Bank Statement Parsing & Financial Document Extraction
Bank statement parsing turns PDFs and scans of bank statements into structured transaction data — opening and closing balances, every transaction line, dates and running balances — so reconciliation, underwriting and accounting run on clean numbers instead of manual keying. Because statements vary by bank and format, the pipeline reads layout and context rather than relying on one fixed template.
Structured data from unstructured statements
Transaction lines
Date, description, reference, debit, credit and running balance for each transaction row.
Balances
Opening balance, closing balance and totals, checked for arithmetic consistency.
Account details
Account holder, account number and statement period from the header.
Charges & interest
Bank charges, interest credited or debited and other adjustments.
How we build a document AI pipeline
Capture
Statements arrive as PDFs (including multi-page) or scans, from one bank or many.
Table-aware OCR
Rows, columns and running balances are read with layout understanding, not flat text extraction.
Normalise
Different bank formats are mapped to one consistent transaction schema.
Validate
Debits, credits and balances are reconciled; anything that does not add up is flagged.
Integrate
Structured transactions flow into your accounting, lending or reconciliation system.
Where it delivers value
Lending & underwriting
Parse applicant bank statements to assess income, obligations and cash-flow patterns.
NBFCLendingBookkeeping & accounting
Turn client statements into reconciled transactions without manual entry.
AccountingReconciliation
Match statement lines against ledgers or payment records to find discrepancies fast.
FinanceFinancial analysis
Build a structured transaction feed for reporting, categorisation and analytics.
AnalyticsWhat we can extract
Built for accuracy and control
Table-aware extraction preserves the row and column structure that plain OCR destroys.
Balance reconciliation validation catches inconsistent parses before they reach downstream systems.
One normalised schema across many banks, so your downstream logic stays simple.
Confidence scores and review queues keep accuracy under control at volume.
Related results
Frequently asked questions
Can you parse statements from different banks?
Do you handle password-protected or scanned statements?
How are transaction tables kept accurate?
What can we do with the parsed data?
Explore related services
Drowning in bank statements to reconcile?
Tell us the banks and statement volumes. We will scope a parsing pipeline and a validation approach.
Request a Statement Parsing Estimate