Calculate the ROI of AI-Powered Underwriting

Most SMB insurance firms run on an Agency Management System (AMS) like Applied Epic, Vertafore, or HawkSoft. These platforms are excellent systems of record for policies and commissions, but they are not designed for intelligent underwriting. Their automation features are typically limited to pre-set reminders and basic, rule-based workflows, which fall short of true risk assessment.

Consider a commercial lines underwriter at a 20-person agency. They receive a 25-page PDF application for a new restaurant. The underwriter spends 30 minutes manually re-keying data from the PDF into Applied Epic. Then, they open separate browser tabs to check property records, local health department scores, and recent online reviews for signs of management issues. This entire process is manual, inconsistent from one underwriter to the next, and creates a high risk of transcription errors that could misprice the policy.

The AMS cannot read the PDF or connect to a new data source like a restaurant review site. Even if it could, it lacks the logic to weigh these disparate factors. It treats an email from a known broker and a PDF from a new one identically. The system forces your most experienced underwriters to spend their time on low-value data logistics instead of high-value risk analysis.

The structural problem is that an AMS is built for administration, not decision-making. Its architecture is optimized for storing policy data in a rigid structure, not for enriching that data in real time. Integrating modern AI tools is often blocked by limited or expensive APIs, forcing agencies to work around the system instead of through it. This manual work directly limits how many policies your team can quote and bind.

The engagement would begin with a focused discovery process. Syntora would audit your current underwriting workflow for one specific line of business, mapping every field you extract from ACORD forms, every external website you check, and every calculation you perform. This produces a technical blueprint for an AI assistant that mirrors your best underwriter's decision process.

The system would be a FastAPI service deployed on AWS Lambda for efficiency and low cost. When a new application PDF arrives in an inbox, the service is triggered. The Claude API reads the document, extracts up to 50 key data points, and validates the data using Pydantic schemas. The service then queries external risk APIs in parallel using httpx for speed. The combined data is fed to a simple, transparent model that generates a risk score and a plain-English summary of its reasoning.

The final output is pushed directly into a custom field in your AMS via its API. Your underwriter would see a risk score from 1-100, a summary of key risk factors, and a link to source data, all within 60 seconds of the application arriving. This transforms the underwriter's job from data collector to decision auditor, allowing them to process more applications with higher accuracy.