You cannot analyze a PDF. BankXLSX is a bank statement analyzer that reads a PDF statement and writes every transaction into clean spreadsheet rows: date, description, amount, and a running balance. From there you calculate average daily balance, monthly income, cash flow, and NSF counts in Excel, your underwriting model, or QuickBooks, with the numbers you can trust. Upload a statement and download an XLSX or CSV in under a minute. Start free, no credit card.
Last updated June 2026
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A bank statement analyzer reads the transactions off a PDF bank statement and turns them into structured data you can measure: dates, descriptions, signed amounts, and a running balance. Once the statement is in rows, you can compute average daily balance, total monthly deposits, recurring obligations, and NSF or overdraft counts. BankXLSX handles the extraction step, converting any US bank or credit card PDF into clean Excel or CSV, so your own analysis, lending model, or accountant works from accurate numbers instead of retyping a document by hand.
A bank statement holds everything you need to judge income and cash flow, but it arrives as a PDF: a picture of a page, not a table of numbers. Until the transactions are in rows, none of it can be measured.
There is no way to total deposits, average a balance, or count NSF fees in a PDF. Every figure stays locked in the layout until the transactions become spreadsheet rows.
Keying a few hundred lines into Excel by hand burns an afternoon and invites a transposed amount that quietly throws off the income average or the balance trend.
The live CSV download only reaches back a few months and often drops the running balance. Older statements, closed accounts, and the full history you need to analyze come as PDFs.
Average daily balance and minimum balance depend on the running balance column, which most raw CSV exports leave out entirely, so you cannot reconstruct it after the fact.
Recurring obligations, large deposits, and transfers all hide inside free-text descriptions, so analyzing spend means parsing each line before you can group anything.
A real analysis spans 12 to 24 months. Stitching a year of separate PDFs into one consistent dataset by hand is slow and error prone.
Upload the PDF and the analyzer reads the statement, then writes every transaction into clean, consistent rows with the running balance intact, so the analysis you run afterward starts from accurate data.
Date, description, amount, and balance land in separate columns, so you can sort, filter, sum, and pivot the data instead of reading a page.
The balance column carries through on every row, which is what average daily balance, minimum balance, and overdraft analysis depend on.
Convert a year or more of statements and combine them into one consistent dataset for income averaging over 12 or 24 months.
Credits and debits are signed correctly, so totaling monthly deposits, counting NSF events, and flagging large deposits is a formula, not a manual pass.
Export to XLSX or CSV for your own model, or to QBO, QFX, and OFX when the next step is QuickBooks or another finance app.
256-bit encryption in transit, and you can delete your uploaded files whenever you want. Nothing to install.
No software to install and no credit card to start.
Drag your PDF bank or credit card statement into the box above. Scanned and image-only statements work too.
Tip: Upload several months for a full analysis.
Once the transactions are read, download a clean XLSX or CSV with dates, amounts, and the running balance in columns.
Tip: Pick CSV for your underwriting model.
Open it in Excel, Google Sheets, or your model and compute average daily balance, monthly income, cash flow, and NSF counts.
Tip: Use a pivot table to group by category.
The people analyzing bank statements are usually deciding something with money on the line: whether to lend, what the books really show, or how a business is actually performing.
Turn a borrower PDF into rows to compute average monthly deposits, average daily balance, and NSF counts before approving a loan.
Analyze a client cash position, catch unusual transactions, and reconcile faster when the whole year is in one clean sheet.
See where money actually goes each month, spot recurring charges, and track cash flow without paying for an analytics platform.
Build a transaction timeline for a dispute, divorce, or audit, with every line searchable and sortable instead of buried in PDFs.
A bank statement analyzer takes the raw transactions on a statement and turns them into measurable financial signals. At the data layer, that means reading every line off the PDF, the date, description, amount, and balance, and writing it into structured rows. At the analysis layer, that structured data lets you calculate the numbers that matter: average daily balance, total monthly income, recurring expenses, cash flow, and NSF or overdraft frequency. BankXLSX is the data layer. It converts any US bank or credit card statement into a clean spreadsheet with the running balance intact, so your own analysis, your underwriting model, or your accountant works from accurate figures rather than a retyped page.
