Build a Custom AI System for Insurance Claim Fraud Detection

Independent insurance agencies rely heavily on Agency Management Systems (AMS) like Applied Epic, Vertafore, or HawkSoft. While these platforms are essential systems of record for claims processing, they are not designed for deep analytical tasks or proactive fraud detection. Adjusters often find themselves relying on intuition and time-consuming manual spot-checks, which are inherently unscalable and prone to missing subtle, coordinated fraud patterns that might unfold over months or even years.

Consider an adjuster handling a First Notice of Loss (FNOL) for a commercial property water damage claim. The claim narrative might include details like 'recent plumbing repair' and 'sudden pipe burst.' Despite a 'gut feeling' of something amiss, the adjuster lacks the tools to quickly validate their suspicion. They might spend valuable time sifting through the AMS for the claimant's history, but without an integrated analytical layer, they cannot easily detect if the same public adjuster or contractor was involved in multiple similar 'sudden burst' claims across different clients over the last six months, a common indicator of organized fraud.

Furthermore, the large, off-the-shelf fraud detection platforms used by national carriers are not a viable solution for independent agencies. These enterprise-grade systems are typically trained on vast datasets of personal auto and home claims, making their pre-built models less relevant for agencies specializing in commercial lines or niche markets. They are also prohibitively expensive for a 5-30 person agency and cannot be cost-effectively retrained on an agency's unique book of business to identify region-specific or client-specific fraud signals. The structural issue is that existing tools operate at the wrong scale: an AMS is a passive database, and enterprise fraud software is built for a different business model entirely. An independent agency requires a system tailored and trained on its own historical data to identify the specific signals of fraud within its unique operational context. The challenge is often compounded by data quality issues, similar to what we encounter when migrating legacy benefits platforms where 40-50% of data can be inconsistent or incomplete, making pattern recognition difficult without prior cleaning.

A targeted engagement would begin with a comprehensive data audit of your existing Agency Management System. Syntora would analyze relevant closed claims data—typically 5-10 years if available—from your Applied Epic, Vertafore, or HawkSoft instance. The primary goal is to identify which specific data fields, including unstructured adjuster notes and FNOL reports, contain statistically predictive signals for fraudulent activity. This discovery phase would culminate in a detailed report assessing data quality and outlining the technical feasibility of building an effective, agency-specific fraud model.

The core of the proposed system would involve developing a custom classification model, typically built using Python and libraries like scikit-learn. For handling the unstructured text content of FNOLs and adjuster notes, the Claude API would be employed to parse, extract key entities, dates, and incident descriptions, transforming this qualitative data into a structured format. We have experience building similar document processing pipelines using Claude API for financial documents, and the same pattern applies effectively to insurance documentation. This structured data, combined with historical claim outcomes, would then be used to train the fraud detection model. The entire analysis pipeline would be designed for deployment on AWS Lambda, ensuring a cost-effective, event-driven processing architecture that activates only when new claim data requires analysis.

The delivered system would integrate directly with your AMS. When a new claim is filed or updated, a configured webhook would trigger the analysis. The system would be designed to process the new claim data and return a fraud risk score, often on a scale of 1-100, along with a plain-English explanation of the key factors contributing to that score. This information would then populate a custom field within the adjuster's existing view in the AMS, providing immediate, data-driven context before any human review or investigation begins. Deliverables would typically include an architectural design document, the deployed and tested AI model, integration components, and documentation for ongoing maintenance.