Give MCA analysts structured statement data when a deal arrives
For MCA brokers and funding operations teams
Turn a merchant’s submitted bank statement into a consistent intake record. Define the fields your analyst actually reviews, then extract each file through the shared API using that schema.
25 free credits/month · No card needed
See the workflow and review checks{
"account_holder": "Example Merchant LLC — synthetic",
"account_last_four": "0042",
"period_start": "2026-07-01",
"period_end": "2026-07-31",
"opening_balance": 8200,
"closing_balance": 9450,
"transactions": [
{
"date": "2026-07-08",
"description": "Example customer receipt",
"amount": 2000,
"direction": "credit"
},
{
"date": "2026-07-10",
"description": "Example supplier payment",
"amount": 750,
"direction": "debit"
}
]
}Synthetic data, not a live extraction. Real responses also include confidence, validation, and result status.
When this work lands on your desk
A broker submits a funding request with several months of bank statements, and operations must prepare the deal for an analyst.
Different bank layouts bury statement dates, opening balances and transaction descriptions in different places. Re-keying those details delays the first review. Extraction can prepare the visible facts; determining eligible revenue, identifying transfers and calculating offer terms remain separate underwriting tasks.
Start with the documents you already collect
- Business bank statement PDFs
- Scanned statement pages
- Separate statements for additional merchant accounts
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 associates each result with the merchant and statement month. Your team checks period coverage, reconciles balances and applies its own transaction categorization before creating an underwriting worksheet.
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 |
|---|---|---|
account_holder | string | Business name printed on this statement. |
account_last_four | string | Only the visible last four digits; preserve leading zeros. |
period_start | date | Opening date of the statement period. |
period_end | date | Closing date of the statement period. |
opening_balance | currency | Printed opening balance, not a calculated estimate. |
closing_balance | currency | Printed closing balance for reconciliation. |
transactions | array | Visible rows with date, description, amount and debit or credit direction. |
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
- Missing statement pages or overlapping periods
- Repeated account numbers across different businesses
- Debit and credit signs lost in a scan
- Transfers, reversals and ambiguous transaction descriptions
Prove the fit on a representative batch.
Evaluate a representative set from several banks, including a poor scan and a merchant with two accounts. Compare extracted rows and balances with an analyst’s checked worksheet.
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
Move to a paid plan when recurring deal volume exceeds evaluation capacity. Add schemas for applications and supporting income documents as the intake process grows.
For a tailored implementation
Discuss custom volume and schema setup when many broker channels need their own intake portals or statement formats.
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 mca bank statement intake
Does this calculate an MCA offer?
No. It prepares document data. Revenue classification, affordability analysis and funding decisions belong to your underwriting process.
Can several months be submitted together?
Each API call processes one supported file. Your application tracks the requested months and joins the resulting records for review.
Does a completed result confirm the statement is authentic?
No. Extraction status reports processing and schema checks; it does not establish authenticity or verify account ownership.
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.