Why converting a PDF bank statement is harder than it sounds
Copy-paste from a PDF into a spreadsheet almost never works. Bank statements use multi-column layouts with column headers repeated on every page, wrapped descriptions that spill onto the next line, running-balance columns that shift when the number of digits changes, and subtotal rows that break the flow of real transactions. The output of a naive copy is a mangled block of text where amounts stack in the wrong column, dates land in the description field, and every page footer contaminates the data. Manual cleanup on a twelve-month statement can eat a full afternoon.
A converter that actually works has to understand the shape of a bank statement, not just the text of a PDF. That means detecting where the transaction table starts and ends, identifying which vertical bands hold which fields, merging rows that wrap, and dropping the header, footer, and subtotal noise. Karchu does this automatically for every common statement layout without any per-bank templates or configuration.
How the conversion works
Karchu treats a PDF conversion as a three-step pipeline. First, extract the text. For digitally generated PDFs (the kind your bank produces when you download a statement from its portal), this is a text-extraction pass that preserves positional data - the x and y coordinates of every glyph. For scanned image PDFs, Karchu runs on-device OCR to convert the pixels into positioned text. Both paths converge on the same intermediate representation.
Second, structure detection. Karchu inspects the positional data to find the transaction table: a block of rows that share a consistent column geometry. Header rows, subtotals, and page footers get filtered out. Wrapped descriptions get rejoined by matching a continuation line's x-coordinate to the description column above. Multi-page statements get stitched into a single continuous table by matching the column geometry across pages.
Third, normalization. Dates are parsed into ISO 8601. Amounts are parsed with the right sign (debits negative, credits positive) regardless of whether the source used parentheses, a trailing minus, a leading minus, or paired debit/credit columns. Currency symbols are stripped. Character encoding is normalized so foreign merchant names survive the trip.
What you get in the exported file
The exported Excel or CSV has a stable schema:
- Date in ISO 8601 (YYYY-MM-DD).
- Description as it appeared on the statement, with wrapped lines rejoined.
- Amount as a signed decimal number.
- Category assigned by the rule engine (Groceries, Rent, Subscriptions, Salary, and so on).
- Balance when the source statement includes one, or a computed running balance otherwise.
- Account when the source statement covers multiple accounts.
Columns do not change between exports, which matters when you build a downstream spreadsheet, a Power Query template, or a script that reads Karchu output on a schedule. UTF-8 encoding means merchant names with accents, umlauts, or non-Latin characters round-trip correctly through Excel, Numbers, and Google Sheets.
Scanned statements and OCR
Many banks still produce statements that are effectively images: scanned paper, or a print-to-PDF workflow that rasterizes the page. Text extraction fails on those files. Karchu detects when a PDF is image-based and switches to OCR automatically. The OCR runs in your browser, so scanned statements are processed on your device rather than uploaded to a third-party OCR API. Accuracy on typical statement fonts is well above 99%, and Karchu highlights any row where a numeric field looks ambiguous so you can double-check before exporting.
Common uses for the converted output
- Accounting software import. QuickBooks, Xero, and Wave all accept CSV imports. Karchu output drops in directly.
- Loan and mortgage applications. Lenders often ask for the last three to twelve months of statements in Excel. Karchu produces the combined file with categories in one export.
- Tax preparation. Filter the exported file by category to isolate deductible business expenses, charitable contributions, or medical spending.
- Budgeting. Import the categorized output into a personal budgeting spreadsheet or dashboard.
- Forensic and audit work. Structured data is the starting point for anomaly detection, duplicate-charge investigations, and cash-flow reconstruction.
Batch conversion
One statement is easy. Twelve is where a converter earns its keep. Karchu accepts a batch upload, converts every file, merges the transactions into a single categorized dataset, and exports the combined output. Duplicates that appear across overlapping statements are detected and deduped. Categories stay consistent across the whole batch because the rule engine is shared, so you can build a rule once during month one and it applies retroactively to every prior month.
Frequently asked questions
How do I convert a PDF bank statement to Excel?
Sign in to Karchu, drop the PDF into the upload area, wait a few seconds, then click Export as Excel. The output is a clean XLSX with a header row (date, description, amount, category, balance) that opens in Excel, Numbers, or Google Sheets without cleanup.
Does it work with scanned PDFs?
Yes. Karchu uses on-device OCR to read scanned image PDFs. The OCR runs in your browser, so scanned statements are processed locally rather than shipped to a third-party OCR service.
Are there per-page fees?
No. The free plan includes a generous monthly document allowance and paid plans lift the limit. Karchu never charges per page, per line, or per export.
Will the converter preserve the running balance column?
Yes. When the source statement has a balance column, Karchu extracts and includes it. When the statement only shows debits and credits, Karchu can compute a running balance during export.
Can I convert multiple PDF statements at once?
Yes. Upload a batch of statements and export the combined output as a single Excel or CSV file. This is useful for annual returns and multi-month analyses.
Which banks are supported?
Any bank that produces a PDF statement. Karchu does not maintain a per-bank template; it detects the layout automatically. Chase, Bank of America, Wells Fargo, HSBC, Barclays, Lloyds, Revolut, Wise, Monzo, N26, Starling, most credit unions, most business banks, and hundreds of regional and international banks work out of the box.
Is my statement private during conversion?
Yes. CSV and Excel files are parsed entirely in your browser. PDFs are processed with in-browser text extraction and OCR. Files are stored only if you deliberately save them into your private vault.
Convert a statement now
You do not need to commit to a workflow to try this. Sign in, drop in the last PDF statement your bank sent you, and export the Excel. If the file is cleaner than what you get from any other converter you have tried, keep going. If it is not, you have lost two minutes.