Identify High-Risk Insurance Policies Faster with AI Automation

Independent agencies run on their Agency Management System (AMS). Whether it's Applied Epic, Vertafore, or HawkSoft, the AMS is the system of record. These platforms are excellent for managing client data and tracking structured information like policy numbers and premium amounts. However, they are not designed to interpret the unstructured text where true risk hides.

Consider an agency writing commercial property insurance. A new submission arrives as a 20-page PDF. Buried on page 15 is a note mentioning a "flat roof with evidence of prior ponding." An AMS workflow can flag a policy based on a zip code or a building's age, but it cannot read that sentence. A human underwriter must read the entire document to find it. On a busy day with 50 new submissions, it is easy for this critical detail to be missed, leading to a badly priced policy.

The structural problem is that an AMS is fundamentally a database with a user interface. It is architected for storing and retrieving structured data fields. There is no native capability to pass a PDF to a Large Language Model for analysis and then act on the results. This leaves agencies stuck with a slow, error-prone manual review process that creates a bottleneck, limits growth, and directly exposes the business to underwriting losses.

The only alternative seems to be expensive, enterprise-grade underwriting platforms that are priced for large national carriers, not a 15-person independent agency. These tools also impose rigid workflows that may not fit your specific book of business or underwriting appetite, forcing you to change your process to fit the software.

A project would begin with a discovery phase to map your exact underwriting workflow. We would audit every document type you process, from ACORD forms to third-party inspection reports. Syntora would work with your lead underwriter to create a definitive list of risk factors to be identified, defining the business logic that separates a standard policy from one needing expert review. This audit produces a clear technical specification before any code is written.

The core of the system would be an AWS Lambda function that uses the Claude API for document intelligence. When a new application is added to a specific folder or receives an email tag, the function triggers. Claude API parses the document text, extracts entities like property addresses and prior claims, and flags the presence of your defined risk factors. The extracted data is then passed to a FastAPI service that scores the policy from 1-100 based on your custom rules.

The final system would integrate directly with your AMS. A high-risk score (e.g., over 80) would create a task in Applied Epic or Vertafore assigned to a senior underwriter, with the specific reasons for the flag noted in the task description (e.g., 'Risk: Knob-and-tube wiring mentioned'). The goal is not to replace underwriters, but to give them a powerful filter that lets them focus their time on the policies that actually require their expertise. You receive the full source code and a runbook for maintenance.