Prepare loan documents as complete, legible digital files, then verify every extracted field against the original before it enters a lending workflow. Start with the lender’s checklist, capture every page without glare or cropping, confirm the AI service’s supported forms, languages and file limits, and establish how submitted personal information is accessed, retained and corrected. AI can locate and structure fields; it does not replace a human review or a creditor’s legal duties.
Start with the exact loan-document checklist
There is no universal set of documents for every loan. Requirements change with the product, borrower profile, lender and jurisdiction. Ask the lender or broker for the checklist that applies to the actual application before scanning anything. A document-selection workflow may vary by loan product, applicant type and application level; Salesforce’s digital-lending documentation describes this kind of decision matrix.
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U.S. conventional-mortgage example
For a U.S. conventional mortgage file, Fannie Mae’s application-package guidance calls for a complete, signed and dated final Uniform Residential Loan Application (Form 1003), plus the supporting materials used to substantiate the borrower’s application. Copies may be made with a scanner or camera, including borrower-provided photos or scans. That guidance is specific to the mortgage context. Do not use it as a checklist for an auto, personal, business or non-U.S. loan.
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Build a document inventory
Before capture, make a simple inventory with the applicant or loan number, document type, date and page count. Mark whether each item is an original, a copy, a photograph or a generated statement. This gives reviewers a way to identify a missing page and gives the extraction system a meaningful source label. Keep documents from different applicants and applications in separate folders or workspaces.
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Capture pages so the text and evidence are readable
Extraction quality depends first on the input. Microsoft’s mortgage-model guidance says, “For best results, provide one clear photo or high-quality scan per document.” That is product-specific advice, not a guarantee or a universal rule for every vendor.
Use a scanner or a phone camera
- Scanner: A flat scan is useful when pages are loose, the document contains dense tables, or you need consistent orientation. A scanner creates a digital copy; it does not itself perform AI extraction.
- Phone camera: A camera is acceptable when the page is held flat, fully illuminated and parallel to the lens. Use the highest practical image quality and review the result at 100% zoom before uploading.
- Paper originals: Remove clips and folds that cover text. Flatten creases without damaging signatures or security features.
Keep every material detail in frame
Include all four page edges when possible. Signatures, dates, check boxes, totals, footnotes, account numbers and table headers can be as important as the largest printed fields. Do not crop a page to its center or combine unrelated pages into a single image. Retake pages with glare, motion blur, shadows, clipped corners, skew, dark backgrounds or reflections from plastic sleeves.
Do not assume a universal resolution
The sources do not establish one industry-wide dots-per-inch requirement or a guaranteed accuracy rate. A practical test is visual: if a reviewer cannot comfortably read the smallest relevant print and distinguish a handwritten mark from the paper, the image is not ready. Keep the original capture even when you create a corrected copy, so a reviewer can inspect what was submitted.
Check the AI service’s input rules before uploading
Input requirements are service- and model-specific. Verify them for the selected tier on the day of upload rather than relying on a generic OCR checklist.
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Microsoft mortgage-model example
Microsoft’s current Document Intelligence mortgage documentation describes English-language extraction for several named mortgage forms: the Uniform Residential Loan Application (Form 1003), Uniform Residential Appraisal Report (Form 1004), Verification of Employment (Form 1005), Uniform Underwriting and Transmittal Summary (Form 1008), and Closing Disclosure. It lists PDF and common image formats for its prebuilt models. A different model, language or lender integration may support a different set.
| Input rule | Published Microsoft example | Qualification |
|---|---|---|
| Language and form coverage | English mortgage extraction for Forms 1003, 1004, 1005, 1008 and Closing Disclosure | Confirm that the exact form edition and language are supported by the model you selected. |
| File formats | PDF and common image formats are listed for prebuilt models | Check the current model page for the accepted extensions and any integration-specific restrictions. |
| PDF/TIFF page count | Up to 2,000 pages on the paid tier | This is a vendor limit, not an industry standard; other tiers or services may differ. |
| File size | Up to 500 MB for the paid S0 tier | Confirm the tier attached to your account before submitting a large file. |
| Image dimensions | Between 50 × 50 and 10,000 × 10,000 pixels | Dimensions outside that range can be rejected even when the page looks readable. |
| Minimum text height | 12 pixels for a 1,024 × 768 image, described as about 8-point text at 150 dpi | This is a service guideline; it does not establish a universal scan resolution. |
| Password-protected PDFs | Must be unlocked before submission | Unlock a working copy only in an approved environment and preserve the protected original. |
Limits and supported forms can change. Confirm the model, region, plan tier and current documentation immediately before an automated run.
Prepare files and labels for reliable handoff
Use descriptive, stable names
Give each file a name that identifies the application, document type and date without exposing more personal information than staff need. For example, a controlled internal convention might use Application123_Form1003_2026-10-16.pdf. Do not put a full Social Security number or other unnecessary sensitive value in a filename. If your organization has a naming standard, follow it consistently.
Keep page order and document boundaries
Preserve the original order of multipage documents. If a PDF contains several document types, split it only when the service or review process requires separate files, and record the split relationship. Never let a continuation page appear to be a new document. A cover sheet can identify the document type, but it should not replace the source pages.
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Retain the source alongside the result
Store the original scan or photo and the extracted record under a common identifier. Record the model name, run date and any confidence or exception information the service provides. The source-to-field relationship is essential when a value is challenged or a reviewer must correct an extraction.
Run extraction as a reviewable workflow
- Check completeness: Compare the inventory with the files selected for upload. Confirm signatures, dates, attachments and continuation pages are present.
