Policy Brain reads policy, coverage and rating data the way an underwriter does, so your team stops decoding fields by hand before data reaches PolicyCenter.
The Problem
Policy Data Has to Be Interpreted Before It Can Be Used
Every route into PolicyCenter starts with someone working out what the policy data means. That interpretation is slow, manual and repeated for each new source.
Legacy Fields Have No Dictionary
Legacy policy systems often lack data dictionaries, forcing engineers to inspect code and tables manually to understand fields before writing transformations.
Systems Describe the Same Policy Differently
Coverage, rating factors, and state requirements vary across systems, requiring manual mapping before policies can be compared or loaded.
Cleanup Comes Before Progress
Guidewire notes that extra data cleansing passes increase costs and delays, as teams spend weeks standardizing coverage codes and rating structures before policy validation.
The Solution
How does InsOps Help?
How Yazh (Yaal) Moves Submissions Into PolicyCenter
Reads Policy Fields and Codes
Interprets insurance-specific fields, codes and terminology across policy data, so no one decodes them manually.
Understands Coverage, Rating and State Rules.
Interprets underwriting logic across policy, rating, coverage and state-specific requirements.
Matches the Same Policy Across Systems
Recognizes when different systems describe the same policy, coverage or rating requirement, which reduces manual mapping.
Suggests Field Mappings
Identifies how source and target fields relate and generates an initial mapping for your team to review.
Works With Messy Data
Handles fragmented, inconsistent policy data without first standardizing coverage codes and rating factor structures.
Writes Transformation and Validation Rules
Generates the transformation rules, validation steps and reconciliation logic that normally take engineering effort.
FAQ