Build an AI System for Fraud Detection

Small insurance agencies typically rely on the built-in features of their AMS, like Applied Epic or Vertafore. These platforms have basic rule-based flags, such as alerting on any claim over $5,000. These static rules are easy for determined bad actors to circumvent. They simply submit multiple claims for $4,500. The system sees each claim in isolation and approves them, missing the fraudulent pattern entirely.

Consider this common scenario. A 15-person agency handles hundreds of small auto claims per month. A new auto glass repair shop begins submitting claims. Each claim is for a legitimate policyholder, but the invoice is inflated by $300. Since each claim is under the manual review threshold, they pass through without scrutiny. Over six months and across 50 different policies, this amounts to $15,000 in fraud. An experienced adjuster cannot possibly track one provider's behavior across dozens of unrelated policies manually.

The structural problem is that an AMS is a system of record, not a system of intelligence. Its architecture is designed to store policy and claim information in structured fields. It is not designed to perform complex analysis on the unstructured text in FNOL reports, adjuster notes, or attached PDFs. Without the ability to parse and understand this text, detecting nuanced fraud like inflated invoicing or staged accidents is impossible with off-the-shelf tools.

An engagement would begin with a data audit. Syntora would analyze 12-24 months of your historical claims data, including adjuster notes and FNOL reports. This audit identifies the key textual and numerical features that correlate with past fraudulent claims. You would receive a report detailing the data's readiness and the specific fraud patterns a custom model could be trained to detect.

The technical approach would use the Claude API to parse unstructured text from claim documents and notes, extracting entities like provider names, repair types, and incident descriptions. This structured output would feed a model that calculates a fraud score. The entire workflow would be managed by a FastAPI service deployed on AWS Lambda, ensuring it can handle claim volume spikes without ongoing server management. We've used this exact document processing pattern with Claude API for financial services clients; the same architecture applies directly to insurance claims.

The delivered system would push a fraud score (e.g., 0-100) and a one-sentence explanation back into a custom field in your AMS. Adjusters would see high-risk claims flagged directly within their existing workflow, without needing to learn a new tool. The serverless architecture on AWS Lambda keeps hosting costs under $50/month for a typical agency's claim volume, and a claim can be processed and scored in under 5 seconds.