The whole point of converting a statement is what you can do afterward. Once the transactions sit in rows, every common bank statement metric becomes a formula instead of a manual count.
| Metric | What it tells you | How to get it in Excel |
|---|---|---|
| Average daily balance | Liquidity and reserve strength a lender weighs | Average the running balance column across the days in the period. |
| Total monthly deposits | Qualifying income for a bank statement loan | SUMIF the credit column by month, then exclude transfers. |
| NSF and overdraft count | Cash-flow stress that triggers manual review | COUNTIF the description column for NSF, overdraft, or returned items. |
| Large deposit flags | Deposits that need sourcing in underwriting | Filter credits above a threshold, often half of monthly income. |
| Recurring obligations | Existing debt and committed spend | Group debits by payee with a pivot table to surface repeats. |
It helps to be clear about what BankXLSX is and is not. Enterprise platforms like Ocrolus, HyperVerge, and DocuClipper bundle extraction with a decisioning layer: they score income, classify transactions, run fraud checks, and hand back a risk verdict. They are built for lenders processing thousands of files through an API. BankXLSX is the converter underneath that work. It extracts the transactions accurately and gives you clean Excel or CSV, then you or your model own the analysis and the decision. For a small firm, an independent underwriter, or anyone who would rather keep the judgment in their own spreadsheet, that is the cheaper and more transparent path. If you want the heavier underwriting workflow, see how BankXLSX compares on the Ocrolus alternative page.
The workflow is the same whether you are sizing up one month or two years. Convert first, then measure.
Upload each PDF and download it as Excel or CSV. For an income analysis, that usually means 12 to 24 consecutive months. Keep the same format so the columns line up when you stack them into one sheet.
Paste each month under the last so you have a single continuous transaction list. Add a month column if it helps you group later. With the running balance preserved on every row, the daily balance series stays intact across the join.
Use SUMIF and COUNTIF for deposits and NSF counts, a pivot table to group spend by payee, and a simple average of the balance column for average daily balance. Our walkthrough on how to analyze bank statements in Excel covers the exact formulas, and the running balance extraction page explains why preserving that column matters.
The analyzer reads any US bank or credit card statement, so the same upload structures a national bank, a regional bank, a credit union, or a card issuer. If you are working a per-bank file, convert a Chase statement, a Bank of America statement, or a Wells Fargo statement, then analyze the rows. Lenders working borrower files can start from the bank statement converter for lenders, and accountants from the accountant workflow. To group spend before you analyze it, the transaction categorization page helps.
Most analyses feed something else. Lenders doing full loan underwriting can run the structured data through dedicated loan underwriting analysis software. For any other business PDF you need as a spreadsheet first, a general PDF to Excel converter handles reports and tables, and firms processing financial documents at scale can extract them with enterprise document OCR.
A bank statement analyzer reads the transactions off a PDF statement and turns them into structured rows you can measure: dates, descriptions, signed amounts, and a running balance. From there you compute average daily balance, monthly income, cash flow, and NSF counts. BankXLSX handles the extraction so your analysis works from accurate numbers instead of a retyped document.
Convert the PDF to Excel first so every transaction sits in rows with a running balance. Then use SUMIF to total monthly deposits, COUNTIF to count NSF or overdraft events, a pivot table to group spend by payee, and an average of the balance column for average daily balance. The conversion is the step that makes all of those formulas possible.
It is a converter. BankXLSX extracts the transactions and gives you clean Excel or CSV with the running balance preserved, then you or your model run the analysis and make the call. It does not score income or approve loans on its own, which keeps the judgment, and the data, in your hands.
Yes. Convert 12 to 24 months of statements, stack them into one sheet, and total the deposits by month while excluding transfers. That gives you the average monthly income figure lenders use for bank statement loans. The analyzer preserves the data; you apply your own income definition and any expense haircut.
Yes, and that is the point. The running balance carries through on every row, which is what average daily balance, minimum balance, and overdraft analysis depend on. Most raw CSV downloads drop that column, so converting the PDF is how you keep it.
As far back as you have statements. Because the analyzer works from the PDF rather than a live bank feed, you can convert old periods and closed accounts that the bank download will not reach, which is exactly the history a 12 or 24 month income analysis needs.
Yes. The analyzer reads credit card statements the same way it reads bank statements, structuring purchases, payments, fees, and interest into rows. That lets you analyze spend, recurring charges, and balances on the card the same way you would a checking account.
Uploads use 256-bit encryption in transit, you can delete your files at any time, and your financial data is never resold or shared. The conversion runs in your browser without installing anything, so the statement stays under your control while it is processed.
Keep the running balance on every row.
Convert any PDF statement to clean Excel.
Get started converting bank statements to spreadsheets.
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$288 yearly
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| Pro AI Best accuracy | 500 pages |
Scale statement conversion across your team with automation.
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| Base AI Faster | 10,000 pages |
| Pro AI Best accuracy | 2,000 pages |
Enterprise-grade bank statement conversion and controls.
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$ yearly
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| Base AI Faster | pages |
| Pro AI Best accuracy | pages |
