The file is never quite complete.
Borrower documents arrive in fragments. Processors repeatedly chase missing information and reconcile versions.
AI FOR MORTGAGE AND LENDING
Move the file. Protect the margin.
Connect loan documents, conditions, and your existing systems. Put complete, review-ready files in front of the people making credit decisions.
See what’s possibleGood people.
Too much friction.
Start where work slows down, information gets lost, and margin slips away.
Conditions create a second queue.
A new upload means another manual comparison against requests, rules, and the original application.
Volume rises faster than capacity.
Re-keying and review preparation consume the hours your specialists need for exceptions.
Make the business case.
Start with one workflow. Put your numbers behind the opportunity.
$546,000Annual capacity value
8,400Hours returned / year
$486,000Value after program cost
810%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.
$11,094
Average production expense per originated loan in 2025
MBA Annual Mortgage Bankers Performance Report. Includes broader production costs, not just automatable work. ↗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 incoming documents, identify missing items, and prepare validated fields for your loan system.
Processors approve exceptions and required system updates.
Measure: Handling time per file; missing-document rate.
Example: 1,200 files/month × 35 minutes saved × $65/hour × 12 ÷ 60 = $546,000/year in capacity before program costs. Validate against your baseline.
Match supporting evidence to outstanding conditions. Prepare a clear satisfied-or-unresolved recommendation for the owner.
Authorized staff decide whether conditions are satisfied.
Measure: Minutes per condition; repeat requests.
Example: 2,500 conditions/month × 18 minutes saved × $65/hour × 12 ÷ 60 = $585,000/year in capacity before program costs. Validate against your baseline.
Reconcile application details and supporting evidence into a traceable review packet for the underwriter.
Credit decisions and fair-lending review stay with qualified people.
Measure: Preparation hours; returned-file rate.
Example: 600 files/month × 90 minutes saved × $90/hour × 12 ÷ 60 = $972,000/year in capacity before program costs. Validate against your baseline.
Compare approved disclosure fields, fees, and timelines. Highlight discrepancies for compliance review.
Compliance specialists validate applicable rules and remediation.
Measure: Review time; fee discrepancies.
Example: 1,500 reviews/month × 25 minutes saved × $75/hour × 12 ÷ 60 = $562,500/year in capacity before program costs. Validate against your baseline.
Assemble completed-file evidence, identify missing documents, and queue exceptions with source references.
Your quality team owns findings, escalation, and investor submissions.
Measure: QC handling time; defects per file.
Example: 1,000 files/month × 45 minutes saved × $75/hour × 12 ÷ 60 = $675,000/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 ↗newslink.mba.org · Reference 3 ↗