Improve Underwriting Accuracy with a Custom AI Risk Model

Mid-sized P&C insurers typically run on an Agency Management System like Applied Epic or Vertafore. These systems are excellent for managing client relationships and standard policies but their underwriting modules are rule-based. They can flag a simple risk, like a home without a fire alarm, but cannot interpret the nuance in a 50-page engineering report for a new commercial construction project.

Consider an underwriter evaluating a policy for a new warehouse in a flood-prone area. The AMS flags the location, but that is it. The underwriter must then manually open the contractor's liability statement, read through a geological survey PDF, and check a separate portal for historical weather data. They synthesize these disparate, unstructured documents in a spreadsheet to justify the final premium. This manual process takes 45 minutes per application, is prone to human error, and creates inconsistent assessments across a 35-person team.

Off-the-shelf risk assessment platforms exist, but they impose a rigid data model and charge steep per-seat fees. They cannot incorporate your firm’s proprietary claims history to identify emerging risk patterns unique to your book of business. You cannot add a new data source, like satellite imagery analysis, without waiting for the vendor to add it to their roadmap. The core architectural problem is that these tools are built for the average insurer, not for your specific risk appetite and operational workflow.

The engagement would begin with a discovery audit of your current underwriting workflow for one specific complex risk category. Syntora would map every document, external data portal, and manual step your underwriters currently use. This produces a technical specification that defines the data inputs, the risk factors to extract, and the exact format of the summary output your team needs.

The technical approach would use a FastAPI service deployed on AWS Lambda for cost-effective, high-availability processing. When a new application is submitted to your AMS, a webhook triggers the service. The Claude API parses unstructured documents like inspection reports or legal contracts, extracting pre-defined risk factors. The system then calls external APIs for supplemental data, like FEMA flood maps or county permit records. All signals are combined into a preliminary risk score and a human-readable summary, which is then posted back into a custom field in your AMS.

The delivered system provides your underwriters with a concise risk brief within 90 seconds of application submission. The brief highlights key risks and provides direct links to the source documents, allowing your experts to focus on judgment, not data collection. You receive the complete Python source code, a runbook for maintenance, and a system running in your own AWS account with hosting costs typically under $100 per month.