Build an AI-Powered Fraud Detection System

Most regional providers rely on manual review and simple checklists to spot fraud. This approach is inconsistent and misses sophisticated schemes. An adjuster reviewing a single claim cannot see that the same body shop and lawyer were involved in three other suspiciously similar claims over the past six months.

Agency Management Systems like Vertafore or HawkSoft offer basic, rule-based flagging. You can set a rule to flag any claim filed within 30 days of a policy's start date, but these static thresholds are easy for organized fraud rings to circumvent. They cannot analyze unstructured text in a claimant's statement or find hidden networks connecting seemingly unrelated claims.

Enterprise fraud platforms like Shift Technology or FRISS are too expensive and complex for a 20-person agency. They require six-figure annual contracts, a 6-month implementation project, and a dedicated team to manage. Their value proposition is built for massive scale, not the specific needs of a regional provider.

Syntora's approach to building an AI-powered fraud detection system would begin with a discovery phase to understand your specific operational context, data sources, and integration requirements. We would work to establish a secure, read-only connection to your Agency Management System, whether it is Applied Epic, Vertafore, or HawkSoft, or other relevant data repositories. The initial step would involve extracting a historical dataset of claims, including both structured fields and unstructured text from documents like FNOL reports and adjuster notes. The Claude API would be used to parse unstructured text from these documents, extracting relevant entities and key phrases to create a structured dataset suitable for analysis. Syntora has built similar document processing pipelines using Claude API for clients in adjacent domains, such as extracting critical information from financial documents, and this pattern applies directly to insurance claims documentation.

From this structured data, Syntora's data scientists would engineer a rich feature set, designing variables that capture potential indicators of fraud. A graph database, such as Neo4j, would be employed to map and analyze complex relationships between claimants, vehicles, service providers, and other entities that might otherwise go unnoticed across your claims history. An XGBoost model would then be trained on this comprehensive dataset to generate a fraud score for each claim, typically ranging from 0 to 100. The model development and validation process typically involves iterative testing and refinement over several weeks, depending on data quality and the complexity of fraud patterns identified, requiring close collaboration with your domain experts.

The trained scoring model would be encapsulated within a lightweight FastAPI application and designed for deployment on a scalable cloud infrastructure, such as AWS Lambda. To integrate with your existing workflow, a new claim filed in your AMS could trigger this function via a webhook. The system would then process the claim data, return a calculated fraud score, and provide a plain-English explanation of the key factors contributing to that score directly back to your AMS. High-risk claims would be automatically flagged for review within the adjuster's dashboard, streamlining the identification process.

All predictions and model inputs would be logged to a Supabase database, providing a robust audit trail and a foundation for continuous model improvement. We would configure monitoring and alerting, using tools like AWS CloudWatch, to track critical operational metrics such as API latency and potential model drift. This proactive monitoring ensures the system's ongoing accuracy and responsiveness. Syntora's engagement would include the initial system design and build, thorough documentation, and knowledge transfer to your team, with options for ongoing support and model retraining as new data becomes available and fraud patterns evolve. Client collaboration, including access to subject matter experts and feedback on model output, would be essential throughout the project.