True MCA burden
Every design choice targets the MCA decision — separating stacking positions from term loans, not just listing categories.
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MetrikData vs Kaaj AI for MCA underwriting
Kaaj AI targets small-business lenders with broad statement analysis — true revenue, MCA position, recurring transactions. MetrikData narrows in on the MCA underwriting read. Here’s the overlap and the difference.
Side by side
MetrikData vs Kaaj AI
CapabilityMetrikDataKaaj AI
MCA burden (financing excluded)YesPartial
Cadence typing (MCA vs loan)YesPartial
Stacking positions detectedYesPartial
Self-serve, single PDF uploadYesYes
Transparent published pricingYesPartial
Fraud / tampering checksPartialYes
Document scopeBank statements (MCA-focused)Statements (SMB lending, broad)
DeploymentWeb app + APIAPI / platform
Yes / Partial / — reflect fit for the MCA bank-statement use case specifically, not overall product breadth.
Why the difference matters
Built for one job, not every document
Cadence-based position typing
MetrikData types positions by payment rhythm and defends the rule that a monthly payer is a loan, not a stack — a distinction general analyzers often blur.
Self-serve, single PDF
Financing inflows, own transfers, and refunds are excluded from revenue, so the burden figure isn’t inflated or deflated by non-sales credits.
Data, not a decision
MetrikData avoids decision language — it hands the underwriter positions and signals, not an approve/decline.
The honest take
Kaaj is stronger for broad SMB-lending analysis
Kaaj AI covers a wide small-business-lending feature set — data extraction, categorization, average-daily-balance trends, debt-payment analysis, operational-expense cash flow — across many loan types. If you underwrite varied SMB products and want one broad analyzer, that breadth is useful. MetrikData is narrower on purpose: it optimizes specifically for the MCA read, where the deciding details are funder identity, payment cadence, and burden against financing-excluded revenue.
For the MetrikData side of that trade-off, see the MCA underwriting software overview — or brush up on the terminology in the MCA glossary.
FAQ
Common questions
How does MetrikData compare to Kaaj AI?
Both read business bank statements, but Kaaj targets broad SMB lending while MetrikData is MCA-specific — focused on stacking positions, cadence typing, and MCA burden.
Do both detect MCA positions?
Both surface lender activity. MetrikData’s focus is separating confirmed MCA positions from term loans by cadence and computing burden against true revenue, which is the core MCA question.
Which should an MCA-only shop choose?
If your book is MCA rather than mixed SMB lending, MetrikData’s narrower, MCA-tuned output is built for exactly that workflow.
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See the difference on your own statement
Start free — run a real merchant statement and compare the output yourself.