Prepare pay stub facts before a borrower’s income review
For Loan processors and income review teams
Give loan processors a predictable record for each submitted pay stub. Capture the employer, pay period and printed earnings while retaining the original document for the reviewer.
25 free credits/month · No card needed
See the workflow and review checks{
"employee_name": "Example Borrower — synthetic",
"employer_name": "Example Employer Inc.",
"period_start": "2026-07-01",
"period_end": "2026-07-15",
"pay_date": "2026-07-20",
"current_gross_pay": 3200,
"year_to_date_gross_pay": 44800
}Synthetic data, not a live extraction. Real responses also include confidence, validation, and result status.
When this work lands on your desk
A borrower uploads new pay stubs to satisfy an income documentation request, and a processor must determine what is ready for review.
Payroll systems use different labels for current earnings, deductions and year-to-date amounts. A current payment can be mistaken for a recurring monthly amount, while a cropped image can omit the period entirely. A dedicated intake schema makes those distinctions explicit before the data reaches your loan workflow.
Start with the documents you already collect
- Pay stub PDFs
- Images of employer-issued pay statements
- W-2 forms using a separate document schema
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
Your application links the result to the correct borrower. A processor checks document recency, employment consistency and required coverage; your own policy determines income calculations and loan system updates.
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 schemaStarter fields are optional until you choose which are required. Nested line items need a defined structure if you want validation of their individual values.
| Field | Type | What to capture |
|---|---|---|
employee_name | string | Name shown on the pay statement. |
employer_name | string | Employer as printed, before matching to your borrower record. |
period_start | date | Start date of this earnings period. |
period_end | date | End date of this earnings period. |
pay_date | date | Payment date, kept distinct from the pay period. |
current_gross_pay | currency | Gross earnings printed for this period. |
year_to_date_gross_pay | currency | Printed year-to-date gross amount. |
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
- Current and year-to-date columns transposed
- Bonus or overtime included without a separate breakdown
- Borrower and employer names that need resolution
- Pay dates mistaken for earnings period dates
Prove the fit on a representative batch.
Choose pay stubs from different payroll providers and pay frequencies. Check every date and amount against the source, including one bonus period and one cropped submission.
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 paid capacity when borrower submissions become routine. Additional schemas let the same intake team handle W-2s and bank statements without forcing unlike documents into one record.
For a tailored implementation
Discuss custom volume and schema assistance for multiple lending teams with distinct document requirements.
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 borrower income document intake
Does extraction verify employment?
No. It reads the submitted document. Independent employment verification and any borrower follow-up remain your team’s responsibility.
Will Dokyumi calculate qualifying income?
The proposed schema captures printed amounts. Your team applies the relevant income methodology and reviews variable compensation separately.
Can W-2s use the same workflow?
They can join the same customer-owned intake process, but use a separate W-2 schema so annual wages are not confused with current pay.
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.