Bank Statement Extraction.

Income and cash-flow verification is one of the last manual steps in most lending pipelines. Applications get submitted digitally, credit pulls happen through an API, but the bank statement, the document that actually shows whether someone can repay a loan, still gets opened, read, and keyed in by hand at a lot of shops. Bank statement extraction closes that gap.

Plain OCR reads pixels into text. It can tell you a page contains a number, not whether that number is the closing balance, a single deposit, or a fee. Axia Extract adds a schema-based AI layer on top that understands which figure is which field, based on context rather than a fixed position on the page, which matters when a single statement can carry dozens of visually similar dollar amounts.

Every bank formats columns, headers, and transaction descriptions differently. Template-based tools need a new template, and a support ticket, for every bank they add. Because Axia's extraction is schema-based, a new bank's statement format works without a rebuild: define the fields you want once (account number, statement period, opening and closing balance, transactions), and the same schema applies across formats.

Digital lenders and BNPL providers use it to read three to six months of statements per applicant, for approval and for ongoing risk monitoring. Manual underwriting teams use it for statements from banks an aggregator doesn't cover, or applicants who won't share banking credentials. It pairs well with ID document extraction for full applicant onboarding, and accounting teams use the same engine to reconcile client statements against ledgers without re-keying transaction lines every month. Our bank statement OCR guide covers how this differs from account-aggregator APIs like Plaid.

Accuracy on financial fields matters most here, since a misread balance can misjudge an applicant's ability to repay. Axia Extract publishes its benchmark numbers openly: 99.9% accuracy on financial totals and 96.3% on dates, tested against SROIE2019, a public dataset of 347 real documents, detailed on the accuracy page. Every extracted field returns its own confidence score, so a low-confidence transaction line can be flagged for review instead of accepted silently. Results export as structured JSON through a REST API, ready to plug into a loan origination or underwriting system.