Drop in PDF or scanned statements from any US bank. Get every transaction back as Excel, CSV or JSON, plus the numbers underwriting needs: true monthly revenue, average daily balance, NSF counts and existing loan payments.
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Bank statement extraction software turns a PDF statement into rows: date, description, amount, running balance. General-purpose parsers such as Parseur, Parsio, Docparser, DocuClipper and Lido do that well enough for bookkeeping, and most start between $20 and $49 a month. For a lender that output is only the raw material. Nobody approves a loan on a list of 1,400 transactions.
What an underwriter actually needs sits one layer above the rows. Which deposits are revenue and which are transfers from the owner's other account, a loan disbursement or a refund. What the average daily balance was each month. How many NSF and overdraft items hit, and on which days the account went negative. Which recurring debits are payments to other lenders or MCA funders that never made it onto the application.
LenderAnalyzer does both steps in one upload. It parses statements from any US bank, text PDF or scan, checks that the extracted transactions add up to the printed opening and closing balances, and then computes the underwriting metrics. You get the transactions as Excel, CSV or JSON for your own models, and a lender-ready summary for the credit file.
Below is how the extraction works, how it compares with the generic parsers and with the lender-specific tools, and what each costs per statement at published prices.
Prices in the comparison are the vendors' own published figures from their pricing pages. Where a vendor publishes nothing, we say so.
The parser reads the header, account number, statement period, opening and closing balance and every transaction line, including multi-line descriptions. It then reconciles the rows against the printed balances. A statement that does not tie out is flagged instead of passed through, which is how a missed page or a doctored figure gets caught.
Downloaded statements from Chase, Bank of America, Wells Fargo, Citi, U.S. Bank, PNC, Truist and regional banks are read as text. Scans, phone photos and faxed pages go through OCR first. You do not build a template per bank, which matters when a broker sends you statements from forty different institutions in one week.
Gross deposits overstate revenue on almost every small business file. LenderAnalyzer separates transfers between the borrower's own accounts, loan and MCA disbursements, refunds, reversals and cash injections from operating income, so the monthly revenue figure is the one you would underwrite to, with each excluded deposit listed for review.
Average daily balance is computed from the running balance for every day of the period, not from the opening and closing figures. NSF and overdraft items are counted per month with their dates, and days in the negative are totaled. These are the three numbers most credit policies and MCA funding guidelines set limits on.
Recurring ACH debits to lenders, MCA funders, equipment finance companies and card processors are grouped by payee with their frequency and amount. A daily or weekly debit to a funder that is not on the application is the most common reason a file should stop, and it shows up on the summary rather than on page 11 of the third statement.
Export the transactions and the summary to Excel or CSV for your spreading model, or JSON for your own scoring logic. Teams building a lending product can post statements to the REST API and read the results back programmatically. The same extraction feeds the income analysis, so one upload serves the whole file.
A single deal usually carries three to six months of statements, often from more than one account. Upload the whole set at once in a batch and each statement comes back parsed and summarized, so a gap in the months or an account nobody mentioned is visible before an analyst opens the file. Funders reviewing dozens of submissions a day use batches to triage.
If you only need transactions in a spreadsheet for bookkeeping, a general parser at $20 to $49 a month does the job and we would not pretend otherwise. The difference shows up when the statement is evidence in a credit decision, where reconciliation, transfer detection and debt grouping decide whether the number you rely on is right.
Published prices from each vendor's own pricing page. A credit, parse or document can mean different page counts, so read the unit column before comparing.
