Improve Underwriting Accuracy with AI-Powered Risk Assessment

Independent agencies run on an Agency Management System (AMS) like Applied Epic, Vertafore, or HawkSoft. These platforms are excellent systems of record for policies and client data. However, their risk assessment features are limited to a handful of data points manually entered from ACORD forms. They cannot read the narrative in a supplemental PDF, analyze a claims history report, or identify a hazard in an inspection photo.

Consider an underwriter at a 15-person agency reviewing a new submission for a restaurant. The ACORD 125 and 126 forms look clean. But buried on page 12 of a supplemental PDF is a note about a deep fryer model with a history of fire incidents. A photo attached to the submission also shows frayed electrical wiring near the kitchen's back door. The AMS has no visibility into this data. The underwriter, facing a queue of 25 other submissions, misses these critical details and quotes a premium that does not reflect the true exposure.

The structural problem is that an AMS is designed for data storage and retrieval, not for inference on unstructured data. The underlying data models are rigid, built around standardized forms. These platforms were not architected to connect to large language models or computer vision APIs for parsing and analysis. This requires a separate, dedicated data processing pipeline that sits alongside the AMS, which off-the-shelf tools cannot provide.

The consequence is inconsistent underwriting and missed risk. High-risk policies get priced too low, leading to future losses, while low-risk applicants might be overlooked. The agency's profitability depends on the individual diligence of each underwriter, with no systemic backstop to catch what humans will inevitably miss under pressure.

The engagement would begin with a discovery audit of your current submission process. Syntora would analyze 20-30 of your recent submission packages, including both accepted and rejected policies. This process identifies the specific, high-signal risk indicators currently buried in your documents and defines the initial set of 50+ features the AI model would be trained to extract.

The technical core would be a FastAPI service running on AWS Lambda for event-driven processing. When a new submission is emailed to a dedicated inbox or uploaded to the AMS, a trigger fires the Lambda function. The documents are sent to the Claude API, which is chosen for its large context window capable of handling PDF packages over 150 pages long. Claude extracts the predefined features, which are then fed into a scoring algorithm. The approach uses Python for the entire pipeline, ensuring full control and transparency.

The delivered system integrates directly with your AMS. For each new submission, your underwriters would see a risk score from 1-100 and a concise summary of AI-identified flags (e.g., 'High-risk deep fryer model detected on page 12', 'Potential electrical hazard identified in IMG_405.jpg') in a custom tab on the client record. You receive the complete source code, a technical runbook for maintenance, and an auditable trail for every score generated.