Case Studies
OZRA AI is currently in early access. The scenarios below are illustrative, modeled on common underwriting bottlenecks insurers bring to us, showing how OZRA AI's proprietary AI is designed to perform once deployed on a partner's own data.
Cutting a multi-week commercial underwriting queue to real time
The Challenge
A mid-size general insurer's commercial-lines desk routinely took two to three weeks to return a decision on new applications, causing brokers to walk time-sensitive quotes to faster competitors.
With OZRA AI
OZRA AI trained a custom risk model on the insurer's own historical claims and policy data, then layered real-time scoring directly into the existing quote workflow, with a full explainability trail for underwriters to review.
Replacing generic actuarial tables with a model built on real claims
The Challenge
A health MGA priced niche policies against generic industry actuarial tables that didn't reflect its own book, leading to inconsistent and often overly conservative pricing.
With OZRA AI
A model trained specifically on the MGA's own claims history replaced the generic table-based approach, giving compliance a clear, auditable reason behind every score.
Freeing underwriters from high-volume, low-value manual review
The Challenge
A motor insurer's underwriting team manually reviewed nearly every incoming policy, regardless of risk, which strained headcount and slowed down renewals at scale.
With OZRA AI
Real-time risk scoring auto-approved clearly low-risk applications instantly, routing only genuine edge cases to human underwriters for review.
These scenarios are illustrative, built from pilot modeling and common patterns reported by insurers we've spoken with. They are not audited results from a live customer deployment. Outcomes depend on each insurer's own data and use case.
Want to see this modeled on your own data?
Join the early access waitlist and we'll walk through what a custom OZRA AI model could look like for your book of business.
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