Build an AI Fraud Detection Model for Your Agency

Independent insurance agencies run on an AMS like Applied Epic, Vertafore, or HawkSoft. These platforms are excellent for managing policies and client relationships but offer limited capabilities for intelligent risk assessment. Their built-in rules engines can check for simple, static conditions, but they cannot perform the complex pattern recognition required for modern fraud detection.

Consider an underwriter at a 10-person agency reviewing a commercial auto application. The business was incorporated yesterday, the listed address is a mail drop service, and the owner has an out-of-state license. Your AMS flags none of these individually. The underwriter must manually check Google Street View, the state's business registry, and pull a separate MVR report. This manual process takes 20 minutes and is prone to human error, especially during busy renewal periods.

The structural problem is that an AMS is a system of record, not a system of intelligence. Its architecture is designed to store and retrieve data, not to execute predictive models. It cannot connect multiple weak signals (like a new business, a virtual address, and a pristine driving record) to identify a coordinated, high-risk pattern. Agencies are left with a choice: either absorb the high labor cost of manual checks or accept a higher loss ratio from policies that should have been flagged.

The engagement would begin with an audit of your current underwriting process and historical application data. We would map every field you collect from ACORD forms and supplemental applications to identify predictive signals of fraud. This discovery phase concludes with a clear scope document detailing the technical approach, data requirements, and a fixed project timeline.

The core of the system would be a Python-based machine learning model wrapped in a FastAPI service and deployed on AWS Lambda. When a new application is submitted, a webhook from your AMS would trigger the function. The Claude API would parse any unstructured text from the application notes, and the service would score the combined data points against the fraud model. We would use a gradient boosting classifier, as this technique is highly effective at identifying subtle, non-linear relationships in tabular data.

The delivered system posts a risk score from 1-100 and a short explanation back to a custom field in your AMS. Your underwriters see the fraud alert directly within their existing workflow, enabling them to fast-track low-risk applications and dedicate expert review time to the high-risk submissions. The system provides decision support, improving consistency and letting your team write better business.