Swipe sideways to see the full comparison
| Tool | What it returns | Published price | What it leaves to you | Best for |
|---|---|---|---|---|
| LenderAnalyzer This page | Transactions balanced to the statement, plus true revenue, ADB, NSFs, negative days and debt payments | From $99/mo, with volume and enterprise tiers | Loan origination and e-signature | Lenders, MCA funders and brokers who underwrite from statements |
| ClearStaq | Parsed statements with MCA position detection, fraud scoring and income analysis | StaqCore $99/mo for 150 credits, StaqPro $249/mo for 500, StaqScale $699/mo for 2,000 | Tax return and financial statement spreading | MCA funders focused on fraud and stacking |
| Parseur | Extracted fields and tables from emails and PDFs, sent to spreadsheets or apps | Free tier of 20 pages/mo; Micro $49/mo for 100 pages, Starter $129/mo for 1,000 | All underwriting metrics | Operations teams parsing many document types |
| Parsio | Transactions and fields from PDFs and emails, exported to Excel, CSV or Sheets | From $24/mo billed yearly; Growth $124/mo for 5,000 credits | All underwriting metrics | Small teams on a tight budget |
| Docparser | Rule-based extraction from PDFs into structured data | Starter $39/mo for 100 credits, 1 credit = a document up to 5 pages | Template upkeep per bank layout, and all metrics | Repeat documents with a fixed layout |
| DocuClipper | Bank statement transactions to Excel, CSV and QuickBooks | $20/mo billed yearly for 60 pages; $111/mo for 640 pages | Revenue, ADB and debt analysis | Bookkeepers and accountants |
| Lido | Tables and fields from any document into Excel or CSV | Standard $29/mo for 100 pages; Scale $7,000/yr for 42,000 pages | All underwriting metrics | Finance teams with mixed document types |
Comparison compiled by LenderAnalyzer from public vendor materials; see the date noted above each table. 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 depends on what you do with the output. For bookkeeping, DocuClipper, Parsio or Parseur extract transactions for $20 to $49 a month. For lending, the best tool also reconciles the rows to the statement and computes revenue, average daily balance, NSFs and existing debt, which is what LenderAnalyzer and ClearStaq are built to do.
Upload the PDF to a bank statement parser. It reads the text layer, or runs OCR on a scan, identifies the transaction table and returns each row with date, description, amount and balance. Good parsers also check the rows against the opening and closing balances so a missed line is caught before you rely on the totals.
Yes, on clean text PDFs modern AI parsers extract transactions with very few errors, and scans are close behind when the image is legible. The safeguard that matters is reconciliation: if the extracted rows do not add up to the printed closing balance, the tool should flag the statement instead of returning a silent error.
Yes. LenderAnalyzer, ClearStaq, Parseur, Docparser and cloud services such as Amazon Textract and Google Document AI all offer APIs. The difference is what comes back: generic APIs return text and tables, while lending APIs return transactions plus computed metrics such as true revenue, average daily balance and NSF counts.
Published entry prices run from $20 a month, billed yearly, for DocuClipper's 60-page plan to $49 for Parseur Micro and $99 for LenderAnalyzer and ClearStaq's entry tier. Units differ: some bill per page, some per credit or per document of up to five pages, so compare the cost of a typical three-month file.
Most can, through OCR, but accuracy depends on scan quality. Skewed phone photos, faxed pages and low-resolution images produce more misread amounts. LenderAnalyzer runs OCR on scans and still reconciles the result to the printed balances, so a misread figure shows up as an out-of-balance statement rather than a wrong total.
Generic parsers do not. Lending tools look for signs of an altered statement: transactions that do not sum to the printed balances, fonts or metadata inconsistent with the issuing bank, round-number deposit patterns and duplicated lines. LenderAnalyzer flags out-of-balance statements and suspicious patterns for the underwriter to review.
LenderAnalyzer exports transactions and the underwriting summary to Excel, CSV and JSON, and the API returns JSON. Most generic parsers also export to Excel, CSV or Google Sheets, and some bookkeeping tools such as DocuClipper add QuickBooks files. Choose the format your spreading model or scoring system reads.
Most small business lenders and MCA funders ask for three to six months of business bank statements, and bank statement mortgage programs typically use 12 or 24 months. Extraction software makes the longer lookbacks practical, since reading 24 statements by hand takes an underwriter most of a day.
Yes. The converter at the top of this page runs on a real statement, so you can see the extracted transactions and the summary before you create an account. Paid plans start at $99 a month, with volume and enterprise tiers for funders and lenders processing large batches.
How credit teams run these calculations by hand, so you can see exactly what the software automates.
Nine parsers priced per three-month loan file.
Post a PDF, get transactions and metrics as JSON.
The full underwriting analysis built on the extraction.
Altered and fabricated statements flagged for review.
Undisclosed MCA and loan payments grouped by funder.
When a bookkeeping converter stops being enough.
What it costs to write and maintain your own parser.
Document, open banking and OCR APIs compared.
Analyze your first statements free, plans from $99/month, 50% off billed annually.
