A bank statement analyser reads a PDF or scanned statement and organises every transaction into a structured table you can analyse by category, counterparty, and month. It captures the statement details — bank, account, period, and opening and closing balances — then extracts date, description, amount, and running balance for each row. Scanned pages are processed with text recognition first, then pass through the same validation as digital files.
Analysis covers four areas: structured transactions, cash flow, obligations, and verification. Transactions include cleaned descriptions, categories, payment mode, and a confidence value per row. Cash flow summarises monthly income, recurring credits, and the expense-to-income ratio. Obligations list EMIs by lender, bounce counts, and debt-to-income. Verification adds a fraud score with the reasons behind it.
The most useful comparison is declared figures versus bank reality. A declared salary is only confirmed when matching credits appear regularly in the statement, with the same payer pattern and plausible amounts. For variable earners, the analyser groups salary-like credits separately from business-like transfers and refunds, then shows month-by-month totals so you can see stability rather than a single average. Expenses get the same treatment: total outgoings, regular bills, and discretionary spending broken down by category.
Before trusting any result, review the verification signals. Opening balance plus transactions should reconcile to the closing balance, and the tool should show that calculation openly. Low-confidence rows — common on faint scans — should be flagged rather than silently included, and any mismatch should produce a clear error instead of an adjusted total. Metadata signals, balance-chain validation, and font consistency add further context on whether the file itself can be trusted.
Lenders, landlords, letting agents, accountants, and small organisations use this workflow to verify affordability and categorise months of activity without manual spreadsheets. Behaviour such as regular overdraft use, frequent bounces, or heavy cash deposits becomes visible in context rather than as isolated lines. To test it on your own file, try the free live demo of the Bank Statement Analyser with an upload or the built-in sample data.