Recurring withdrawal cadence
Fixed daily and weekly debits of consistent amounts are the clearest fingerprint of an active advance. MetrikData detects the cadence and groups the related debits into a single position.
MCA stacking detection
MCA stacking detection: see every advance a merchant is already carrying
Stacking is the fastest way a clean-looking deal turns into a default. MetrikData reads the merchant’s bank statements and surfaces existing MCA positions, recurring funder withdrawals, and the daily debt service eating into deposits — with the exact transactions behind each one.
- Recurring daily and weekly withdrawal patterns
- Funder and ISO naming signatures
- Estimated existing daily debt service
- Every position backed by source transactions
The problem
Why stacking is so hard to catch by eye
A merchant with three active advances can still show strong gross deposits. The danger isn’t in the top-line revenue — it’s in the debits. Funders pull repayment on fixed daily or weekly schedules, often under ACH descriptors that don’t obviously read as “loan repayment.” Scrolling a 300-line statement to find them is slow, and it’s easy to miss a position that quietly consumes a fifth of daily cash flow.
MetrikData was built to make that invisible burden visible. It scans every debit for the repayment signatures of MCA funders, groups matched debits into positions, and labels each one with a confidence level so you know what’s a strong match versus a possible one.
Evaluating tools? See how MetrikData compares to Ocrolus, MoneyThumb, and other statement-analysis platforms for MCA underwriting.
How detection works
What MetrikData looks for
Stacking detection combines pattern, cadence, and naming signals — then ties everything back to the underlying transactions so you can verify it yourself.
Funder & ISO naming signatures
Known funder names, ISO descriptors, and ACH patterns consistent with advance repayment are matched against the debit stream and surfaced with the merchant the money is going to.
Estimated daily debt service
From the matched positions, MetrikData estimates the merchant’s existing daily and weekly repayment load — so you can weigh stacking burden against real deposit volume, not guess at it.
Positions grouped with confidence
Each detected advance is presented as a position with a confidence level, the number of payments seen, and the amount per payment — not a raw list of scattered debits.
Burden against external revenue
Debt service is compared against true external revenue rather than gross deposits, so transfers and non-revenue credits don’t mask how leveraged a merchant really is.
Evidence you can click into
Every position links back to the exact debits that produced it. Nothing is a black box — you can confirm each match before it informs your decision.
Methodology
How to detect MCA stacking in bank statements
Whether you review statements by hand or with software, stacking detection comes down to four signals in the debit stream:
1. Repeating debit cadence. An active advance repays on a fixed schedule — most often every business day, sometimes weekly. Sort debits by amount and look for the same figure recurring at the same rhythm: $498 leaving the account every business day is a position, whatever the descriptor says.
2. Funder naming signatures. Repayment debits carry ACH descriptors tied to the funder or its processor. Some are obvious; many are abbreviations, initials, or a processing partner’s name that only reads as an advance once you have seen it across many statements. A maintained funder-descriptor list turns this from guesswork into matching.
3. Deposit-to-debit mismatch. Compare true external revenue — card settlements and customer payments, not inter-account transfers — against the total of the recurring debits. A merchant can show healthy gross deposits while fixed debits quietly consume a quarter of real revenue.
4. The missing-position tell. If an application discloses one advance but the statement shows two repayment cadences — or a cadence stops mid-month without a payoff credit — treat it as a red flag for an undisclosed position or a statement edited to hide one.
A worked example. A merchant shows $85,000 in monthly deposits and looks fundable. The debit stream tells a different story: $740 every business day to one descriptor and $500 to another — roughly $26,000 a month in existing debt service. Against ~$78,000 of true external revenue (after removing transfers), that is a 33% repayment burden before any new advance. A third position would push the merchant past the point where daily balances survive a slow week — exactly the deal stacking detection exists to catch.
In practice
From upload to a stacking picture in minutes
Upload the statements
Drop in the merchant’s bank-statement PDFs — text-based or scanned. MetrikData reads every transaction and daily balance.
Review detected positions
See each existing advance grouped as a position with its cadence, payment amount, and confidence level, plus an estimate of total daily debt service.
Verify and decide
Click any position to see the source debits. Correct anything that looks off, then underwrite with a clear view of the stacking burden. The decision stays yours.
FAQ
Common questions
Can MetrikData detect stacking from scanned statements?
MetrikData reads text-based PDF bank statements directly for the most accurate extraction. Scanned or image-based statements can also be processed, though results are strongest when the underlying text is selectable. In every case you can review the parsed transactions before relying on the detected positions.
Does it tell me whether to approve or decline the deal?
No. MetrikData surfaces the stacking picture — existing positions, cadence, daily debt service, and burden against external revenue — but it does not approve or decline deals and is not a decision engine. Every figure links back to the source transactions so you can verify it, and the underwriting decision stays with you.
What if a position is mislabeled?
Every detected position links to the exact debits behind it, so you can open it, check the transactions, and correct the label if needed. Corrections flow through to the daily debt service estimate and the overall stacking view, keeping the picture accurate and auditable.
How confident is the detection?
Each position is presented with a confidence level based on cadence, amount consistency, and funder naming signals, so you can tell a strong match from a possible one. Because every match points back to source rows, you are never asked to trust a black-box score.
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