Closing disclosure fee capture
For Mortgage closing document coordinators
Prepare loan identifiers, cash-to-close and itemized fees from a closing disclosure with fields you can edit. No disclosure compliance assessment.
25 free credits/month · No card needed
See the workflow and review checksInspect a document example.
Synthetic documents · Expected output- borrower name
- Example Borrower - synthetic
- property address
- 100 Example Way, Sample City, CA 00000
- loan reference
- DEMO-LOAN-0042
- closing date
- 2026-09-30
- loan amount
- 1250
- cash to close
- 1250
- fee rows
- [ { "description": "Example document line - original synthetic data", "payer_name": "Example Payer - synthetic", "amount": 1250, "payment_timing": "At closing" } ]
{
"borrower_name": "Example Borrower - synthetic",
"property_address": "100 Example Way, Sample City, CA 00000",
"loan_reference": "DEMO-LOAN-0042",
"closing_date": "2026-09-30",
"loan_amount": 1250,
"cash_to_close": 1250,
"fee_rows": [
{
"description": "Example document line - original synthetic data",
"payer_name": "Example Payer - synthetic",
"amount": 1250,
"payment_timing": "At closing"
}
]
}A complete synthetic document with the visible values shown below. Real responses also include model-reported confidence, validation and result status. Test your own file with this schema.
When this work lands on your desk
A closing disclosure arrives for mortgage closing document coordinators. The next step needs loan identifiers, cash-to-close and itemized fees, recorded under the correct document reference before the information enters your loan intake worksheet.
The same fee is split between borrower-paid and seller-paid columns. Preserve the payer on each row before a closing reviewer reconciles the totals. This schema keeps the source labels and values explicit so the reviewer can resolve the issue before downstream use.
Start with the documents you already collect
- Closing disclosure PDFs
- Scanned or photographed closing disclosure pages
PDF, JPEG, PNG, TIFF, or WEBP. Send one file per extraction call; create separate schemas when the document types need different fields.
Define the fields
Open the starter schema. Edit field descriptions, mark the values your process requires, and save it in your account.
Extract at intake
Send the document and saved schema slug to
POST /api/v1/extract, or collect the file through a branded upload site.Hand off reviewed data
After checking borrower name, property address, loan reference, your application maps the reviewed record into your loan intake worksheet. Keep the original document reference with the result. No disclosure compliance assessment. Dokyumi processes one file with a saved schema; your application owns record matching, business rules and updates to other systems.
A schema shaped around the next step.
Start with these fields, then adjust them to match your documents and destination system. The example above shows the data object you are asking Dokyumi to extract.
Download editable starter schemaChoose required fields for your process. The starter schema preserves any defined nested fields; refine line-item structures and test them on representative files.
| Field | Type | What to capture |
|---|---|---|
borrower_name | string | Borrower name exactly as printed in this closing disclosure. Preserve identifiers and qualifiers; do not look up or infer missing information. |
property_address | string | Property address exactly as printed in this closing disclosure. Preserve identifiers and qualifiers; do not look up or infer missing information. |
loan_reference | string | Loan reference exactly as printed in this closing disclosure. Preserve identifiers and qualifiers; do not look up or infer missing information. |
closing_date | date | Closing date explicitly printed in this closing disclosure. Keep it distinct from other dates; leave it empty if no calendar date is stated. |
loan_amount | currency | Loan amount explicitly labeled in this closing disclosure. Preserve its sign and stated currency; do not calculate a missing amount. |
cash_to_close | currency | Cash to close explicitly labeled in this closing disclosure. Preserve its sign and stated currency; do not calculate a missing amount. |
fee_rows | array | Every visible fee rows row in this closing disclosure. Preserve separate rows, labels and qualifiers; do not merge entries or reconstruct missing ones. |
Give exceptions a clear path to review.
Dokyumi returns a model-reported confidence map, validation details, and a completed or review status for produced results. Check required fields and your business rules before passing data onward.
A completed extraction describes the extraction result. Your application and reviewers own matching, approvals, routing, and updates to other systems.
Read the response contractChecks for this process
- The same fee is split between borrower-paid and seller-paid columns. Preserve the payer on each row before a closing reviewer reconciles the totals.
- Check closing date against their own printed labels; leave an unstated date empty instead of deriving it.
- Check loan amount, cash to close for signs, decimal placement and stated currency before any downstream calculation or posting.
- Compare every fee rows row with the source, including continuation pages, separate categories and repeated table headings.
Prove the fit on a representative batch.
Test closing disclosure examples from the layouts your team actually receives. Include the exception described here: The same fee is split between borrower-paid and seller-paid columns. Preserve the payer on each row before a closing reviewer reconciles the totals. Compare the extracted borrower name, property address, loan reference and any line rows with a reviewer's checked reference before enabling downstream actions.
Compare extracted values with a reviewer’s reference set. Record corrections, unresolved fields, review time, and credits used before connecting downstream actions.
The free plan’s credit limits apply. Assisted pilots are scoped with Dokyumi before work begins.
Expand when the next process is ready
Add recurring extraction capacity when closing disclosure intake becomes routine. Reuse this field contract for layouts that express the same information; create a separate schema when the next document has different fields or review requirements.
For a tailored implementation
Discuss document volume, page lengths and schema setup for mortgage closing document coordinators. Bring difficult closing disclosure samples and the review requirements your implementation must preserve.
Talk through document mix and volumeStart small. Add capacity as intake grows.
All self-service plans include the REST API, upload-site webhooks, custom confidence thresholds, and schema inference. Choose by volume, schema count, and upload sites.
Each document uses one credit per 5 pages, rounded up. An 8-page file uses 2 credits. Self-service files can be up to 50 pages; larger files, custom-built schemas, and additional volume are quoted through Enterprise. Full pricing details
Questions about closing disclosure fee capture
Which fields can I capture from a closing disclosure?
The starter schema contains borrower name, property address, loan reference, closing date, loan amount, cash to close, fee rows. Edit those definitions and choose your required fields before saving. Array fields include defined sub-fields so row values have a validation structure.
What needs review in closing disclosure fee capture?
The same fee is split between borrower-paid and seller-paid columns. Preserve the payer on each row before a closing reviewer reconciles the totals. Check the original source alongside model-reported confidence and validation output; completion status describes the extraction result, not the business decision.
How does this data reach your loan intake worksheet?
Save the schema, then send one supported PDF or image to POST /api/v1/extract with its slug. The response contains the data, model-reported confidence, validation and result status. Your application maps reviewed values to the destination; upload-site submissions can use a configured webhook. No disclosure compliance assessment.
How do I test this workflow on my own documents?
Start free with 25 credits per month, 2 schemas, and 1 branded upload site. The starter schema opens in the schema builder after sign-up. Edit the fields, save it, and upload a representative file through your account or the extraction API. Account documents are stored according to Dokyumi’s privacy policy; contact hello@dokyumi.com for deletion requests and retention requirements.