True MCA burden
Financing inflows are removed from the revenue base, so burden isn’t distorted by advances landing as ‘deposits.’
MoneyThumb converts PDF statements into transaction data with fraud detection. MetrikData goes a layer deeper for MCA: it types each funder position by cadence and computes true burden. Here’s the comparison.
Yes / Partial / — reflect fit for the MCA bank-statement use case specifically, not overall product breadth.
Financing inflows are removed from the revenue base, so burden isn’t distorted by advances landing as ‘deposits.’
A monthly payer is typed as debt service, a daily/weekly payer as an MCA position — the distinction that decides whether it’s stacking.
MetrikData groups debits by funder identity and reports each active MCA position separately, so you see the stack — not just a list of categorized debits.
Metrics and signals are laid out for an underwriter to interpret, with the source transactions behind each figure.
Each position, burden figure, and flag opens to the source debits it came from — traceable evidence, not summary numbers you take on faith.
Text and scanned PDFs from every major US bank parse the same way, so an image-only scan is read as fully as a clean digital statement.
MoneyThumb has a long track record converting a wide range of PDF and financial-file formats into clean transaction data, with well-regarded document-forensics and tampering detection. If your core need is turning messy statements into structured data across many file types, or deep forensic fraud analysis, that’s their strength. MetrikData assumes you have the transactions and focuses on what they mean for an MCA decision — positions, cadence, and burden.
For the MetrikData side of that trade-off, see the MCA underwriting software overview — or brush up on the terminology in the MCA glossary.
MoneyThumb specializes in converting statements to data and forensic fraud checks. MetrikData specializes in MCA interpretation — typing funder positions by cadence and computing burden against true revenue.
It surfaces transactions and can flag lender-like activity, but position-level stacking detection with cadence typing and burden calculation is MetrikData’s specific focus.
If you mainly need a fast MCA read on a merchant’s statement without a data-engineering step, MetrikData’s self-serve, MCA-specific output is designed for that use case.
Start free — run a real merchant statement and compare the output yourself.