Every NSF In The Bank Statements, Caught Before The File Goes To The Funder.
A merchant's bank statements tell you almost everything you need to know before you submit — if someone actually reads all three to six months of them, page by page, catching every NSF, every recurring debit that looks like a competing advance, every deposit pattern that doesn't match the stated revenue. Most shops skim. Scaler reads every page, extracts NSF count and dates, flags recurring debits and deposit irregularities, and hands your underwriter a clean summary before the package ever goes to a funder — so the surprises show up on your desk, not the funder's.
AI document extraction on financial statements is a mature, widely deployed capability — the same category of technology banks and lenders already use for automated statement analysis in underwriting. Applying it to MCA bank statement review is a direct fit for a process that's inherently visual pattern-matching across many pages, which is exactly where manual review is weakest under time pressure.
General AI document-processing capability — not a Scaler client result.
Nobody has time to read six months of bank statements line by line — but the NSFs are in there.
- 01Manually scanning 3–6 months of bank statements for NSF activity across a busy submission queue means the fifth or sixth page of a long statement gets skimmed, not read.
- 02Recurring debits that look like payments on an existing MCA position get missed when there's no time to trace every line item back to a pattern.
- 03Deposit totals that don't line up with the revenue the merchant stated on the application surface late — sometimes only after the funder's own underwriting catches it and kicks the file back.
- 04A file that goes to a funder with an NSF problem you missed costs you the relationship, not just the deal — funders remember which ISOs send clean paper.
- 05Manual bank statement review is one of the slowest steps in getting a package submission-ready, which means good deals sit in queue behind the review, not the underwriting decision.
Live in days, not months.
Ingest
Upload the merchant's bank statements — PDF, scanned, or portal export — in whatever format they came in from the merchant or your submission portal.
Extract
AI reads every page and extracts NSF events with dates and fees, recurring debits (flagged as likely existing-position payments where the pattern matches), and daily/average deposit totals by month.
Flag
Anything past the thresholds you set — NSF count, a recurring debit that looks like a stacked position, a deposit pattern that doesn't match the stated revenue — is flagged with the specific statement page it came from.
Summarize
Your underwriter gets a one-page summary per merchant: NSF count and trend, suspected existing positions, and deposit consistency — before the file goes into your submission package.
Underwriting gets the full picture before the funder does — every time.
- Every page of every statement actually gets read, not skimmed — the fifth page of a long statement gets the same scrutiny as the first.
- NSF count, dates, and trend direction (improving or worsening) surface automatically, instead of depending on whoever reviewed the file catching them by hand.
- Recurring debits that look like an existing MCA position get flagged before you submit, so stacking risk shows up on your desk, not the funder's declination.
- Deposit-to-stated-revenue mismatches surface early enough to have the conversation with the merchant before the package is built, not after a funder kicks it back.
- A submission-ready package moves faster because the slowest manual step — reading months of bank statements — no longer sits in the queue.
Questions, answered.
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