A next-generation preﬁll solution, for the most accurate replacement cost and Coverage A calculations
State-of-the-art technology ↗, combined with novel data sources, provides a new level of prefill consistency, accuracy, and transparency.
Purefill uses data fusion technology to improve the accuracy and dependability of home insurance prefill, helping carriers create more accurate replacement cost estimates and better match premium to true exposure.
Outlier Detection layers onto your existing prefill solution to identify large errors in the key drivers of replacement cost, including living area, quality grade rating, and the number of stories. Suitable for front-end prefill, it can improve the customer experience by increasing the initial accuracy of data. Outlier Detection provides underwriters with an opportunity to identify policies with potentially incorrect Coverage A.
Identifying errors in prefill data before binding improves the experience and avoids policy updates on the backend or at renewal. When Purefill detects an outlier while the customer is in the quote flow, it can be corrected quickly and easily compared to an endorsement or changed premium later.
A significant portion of carrier books have errors in key replacement cost drivers. When these are low, the carrier has an opportunity to identify them and capture a higher premium while making sure the customer has the appropriate Coverage A.
Replacement costs can be overestimated, resulting in a higher-than-necessary premium. These customers are likely to churn when another carrier gets the estimate correct and quotes a more appropriate premium.
A Deeper Look
Purefill Outlier Detection fuses high-resolution imagery with multiple sources of structured data to provide a confidence interval for the key drivers of replacement cost. These include:
Errors in living area create the largest mismatch in Coverage A. These may result from incorrectly using below-grade areas, accessory structures, or errors in public record data.
Quality grade rating is subjective and subject to misinterpretation during form fills. A consistent objective approach leads to more accurate assessments.
Partial stories are often overlooked and are not reflected in public records leading to inaccurate replacement cost estimates.
Request a personalized demo using information about your existing business.
Learn more about Purefill: Outlier Detection
Purefill provides dozens of data attributes, including many with unique confidence intervals and accuracy based on multi-source machine learning models. Purefill can enhance existing workflows or replace an existing prefill solution.
Purefill is the most accurate solution for home insurance data prefill. Combining high-resolution imagery and structured data with advanced machine learning, it provides both a more accurate prefill experience and a high fill rate.
More accurate prefill leads to fewer coverage changes and helps avoid surprised customers. Rather than having to change the replacement cost estimate after binding, carriers and insureds have access to accurate data inputs from the beginning.
More accuracy in key data points like living area leads to more accurate replacement cost estimates and Coverage A.
A Deeper Look
Ready for a complete prefill replacement that you can have confidence in? Purefill can offer 25+ key data elements for replacement cost estimation with large improvements in overall accuracy.
These enhanced models leverage machine learning to fuse and interrogate multiple data sources, and provide actionable confidence scores for all key elements.
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