Give applicants their next steps before they leave the branch.
Your loan officer uploads the application files and adds their notes. In minutes, AI summarizes the file, flags what's missing, and drafts clear next steps to share while the applicant is still at the desk. Borrowers leave knowing exactly what comes next, loans close faster, and the same summary saves underwriting hours of review.
- Loan officers and branch lending staff
- Mortgage loan originators
- Loan processors and underwriters
- Branch and lending managers
Next steps in the meeting, not days later.
When an applicant leaves the branch without knowing what comes next, the file waits on a follow-up call about a missing pay stub or an unexplained deposit, and every call adds days. With MultiModel, your loan officer uploads the files and notes during the meeting and gets back a list of next steps: the documents still needed, questions to answer, and what happens next.
The time savings carry through to underwriting. Choose the loan type, whether a mortgage, HELOC, auto loan, or small business loan, and the summary follows the checklist for that product, so your underwriters start from a consistent, cited summary instead of re-reading the file.
- Give the applicant a list of next steps before they leave
- Flag missing documents and inconsistencies while there's still time to fix them
- Hand underwriting a cited summary of income, debts, and collateral
Claude
Internal
Upload the application files, choose the loan type, and add your notes. The assistant summarizes the applicant, income, debts, and collateral, flags missing documents and inconsistencies, and lists next steps to share with the applicant before they leave.
Pre-review a loan applicationInternal
Mortgage pre-review
Mortgage
Borrower changed jobs in March; new employer letter to follow. Wants to close before month end.
Applicant(s): Daniel James Whitford; Elena Marie Whitford
Product Interest: Mortgage — $393,750, fixed-rate first mortgage
Suggestion: Conditionally Approved
Approval Level: Manager (as entered on underwriting worksheet)
Gating Item: Obtain signed mortgage commitment package, title/tax/insurance confirmations, and down-payment proof from applicants/title company — unlocks final mortgage documentation and funding readiness.
Needs Verification
- Letter of employment and recent paystub for each applicant
- 2025 W-2s and federal tax returns for each applicant
- 90-day history for $131,250 down payment
- Title commitment and property tax certificate from the title company
- Homeowners insurance declarations page naming lender as mortgagee
- Signed credit life/disability insurance election or waiver
Basis for Suggestion
- Eligibility screen — no undischarged bankruptcy, age-of-majority issue, citizenship/permanent residency issue, or litigation disclosed (Qualifications/Conditions for Borrowing)
- LTV — 75.00% on $525,000 value; within 80% conventional maximum without mortgage insurance (LOAN / VALUE RATIO)
- Valuation — 60%-80% LTV requires a current appraisal; appraisal dated 07/15/2026 noted, confirm on file (Location and Marketability)
- Debt service — housing ratio 26.83% and DTI 31.76%, applicant-supplied as entered; within 28%/43% mortgage-origination maxima (Debt-to-Income Calculation)
- Credit — FICO 742 / 715, applicant-supplied as entered; above 620 threshold for conventional mortgage (Credit Requirements)
- Documentation — commitment, title, insurance, TRID disclosures, and employment verification required before closing (ALL MORTGAGES)
Approval Note: Preliminary suggestion only, based on self-reported and applicant-supplied figures. Final determination depends on policy-required verification of income, down payment, valuation/title position, tax status, and insurance/documentation completion.
From your documents to a sourced draft.
The assistant uses retrieval-augmented generation (RAG): it finds the relevant passages in the content you approve, then answers from them with citations your team can check.
Upload during the meeting
Your loan officer chooses the loan type, uploads the application files, and adds notes on anything the documents don't show.
Walk the applicant through next steps
In minutes, the assistant lists missing documents, open questions, and what happens next. The loan officer confirms the list and goes through it with the applicant before they leave.
Send a cited summary to underwriting
The same run produces a summary of the applicant, income, debts, and collateral, with every figure linked to its source document.
What your team asks, and what comes back.
Mortgage
The request. A borrower meets a loan officer to apply for a purchase mortgage, with two years of W-2s, recent pay stubs, bank statements, and an appraisal. They changed jobs three months ago.
What the assistant returns. A summary of income, assets, debts, and the property, plus next steps for the borrower: send the new employer's offer letter and a short explanation of a large recent deposit.
Why it matters. The borrower leaves knowing exactly what to send, instead of getting a call a week later, and underwriting receives a complete file sooner.
HELOC
The request. An existing customer comes into the branch to apply for a home equity line of credit to fund a renovation.
What the assistant returns. A summary of the customer's income, existing mortgage balance, and estimated combined loan-to-value, plus next steps: the documents still needed and what happens between now and closing.
Why it matters. The customer gets a clear picture of what comes next during the visit, and the key figures are laid out the same way on every file.
Small business loan
The request. A business owner meets a lender to request a small business loan, bringing business tax returns, a year-to-date P&L, bank statements, and a personal financial statement.
What the assistant returns. A summary of revenue trends, cash flow, and existing debt, with differences between the tax returns and bank deposits flagged, and a list of questions to ask while the owner is still in the meeting.
Why it matters. Open questions are answered in one meeting instead of over several rounds of email, and credit analysts start from a consistent summary.
Measurable time back, with the controls already in place.
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See the solutionDiscover real uses for AI at your institution.
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Talk to us about Loan Application Pre-Review
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AI for loan application pre-review: FAQ
Straight answers for CIOs, CISOs, and compliance officers evaluating AI.
Talk to usCan your loan officers really give next steps before the applicant leaves?
Yes. The summary and next steps come back in minutes, while the applicant is still at the desk: the documents still needed, questions to answer, and what happens next in the process. Your loan officer reviews the list before sharing it.
Does the AI make lending decisions?
No. The next steps cover documents and process, not a credit decision. Your loan officers and underwriters review the summary, check the cited sources, and make the decision under your credit policy.
Which loan types does it cover?
The example structured AI assistant covers mortgages, HELOCs, auto loans, and small business loans, each with its own checklist. Your team can edit the checklists or add products, such as commercial real estate or personal loans, to match your credit policy. See AI assistants for how assistants are built and shared.
How does it save time after the applicant leaves?
The same run produces a one-page summary for underwriting, with every figure cited to the document it came from, such as a pay stub, tax return, or bank statement. Your underwriters confirm the figures instead of re-reading the whole file, and fewer files come back for missing documents.
How are your loan officer's notes used?
Notes add context the documents don't show, such as a recent job change or a document that is still to come. The assistant includes them in the summary but keeps them separate from what the documents say, so underwriters can tell the two apart.
Is applicant data sent to OpenAI or other model providers?
Documents are processed inside your Azure tenant and are never copied to a third-party service. Account numbers, SSNs, and other PII are redacted on the employee's device before a prompt is sent, and prompts are excluded from model training across all AI providers. See data loss prevention.
See next steps drafted while the applicant is still in the branch.
Bring a sample loan file. We'll show you a cited summary and a list of next steps for the applicant, generated in minutes inside your perimeter.