Every document looks different.
Submissions and claim evidence arrive in inconsistent formats that require repeated handling.
AI FOR INSURANCE
Faster operations. Better-informed decisions.
Connect underwriting, claims, and policy servicing. Remove document handling and repeated handoffs while keeping consequential decisions with your experts.
See what’s possibleGood people.
Too much friction.
Start where work slows down, information gets lost, and margin slips away.
Exceptions become the workflow.
Missing information and cross-system checks create long queues between teams.
Demand rarely arrives evenly.
Renewal peaks and claims surges put pressure on the same specialist capacity.
Make the business case.
Start with one workflow. Put your numbers behind the opportunity.
$765,000Annual capacity value
9,000Hours returned / year
$705,000Value after program cost
1,175%Illustrative annual ROI
Illustrative model, not a forecast or Mason pricing. Capacity value = monthly volume × minutes saved ÷ 60 × hourly cost × 12. ROI = (capacity value − annual program cost) ÷ annual program cost. Realized savings depend on adoption and how capacity is redeployed. Include implementation, review, and operating costs in your estimate.
75%
Faster claims cycles reported by an automation vendor
Automat public benchmark. Vendor-reported, not independently verified or a Mason result. ↗Workflows worth
transforming.
Explore the work, the human decision points, and the value to measure.
Each value below is an illustrative annual capacity scenario. Open a workflow for its assumptions. Scenarios may overlap and should not be added together.
Organize submission evidence and loss history into a consistent file, with missing information flagged.
Underwriters own appetite, pricing, and acceptance decisions.
Measure: Preparation time; incomplete submissions.
Example: 1,000 submissions/month × 45 minutes saved × $85/hour × 12 ÷ 60 = $765,000/year in capacity before program costs. Validate against your baseline.
Structure incoming evidence, check completeness, and route the file to the appropriate claims team.
Adjusters decide coverage, reserves, and settlements.
Measure: Intake time; time to first review.
Example: 4,000 claims/month × 20 minutes saved × $65/hour × 12 ÷ 60 = $1,040,000/year in capacity before program costs. Validate against your baseline.
Prepare requested changes and renewal packets from approved policy information.
Authorized staff approve policy changes and communications.
Measure: Handling time; repeat servicing work.
Example: 5,000 requests/month × 12 minutes saved × $60/hour × 12 ÷ 60 = $720,000/year in capacity before program costs. Validate against your baseline.
Reconcile broker submissions, status requests, and supporting records into a shared service queue.
Distribution teams approve business commitments.
Measure: Response effort; pending-request age.
Example: 2,500 requests/month × 18 minutes saved × $65/hour × 12 ÷ 60 = $585,000/year in capacity before program costs. Validate against your baseline.
Assemble supporting data and evidence for review, making discrepancies traceable to their source.
Compliance specialists approve filings and regulatory interpretations.
Measure: Preparation hours; unresolved data gaps.
Example: 200 packages/month × 150 minutes saved × $90/hour × 12 ÷ 60 = $540,000/year in capacity before program costs. Validate against your baseline.
Your business.
Your control.
The same security foundation across Mason Platform and Mason Transformation.
Explore securityYour data stays yours+
Your data and processes are not used to train or update AI models.
Access shaped around your work+
Define the applications, information, and people in scope. Apply least privilege and strong authentication.
Deployment that fits your environment+
Work with your security and IT teams to agree on infrastructure, data boundaries, ownership, and ongoing support.
Find your next
AI advantage.
Bring us the workflow that costs too much, takes too long, or keeps falling between systems.
Build your AI edgeSources and assumptions +
Industry references inform the workflow themes. Third-party benchmarks are attributed above and are not Mason performance claims. Workflow values are independent, illustrative calculations.
runautomat.com · Reference 1 ↗worktrace.ai · Reference 2 ↗