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Case Architecture

How OZRA AI is designed to work inside real underwriting teams. The scenarios below are illustrative, modeled on common bottlenecks insurers bring to us, and show how OZRA AI's proprietary AI is designed to perform once deployed on a partner's own data.

Large commercial warehouse with stocked shelving

warehouseGeneral Insurance

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.

See what changedexpand_more

What changed

  • checkBroker submissions for warehouses, factories and retail units are read and structured automatically, without re-keying.
  • checkClean, in-appetite risks receive a score and suggested rate as soon as the submission lands.
  • checkSenior underwriters only see the accounts that sit outside guidelines, with the reasons already written down.
Data used
8 years of commercial property policies, claims and broker submissions
Modeled timeline
30-day pilot on one region, then rollout to the full commercial book
2–3 weeks< 60 secmodeled decision time
ManualAuto-triagedlow-risk applications
Doctor consulting with a patient in a clinic

health_and_safetyHealth Insurance

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.

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What changed

  • checkPricing reflects how the MGA's own members actually use care, by age band, region and plan design.
  • checkActuaries can compare the new model side by side with the old tables before switching anything on.
  • checkEvery premium comes with a written explanation that the compliance team can file with the policy.
Data used
5 years of member claims, utilisation and plan data
Modeled timeline
Parallel run against existing tables for one renewal cycle
Generic tablesCustom modelpricing basis
OpaqueFully explainablescore reasoning
Damaged car after a collision

directions_carMotor Insurance

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.

See what changedexpand_more

What changed

  • checkRenewals for drivers with clean histories are approved instantly, without waiting in a queue.
  • checkUnusual combinations of vehicle, driver and usage are flagged for a human review with the key factors highlighted.
  • checkThe underwriting team's time shifts from routine checks to fleets and complex commercial accounts.
Data used
Policy, claims and vehicle data across private and fleet portfolios
Modeled timeline
Phased rollout, starting with private-car renewals
Near 100%Risk-based routingmanual review rate
Slow renewalsInstant approvalsfor low-risk policies

How each scenario was modeled

Every case follows the same four steps, whatever the line of business.

  1. Step 1

    Understand the book

    We start with the insurer's own questions: which lines are slowest, where pricing feels least reliable, and which decisions take up the most underwriter time.

  2. Step 2

    Prepare the data

    Historical policies and claims are cleaned, mapped and linked so that every past decision can be matched with what actually happened afterwards.

  3. Step 3

    Train and compare

    A custom model is trained on that history and tested against decisions the insurer has already made, so improvements are measured, not assumed.

  4. Step 4

    Fit into the workflow

    Scores, explanations and referral rules are delivered into the tools underwriters already use, starting with a limited pilot before wider rollout.

The architecture behind every case

Each deployment is built from the same four layers. Insurers can start with one layer, such as scoring on top of their existing rating engine, and add the rest as confidence grows.

database

Data layer

Connects to policy, claims, broker and third-party data, and keeps it inside the insurer's own environment.

model_training

Model layer

Custom risk models for each line of business, retrained as new claims experience comes in.

fact_check

Decision layer

Scores, suggested rates and referral rules that turn model output into clear underwriting actions.

policy

Governance layer

Plain-language explanations, version history and an audit trail for every decision the platform supports.

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?

Apply for the pilot and we'll walk through what a custom OZRA AI model could look like for your book of business.

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