Sovwizard is an Excel‑integrated platform that automatically validates and scrubs Schedule of Values (SOV) data in seconds, giving underwriters full control over modeling assumptions. It provides transparent field‑mapping visualizations and one‑click loss modeling to generate catastrophe‑ready insights within minutes, all without requiring workflow changes or additional software.
Funding
Funding not disclosed
Founders
Product
Problem
Insurance underwriters often receive unstructured, error‑prone Schedule of Values (SOV) data that requires extensive manual cleaning and opaque modeling tools, leading to delays, hidden assumptions, and inaccurate loss estimates.
Solution
Sovwizard provides an Excel‑integrated platform that automatically validates and scrubs SOV data in seconds, allowing underwriters to control every modeling assumption. The tool offers transparent field mappings and built‑in visualizations so each transformation is visible and reviewable before loss modeling. Direct, one‑click modeling generates catastrophe‑ready loss insights within minutes, eliminating the need for separate data‑preparation pipelines. By keeping the workflow inside familiar Excel environments, Sovwizard avoids costly process overhauls while delivering trustworthy results that reduce downstream surprises and improve combined ratios.
Target Audience
Primary customers are property and casualty insurance underwriters and pricing analysts who need rapid, reliable SOV cleaning and loss modeling within their existing Excel‑based workflows.
Features
- One‑click validation that flags and corrects data issues instantly
- Transparent field mapping view showing every transformation and assumption
- Built‑in visualizations for real‑time review of cleaned data
- Direct loss modeling from cleaned SOVs, delivering CAT‑ready insights in minutes
- Fully operates within Excel, requiring no additional software or workflow changes
- User‑controlled modeling parameters to prevent black‑box automation
- Fast performance: data cleaning in ~1 second, modeling in ~2 minutes