Build a Custom Churn Prediction Model for Your Agency

Most agencies rely on their AMS for reporting, but platforms like Applied Epic, Vertafore, and HawkSoft are systems of record, not prediction engines. They can show you which policies are up for renewal, but they cannot tell you which of those clients are quietly shopping your quote. You might try exporting a report of last year's non-renewals, but this is a lagging indicator. The data exists, but it is not structured for proactive analysis.

Consider a 15-person agency managing 4,000 personal lines policies. An account manager notices a client had a 20% premium increase on their auto policy last year and filed a small claim 6 months ago. This is a classic churn signal, but it is buried in the client's record. With 200 renewals this month, it is impossible to manually review every client's history for subtle signs like this. The agency only finds out there is a problem when the client calls to cancel.

The structural problem is that an AMS is designed for transactional processing, not relational analysis. Its database is optimized to store policies, endorsements, and claims efficiently, but not to find patterns across them over time. Built-in reporting modules are limited to simple filters and aggregations. They lack the ability to weigh multiple factors, like the combination of a premium hike, a recent claim, and fewer agent interactions, to calculate a single risk score.

Without a predictive model, retention efforts are reactive and inefficient. Agents either treat every renewal the same, wasting time on loyal clients, or they miss the at-risk ones entirely. This leads to preventable revenue loss and a constant, expensive scramble to win new business to replace what was lost. You are flying blind into your most critical revenue period.

The engagement would start with a secure, read-only connection to your AMS database (Applied Epic, Vertafore, etc.). Syntora would audit 24-36 months of historical policy, claims, and interaction data to identify predictive features. You would receive a data quality report outlining which signals are strongest, what data might need cleaning, and a clear project scope before any code is written.

The technical approach would use a Python data pipeline to extract, clean, and transform the data from your AMS. A gradient-boosted model (using a library like XGBoost) would be trained on this historical data to learn the patterns that precede churn. The model is then wrapped in a FastAPI microservice, deployed on AWS Lambda for low-cost, serverless execution, with results stored in a Supabase database. This architecture costs less than $50 per month to operate.

The final system would run weekly, calculating a churn risk score (0-100) for every client with a policy renewing in the next 90 days. These scores are pushed back into a custom field in your AMS or delivered as a simple, prioritized list. Your account managers see a clear, actionable dashboard showing exactly which clients need attention now, turning renewal processing from a reactive task into a proactive retention strategy. Response time for scoring a single client would be under 500ms.