Underwrite loans on what the bank account actually shows: true operating revenue, month-by-month net cash flow, average daily balance, NSF and negative days, and the debt already collecting from the account. Computed from every transaction, traceable to the line it came from.
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Cash flow underwriting decides a loan from the borrower's real bank account activity rather than a credit score alone. The evidence behind the method has firmed up: FinRegLab's 2025 small business research found that cash flow variables built from bank account data predict loan performance better than the owner's personal credit score alone, especially for younger businesses and financially constrained owners. That is why the method has moved from a fintech workaround into mainstream credit policy at banks, credit unions, CDFIs and working capital funders. The practical obstacle was never the idea; it was the work. Reading three to twelve months of statements, netting transfers out of revenue, building the monthly series and finding the debt the borrower did not mention takes an analyst hours per file. LenderAnalyzer does that part in minutes: upload the statements and get the full cash flow picture, every figure linked to the transactions behind it, ready to drop into your credit box. It is the document analysis layer for cash flow underwriting, self-serve from $99 a month, with an API when you want the metrics inside your own decisioning.
A credit score summarizes how someone handled credit in the past. The bank account shows what the business is doing right now: what actually comes in, what actually goes out, and how much room is left. Cash flow underwriting is the discipline of turning that record into a credit decision. Here is what a defensible version looks like.
A large share of creditworthy US small businesses cannot be underwritten on a score: the business is young, the owner's personal credit is thin or scarred by something unrelated, or the file simply has no depth. The bank account has no such gap, because every operating business has one and the deposits are already there. FinRegLab's 2025 study of small business lending found cash flow attributes predicted default better than the owner's personal score alone, and academic work analyzing tens of thousands of fintech loan outcomes found cash flow intensive underwriting approved low-FICO applicants that score-based lenders declined, without the loss rates a score would have predicted. The signal is real. What matters is computing it honestly, which is where the document work begins.
The first number a cash flow decision rests on is monthly operating revenue, and the raw deposit total is not it. Transfers between the borrower's own accounts, owner injections, loan and advance proceeds and refunds all land as credits without being sales. LenderAnalyzer classifies every credit and nets those out, so the revenue figure you underwrite is what customers actually paid. From there the monthly series takes shape: gross inflows, outflows and net cash flow for each statement month, which is the backbone of any cash flow credit box. On accounts with heavy inter-account movement, the gap between gross deposits and true revenue is routinely large enough to flip a decision.
Two borrowers with the same average monthly cash flow can be completely different credits. One earns it steadily with a five-figure average daily balance; the other swings between strong months and near-zero weeks with NSF items in between. A workable cash flow credit box therefore tests more than the mean: minimum monthly revenue in the weakest month, average daily balance against the proposed payment, NSF and negative-day counts over the period, and the direction of the trend across months. LenderAnalyzer computes all of those from the transaction level, so the file shows whether the borrower can absorb a payment in a bad month, not just an average one.
To be precise about scope: LenderAnalyzer is not a decisioning platform or a credit model. It does not output a score, apply approval rules, or decide anything. You set the credit box; it computes the inputs, true revenue, the monthly net series, average daily balance, NSF and negative days, and existing debt service detected in the outflows, including advances the borrower did not disclose. Teams run it two ways: underwriters read the analysis in the workspace on individual files, and fintech lenders call the REST API to pull the same metrics object into their own rules engine or model. Either way, every figure traces to the statement line it came from, which is what a reviewer, an auditor or a regulator asks for.
Where each approach gets its cash flow data and what it costs to operate. Last updated July 2026; third-party pricing changes, so confirm current figures with each vendor.
| Approach | Where the data comes from | What you get for the decision | Pricing |
|---|---|---|---|
| LenderAnalyzer This page | Bank statement PDFs the borrower already has, no account connection required | True revenue net of transfers, monthly net cash flow, average daily balance, NSF and negative days, existing debt detection, in the workspace or via API | Self-serve, $99 to $399/mo |
| Open banking attributes (Plaid, Prism Data) | A live feed from accounts the borrower agrees to connect | Clean cash flow attributes for connected accounts, with gaps when a borrower will not link every account or the bank is unsupported | Per-connection / API pricing |
| Bureau cash flow scores | Bureau-built attributes layered on permissioned account data | An off-the-shelf score to blend with traditional credit, less transparent at the transaction level | Per-pull, contract pricing |
| Decisioning platforms (Taktile, Lendflow) | Whatever data sources you integrate, including statement analyzers like this one | A rules and workflow engine that automates the decision once the inputs exist | Quote-based, typically five figures a year |
| Manual statement spreading | An analyst keys figures from the PDFs by hand | A hand-built spread: workable at low volume, slow and inconsistent as volume grows | Staff time, hours per file |
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.
Cash flow underwriting is deciding a loan from the borrower's real bank account activity, deposits, withdrawals, balances and existing obligations, rather than from a credit score alone. It judges current repayment capacity directly instead of inferring it from past credit behavior, which is why it works for young businesses and thin-file borrowers a score cannot read.
The lender collects 3 to 12 months of bank statements or connected account data, computes true operating revenue net of transfers and financing inflows, builds the monthly net cash flow series, measures balance cushion through average daily balance and NSF activity, and totals the debt already being serviced. Those figures then run against the lender's credit box: minimum revenue, minimum cushion, maximum existing debt burden and maximum payment-to-cash-flow ratio.
For small business credit, the evidence says cash flow data adds real predictive power. FinRegLab's 2025 research found cash flow variables predicted loan performance better than the owner's personal credit score alone, especially for early-stage businesses. Most lenders use both: the score for credit history and character, the cash flow for current capacity. The combination outperforms either alone.
Software that turns raw bank account data into the metrics a cash flow decision needs. LenderAnalyzer does it from statement PDFs: it extracts every transaction, nets transfers and financing out of revenue, computes the monthly cash flow series, average daily balance, NSF and negative days, and detects existing loan and advance payments, with every figure traceable to its source line.
Fintech lenders and MCA and revenue-based funders built their models on it, and it has spread into mainstream credit: community banks and credit unions use it for small business loans the score cannot support, CDFIs use it for thin-file borrowers, and factors and equipment lenders use it to verify capacity. Bureau and open banking cash flow products launched through 2024 and 2025 reflect that adoption.
Primarily bank account transactions: every deposit and withdrawal over the period, daily balances, and the patterns inside them, recurring revenue streams, payroll, rent, existing loan payments and NSF events. Statements are the universal source because every business has them; open banking feeds add real-time data when the borrower connects accounts. Tax returns provide the cross-check that the account activity matches what was reported.
Yes. LenderAnalyzer's REST API accepts statement uploads and returns the full metrics object as JSON, the monthly series, true revenue, average daily balance, NSF and negative days and detected debt payments, so fintech lenders and funders pull the numbers straight into their own decisioning or models.
LenderAnalyzer is self-serve with public pricing: Starter $99, Plus $199 and Pro $399 per month, with roughly 50% off on annual plans. Open banking and bureau cash flow products price per connection or per pull under contract, and decisioning platforms are typically quote-based at five figures a year, so a smaller lender can adopt the method without an enterprise agreement.
No, and that is deliberate. It computes the decision inputs and leaves the decision to your credit policy, whether that is an underwriter reading the workspace or your rules engine consuming the API. It does not score applicants, apply approval logic or decline anyone, which keeps your credit box, and the accountability for it, in your hands.
Analyze your first statements free, plans from $99/month, 50% off billed annually.
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