{}

Income & Asset Verification: Automate Lending Document Intake

Pay stubsBank statementsW-2sTax returns (1040)Employment verification letters

The problem

Underwriters and loan processors spend hours reading and re-keying figures from stacks of borrower-submitted pay stubs and bank statements. It slows time-to-close, and it's exactly the kind of manual transcription where a single mis-keyed digit compounds into a bad income calculation.

How Dokyumi solves it

Pay stubs, bank statements, and tax returns don't look anything alike, so the right move is one schema per document type — each with its own endpoint. A pay-stub schema pulls gross/net pay and YTD figures; a bank-statement schema pulls balances and transactions; a tax-return schema pulls AGI and withholding.

Borrowers or loan officers submit documents — directly through the API, or through a white-label upload portal you brand for applicants — and Dokyumi returns structured income/asset data with confidence scores you can drop straight into your LOS or underwriting checklist.

Example schema and result

A real schema shape for lending & underwriting documents, and the JSON you get back from one extraction.

Schema — GET /api/v1/schemas

{
  "slug": "pay-stub-parser",
  "name": "Pay Stub Parser",
  "description": "Extracts income figures from employee pay stubs for income verification",
  "ocr_mode": "standard",
  "fields": [
    {
      "key": "employer_name",
      "type": "string",
      "required": true
    },
    {
      "key": "employee_name",
      "type": "string",
      "required": true
    },
    {
      "key": "pay_period_start",
      "type": "date",
      "required": false
    },
    {
      "key": "pay_period_end",
      "type": "date",
      "required": false
    },
    {
      "key": "gross_pay",
      "type": "currency",
      "required": true
    },
    {
      "key": "net_pay",
      "type": "currency",
      "required": true
    },
    {
      "key": "ytd_gross",
      "type": "currency",
      "required": false
    },
    {
      "key": "federal_tax_withheld",
      "type": "currency",
      "required": false
    }
  ]
}

Result — POST /api/v1/extract

{
  "id": "ext_ln4p9x",
  "status": "completed",
  "schema": "pay-stub-parser",
  "data": {
    "employer_name": "Meridian Logistics LLC",
    "employee_name": "J. Alvarez",
    "pay_period_start": "2026-05-16",
    "pay_period_end": "2026-05-31",
    "gross_pay": 3120,
    "net_pay": 2384.11,
    "ytd_gross": 15600,
    "federal_tax_withheld": 412
  },
  "confidence": {
    "gross_pay": 0.99,
    "net_pay": 0.98,
    "ytd_gross": 0.95
  },
  "meta": {
    "processing_time_ms": 990,
    "page_count": 1,
    "ocr_cached": false
  }
}

How it works

1

Create one schema per document type

Pay stubs, bank statements, and tax returns each get their own schema and endpoint.

2

Collect documents from borrowers

Upload directly via your loan portal, or hand borrowers a white-label Dokyumi upload page.

3

Extract structured income/asset data

Each document returns validated JSON with a per-field confidence score.

4

Feed your underwriting workflow

Use the API response or a webhook to populate your LOS, spreadsheet, or decision engine.

Lending & Underwriting FAQ

Does Dokyumi make the underwriting decision or verify income automatically?+
No. Dokyumi extracts the fields you define into structured data — the underwriting decision, DTI calculation, and any policy logic stay in your system or with your underwriters.
Can Dokyumi detect altered or fraudulent documents?+
No — it's a data-extraction pipeline, not a fraud- or authenticity-detection tool. Treat extracted figures like any borrower-submitted data, subject to your existing verification process.
Should pay stubs and bank statements share one schema?+
No — define a separate schema for each document type. The fields are different enough that a schema-per-doc-type keeps confidence scoring and validation accurate.
Can borrowers upload documents themselves?+
Yes, via a white-label portal (a branded upload page) you set up — available on every plan, with more portals and custom domains on Growth and Enterprise.
Is this suitable for handling tax returns and other sensitive documents?+
You control exactly which fields your schema captures. See our security page for how we handle encryption, retention, and access controls.

Related use cases

Accounts Payable

Extract vendor, line-item, and total data from invoices and receipts — no template building, no AWS Textract setup.

Tax Prep

Turn client-submitted W-2s, 1099s, and 1040s into structured data — or hand clients a branded upload portal for intake season.

Real Estate & Leasing

Pull lease terms, applicant income, and insurance details out of leases, applications, and dec pages — no per-property template work.

Stop re-keying pay stubs by hand.

100 free extractions every month — no credit card, no AWS account, no setup hell.