Underwrite MCA deals in minutes: true monthly revenue from bank statements, NSF and negative-day counts, existing advance detection (stacking), and holdback affordability, computed automatically from every transaction.
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MCA underwriting lives and dies on bank statements: real revenue, balance behavior and whether the merchant is already stacked. LenderAnalyzer extracts months of statements in one pass and answers the funder's checklist instantly, average monthly deposits, deposit frequency, NSF/negative days, detected advance payments by funder, and net cash flow to size the offer.
An advance is repaid out of daily or weekly deposits, so the bank statements are the credit file. Almost every MCA decline, and almost every default, traces back to one of four numbers being read wrong. Here is what the software has to compute, and where funders get burned.
The most common underwriting error in merchant cash advance is treating the deposit column as revenue. A merchant's statement credits include transfers between their own accounts, refunds and chargebacks reversing out, loans and other advances landing as deposits, tax refunds, and owner capital injections. Fund against that number and you have sized the advance on money the business never earned. True revenue strips those out and counts only what the merchant actually sold. LenderAnalyzer classifies every credit and separates real revenue from transfers, financing proceeds and reversals, so the number you underwrite against is the number the merchant can repay from.
Stacking is when a merchant takes a second or third advance while the first is still outstanding, and it is the single largest driver of MCA default. It shows up in the debit column as fixed daily or weekly withdrawals to financing counterparties, often disguised behind generic ACH descriptors. Reading it by hand means recognizing hundreds of funder descriptors from memory. Software groups debits by counterparty, matches them against known funder ACH patterns, and totals the combined daily burden, so you can see every open position and what it already takes out of the account each day before you add yours on top.
Revenue tells you what came in; balance behavior tells you whether the merchant can survive a daily debit. Two businesses with identical monthly deposits behave completely differently if one holds an average daily balance of $18,000 and the other runs to zero on the 20th of every month. Count NSF events and negative days separately, because they mean different things: an NSF is a returned item and a fee, while a negative day is an account that went below zero and recovered. Most funder credit boxes cap both. LenderAnalyzer counts each per month and shows the daily balance curve behind them.
Once true revenue and existing positions are known, sizing is arithmetic against your credit policy. The holdback is the percentage of daily deposits the merchant remits; the factor rate sets total payback (an advance of $50,000 at a 1.35 factor rate pays back $67,500 regardless of how long it takes). What the statements have to support is the daily remittance surviving alongside payroll, rent and any existing advances. Funders who want this inside their own pipeline rather than a web app use the API: submissions in, a scored metrics object out, which is the same path teams take when they search for the best API for merchant cash advance underwriting.
How each approach analyzes the bank statements behind an advance. Last updated June 2026; third-party pricing changes, so confirm current figures with each vendor.
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| Approach | What it does for MCA underwriting | Best for | Pricing |
|---|---|---|---|
| LenderAnalyzer This page | Extracts every transaction from 3 to 12 months of statements, computes true monthly revenue, deposit frequency, NSF and negative days, and groups debits to other funders to surface existing positions (stacking) | Funders and brokers who want self-serve statement analysis and stacking detection without a build | Self-serve, $99 to $399/mo flat |
| Heron Data | MCA-purpose automation API that categorizes transactions, flags positions and scores files inside your own pipeline | Funders with engineering who want analysis embedded in a custom workflow via API | Usage / API pricing (quote) |
| Ocrolus | Lending-grade extraction plus analytics: income patterns, recurring obligations, NSF events and cash-flow summaries | Banks and larger lenders standardizing on a reference extraction vendor | Quote-based |
| DocuClipper / MoneyThumb | Converts statement PDFs to a spreadsheet of transactions; no MCA-specific stacking, holdback or revenue analytics | Getting transactions into Excel cheaply, then analyzing them by hand | Low monthly / per-statement |
| Manual review | An analyst reads the statements and tallies revenue, positions and NSF counts by hand | Any file, but slow and inconsistent once submission volume climbs | Staff time |
Comparison compiled by LenderAnalyzer from public vendor materials, June 2026. Competitor names are trademarks of their respective owners; figures may change, so verify current details with each vendor.
Computed deterministically from every extracted transaction, every figure traceable to its source line.
Computed across the full statement period, carried forward day by day.
Deposits vs withdrawals and net flow, broken down month by month.
Every insufficient-funds and overdraft incident counted, with fees totaled.
Recurring deposits grouped into income streams with estimated monthly amounts.
Debits to other lenders and funders detected and totaled per month.
Days below zero across the period, a direct stress signal.
The biggest credits with dates and sources, concentration flagged.
Automatic red and yellow flags your analysts can review in seconds.
Drop in PDFs, scans or photos, one statement or a multi-month package, from any bank.
Every transaction is extracted, then cash flow, balances, income streams, NSF activity and debt payments are computed.
Read the underwriting snapshot, download the Excel report, or pull structured JSON into your LOS via API.
28 lending document types extracted out of the box, build the complete picture of an applicant's financial situation.
Common questions from lending and credit teams.
It automates the statement review behind every advance: extracting transactions, computing monthly revenue and balance metrics, counting NSF/negative days and detecting payments to other funders, the core of an MCA credit decision.
Debits matching advance, loan and financing patterns are grouped by counterparty with estimated monthly totals, so existing positions and their combined daily/weekly burden are visible immediately.
The metrics give you the inputs: average monthly deposits, net cash flow and existing debt service, apply your factor and holdback policy to a verified revenue base instead of a summary page.
As many as your policy requires, 3, 4, 6 or 12 months in one batch, with a month-by-month breakdown and gap detection across the period.
Yes, REST API and webhooks return the full metrics object as JSON, and Excel/CSV exports fit manual desks. Submissions can flow in and scored summaries flow out automatically.
Merchant cash advance underwriting is the review a funder runs to decide whether to advance capital against a business's future revenue and on what terms. Because repayment comes from daily or weekly card and deposit activity, the decision leans on bank statements rather than credit bureau data: underwriters check true monthly revenue, deposit consistency, average balances, NSF and negative days, and whether the merchant already has advances stacked on top of each other.
You detect stacking by scanning statement debits for recurring advance-style payments, fixed daily or weekly withdrawals to financing counterparties, and matching them to known funder ACH descriptors. Group those debits by counterparty to see each open position and its combined daily burden. LenderAnalyzer flags these patterns automatically, so existing advances and their total holdback are visible before you fund.
LenderAnalyzer is self-serve with public pricing: Starter $99, Plus $199 and Pro $399 per month, with about 50% off on annual plans, covering every supported document type in one flat fee. MCA-focused APIs like Heron Data and reference vendors like Ocrolus are usage or quote based, so per-file cost varies with submission volume.
How credit teams run these calculations by hand, so you can see exactly what the software automates.
Spotting other funders' debits before you advance against the same revenue.
Two different stress signals, and why funders weigh them separately.
The cushion-to-deposits rule of thumb most funders apply.
Where the fees hide and how to count incidents consistently.
Analyze your first statements free, plans from $99/month, 50% off billed annually.
