Use AI to Identify High-Risk Clients and Improve Underwriting

Most small insurance firms rely on their Agency Management System (AMS) like Vertafore or HawkSoft for reporting. An AMS is a system of record, not a predictive engine. You can run a report to see which clients had claims last year, but you cannot use that data to predict which new applicants are most likely to have a future claim. The tools lack the statistical functions to build and test a predictive model.

Consider a 10-person agency specializing in commercial liability for small contractors. Your underwriters manually review each application, looking for obvious red flags based on their experience. An applicant with a prior claim is easy to spot. But what about subtle patterns? Contractors who operate as sole proprietorships in a specific zip code with less than 3 years in business might have a 40% higher claim frequency, but this pattern is invisible without statistical analysis of your past 1,200 policies. The manual process misses this, leading to underpriced risk.

Enterprise-grade risk assessment tools from companies like Verisk or LexisNexis are priced for large carriers with millions of policies, making them unaffordable for a 15-person agency. Even if they were affordable, these models are generic black boxes trained on industry-wide data, not your specific book of business. The structural problem is that existing tools for small agencies are built for workflow management, not predictive analysis, and their architecture is mismatched for the needs of a specialized firm.

The first step would be an audit of your historical data within your AMS. Syntora would analyze at least 36 months of policy, client, and claims data to identify potentially predictive features. You would receive a data quality report that outlines which variables have predictive power and what data cleanup is required before a model can be built.

The technical approach would use a gradient boosting framework in Python, a standard for this type of predictive task. This model would be wrapped in a FastAPI service and deployed on AWS Lambda for cost-effective, serverless execution. When a new application is entered into your AMS, a webhook would send the data to the API, which returns a risk score (e.g., 1-100) and the top three contributing risk factors in about 200ms.

The final system would write this risk score and the rationale directly back into a custom field in your AMS. Your underwriters see the score inside the tool they already use every day. You receive the full Python source code, a runbook for retraining the model quarterly, and a simple monitoring dashboard to track model performance.