- Validate compatibility: Check language, document class, file extension, password status, page count, size and image dimensions against the selected model.
- Submit the smallest useful unit: Follow the service’s document boundary guidance. For Microsoft’s mortgage model, its stated best-results advice is one clear photo or high-quality scan per document.
- Inspect the returned fields: Open the source next to the extracted result. Do not accept a field merely because the system returned a value.
- Verify high-impact values first: Compare names, loan or account identifiers, dates, amounts, totals, employment details and handwritten entries character by character.
- Resolve exceptions: If a value is unclear, mark it for human review and recapture the page when possible. Never invent a missing digit or silently choose between conflicting source values.
- Record corrections: Keep an audit note showing the original extracted value, the corrected value, who made the correction and which source page supports it.
- Transfer only after approval: Move verified fields into the loan system only after the designated reviewer has completed the required checks.
Handwriting and totals deserve extra scrutiny
Handwritten figures, check marks and overwritten dates are common sources of uncertainty. A typed total can also be wrong in the source, so compare it with the component figures when the workflow requires arithmetic validation. Extraction software can report what it sees; it does not establish that the applicant’s statement is complete, authentic or internally consistent.
Protect personal information before using an external AI service
Loan files contain sensitive personal and financial information. Before uploading, establish whether the service is necessary for the task and whether it has been tested for the intended use. The Australian Office of the Information Commissioner (OAIC) recommends ongoing due diligence and lifecycle review rather than a one-time vendor decision.
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- What input and output data can the provider’s staff, contractors or subprocessors access?
- How long are files, extracted fields, logs and backups retained, and can they be deleted on request?
- Are submitted documents used to train or improve a general model, or limited to the contracted service?
- Where are data stored and processed, and which jurisdiction’s privacy and financial-services rules apply?
- What authentication, access controls and security commitments apply to the account and its integrations?
- How can an applicant or authorized staff member review, correct or contest an extracted value?
- What happens when the service is unavailable, changes its model or ends the account?
Minimize exposure without damaging the record
Use the least data necessary for the stated extraction task, but do not redact a field that the model or reviewer must verify. If a test environment is needed, use synthetic or properly de-identified documents rather than real loan files. Keep production and test workspaces separate, restrict access by role and define a deletion schedule that matches legal and operational retention duties.
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Separate extraction from the credit decision
Finding fields in a document is not the same as approving or denying credit. A lending workflow must preserve the source evidence and a human route for resolving errors, even when an automated model supplies structured data.
“Creditors who use complex algorithms, including artificial intelligence or machine learning, in any aspect of their credit decisions must still provide a notice that discloses the specific principal reasons for taking an adverse action.”
This is the Consumer Financial Protection Bureau’s statement in Consumer Financial Protection Circular 2022-03 for U.S. creditors. An opaque model output is not, by itself, a specific principal reason. If extracted income, debt, employment or collateral data contributes to an adverse action, the creditor must still meet the applicable notice and explanation requirements.
Regulatory obligations differ outside the United States. Use the rules that apply to the lender’s location, product and decision process, and document who reviewed the source data.
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Compare extraction options on more than OCR accuracy
No broadly applicable independent accuracy statistic is established by the cited sources. Compare services against the real workflow instead of relying on a single accuracy claim.
| Evaluation axis | Questions to ask | Evidence to request |
|---|---|---|
| Document coverage | Does the model support the exact forms, editions and document classes in this loan process? | A current supported-form list and handling for unknown or mixed documents. |
| Capture and format compatibility | Can it process the PDF, photo and scan formats applicants actually submit, in the required languages? | Input specifications and examples of accepted and rejected files. |
| Technical limits | What are the page, file-size, image-size, password and minimum-text rules, and do they vary by plan? | Tier-specific limits and an error-handling procedure. |
| Privacy and security | Who can access inputs and outputs, where are they stored, and how are retention and deletion controlled? | Contract terms, privacy documentation and a documented data-flow review. |
| Human review and traceability | Can staff view the source beside each field, correct uncertainty and preserve an audit trail? | Reviewer screens, confidence or exception handling and exportable logs. |
| Ongoing fitness | Has the model been tested for this intended use, and is accuracy monitored after forms or applicant populations change? | Validation results for your document mix, monitoring thresholds and a change-management process. |
Fix common upload and extraction failures
The service rejects the file immediately
- Check the extension and whether the selected model accepts it.
- Remove the PDF password from an approved working copy.
- Compare page count, file size and image dimensions with the service tier limits.
- Confirm that the file is not a zero-page export or a corrupted scan; reopen it locally before retrying.
The result is missing fields
- Inspect the source at full size for glare, blur, shadows, clipped edges or tiny text.
- Verify that the document class and language match the model.
- Recapture the affected page rather than repeatedly submitting the same unreadable image.
- Route handwriting, stamps, overwritten values and unusual layouts to a human reviewer.
Values conflict across pages
Do not merge conflicting names, dates or amounts automatically. Identify which page is authoritative under the lender’s procedure, ask for clarification when required and record the resolution with both source references.
A page is missing after processing
Compare the returned page or field list with the original inventory. Re-upload the complete document or the missing page according to the service’s document-boundary rules, then repeat the completeness check before transfer.
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A loan file is ready for AI extraction when the lender’s applicable checklist is complete, every page is legible and in order, the selected model accepts the form and file, the source is retained, and a named reviewer can compare each consequential field with its originating page. It is ready for downstream lending use only after that review, privacy controls and any required regulatory explanations are in place.
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