Automate Insurance Claims Processing with Custom AI

An agency with 30 employees likely uses an AMS like Applied Epic, Vertafore Sagitta, or HawkSoft. These are powerful systems of record but are fundamentally databases. Their built-in automation is based on triggers and static rules. They cannot read an unstructured PDF of a First Notice of Loss (FNOL) and understand that "water line break in basement" signifies higher severity than "dented fender in parking lot."

Consider an agency handling 750 claims per month. A claim arrives as an email with a 3-page PDF attachment from a partner. A claims assistant opens it, reads through to identify the policy number and loss details. They then switch to their AMS, manually key in over 20 fields, make a subjective judgment on severity, and assign it to an adjuster. This process takes 15 minutes per claim and is prone to errors that delay settlements.

The structural problem is that an AMS is designed for structured data input, not intelligent document interpretation. Off-the-shelf OCR or "AI" plugins attempt to bridge this gap, but they perform poorly on the messy formats of real-world FNOLs. They might extract a claimant's name correctly but fail to parse a narrative description of the loss, which contains the critical information for accurate triage.

The result is that your most experienced adjusters spend time on low-severity claims while complex ones wait, simply because of the luck of the queue. This inconsistent triage directly impacts cycle times and customer satisfaction. The bottleneck is not the adjusters' skill but the manual, error-prone process of getting the right information to them.

The engagement would begin with a discovery phase to audit your current claims intake process. We would analyze 50-100 anonymized FNOL reports to identify all document formats and the key data points required for your AMS. This audit defines the exact data extraction and severity scoring logic before any code is written. You receive a technical specification outlining the full workflow.

The core of the system would be a FastAPI service running on AWS Lambda, ensuring it only incurs costs when processing a claim. When an FNOL email is received, a function triggers the Claude API to read the document, extract entities, and classify claim severity. We use Pydantic models to validate the extracted data against your AMS schema before pushing it via the AMS's native API, eliminating data entry errors. Supabase is used for logging every transaction for a complete audit trail.

The delivered system would process an average 3-page FNOL report and update your AMS in under 60 seconds. The entire build is typically a 4-6 week engagement. For an agency processing 750 claims per month, ongoing cloud hosting costs on AWS Lambda would be under $100 per month. You receive the complete Python source code and a runbook detailing how to monitor the system.