails). This initial 3-day discovery phase produces a data flow diagram and a clear plan for what data to extract and which rules to automate.

The technical approach would be a data processing pipeline using the Claude API to parse documents and extract key risk factors. A FastAPI application would host the business logic, scoring each submission against carrier-specific rules defined in a Supabase database. This architecture is ideal because AWS Lambda allows for event-driven processing, triggering the analysis the moment a new document hits your system, with a typical processing time under 15 seconds per submission packet.

The final system would integrate with your existing AMS. When a new application is logged in Applied Epic, a webhook would trigger the Syntora pipeline. The system would return a risk score, a summary of red flags, and a checklist of verified underwriting rules directly into a custom field. Underwriters get an instant, accurate triage, reducing their manual review time by over 90%.