logo
navbar hamburger

Financial Services · Document processing automation

Your finance analysts spend their day re-keying data that is already in PDFs

Banks, NBFCs, insurers, and leasing companies handle thousands of documents every working day — loan applications, bank statements, invoices, contracts, KYC packets — and a large share of the data inside them is still extracted by hand or by brittle RPA that breaks on every format change.

Banao builds AI document processing that reads your documents in full — structured PDFs, scanned pages, handwritten fields, mixed formats — extracts the fields your downstream systems need, validates them against your rules, and routes the output into your core without a human touch unless an exception requires one.

What a Banao document automation deployment includes

Document automation in financial services is not just OCR. It is extraction, validation, exception logic, and a compliance audit trail — we deliver all four.

Statement and financial data extraction

Bank statements, income documents, and GST returns extracted field-by-field — account numbers, transaction histories, income lines, tax figures — validated against your credit or underwriting rules before they reach the analyst.

Invoice and trade document automation

Vendor invoices, purchase orders, and trade finance documents read and mapped to your ERP or core banking fields, with line-item matching and three-way reconciliation where your workflow requires it.

Contract and agreement intelligence

Loan agreements, insurance policies, and service contracts parsed for key terms — dates, rates, covenants, conditions — indexed and made queryable, so compliance and legal teams find what they need without a manual read.

KYC document reading and cross-check

Identity documents, address proof, and business registration records extracted and cross-checked against your onboarding rules and watchlist data, with a compliance decision log on every case.

Exception routing and human-in-the-loop

Low-confidence extractions and rule violations are flagged and queued for a reviewer — with the context they need to decide in thirty seconds, not thirty minutes of hunting through folders.

Audit trail and regulatory log

Every document read, every field extracted, every validation and exception decision is logged with a timestamp and model version — so your next audit is a query, not a reconstruction.

Where this pattern is running

Metrics shown dotted (··) are being finalised in our case-study metrics pack — we publish figures only once verified. Clients in this vertical are named where contracts allow; others are described without identifying detail.

A digital NBFC lender

Statement extraction removed manual credit prep

  • ··%reduction in analyst prep time
  • ··%extraction accuracy on scanned statements
  • ··×faster application-to-decision cycle

The credit team re-keyed bank statement data by hand for every loan application — three to five statements per file, each taking around twenty minutes. Banao deployed an extraction model that reads multi-format statements, pulls the required fields, and validates them against the underwriting checklist before an analyst sees the application.

We process our own documents before yours

Banao runs a ~300-person engineering operation on its own AI products. Vikaas — our demand-gen AI — processes structured and unstructured data inputs every day, and InterviewGod handles candidate document review as part of our hiring pipeline.

Building AI that has to work inside our own operation before it ships to a client is the standard we hold every document automation project to. We are not describing what AI can do in theory; we depend on it for our own business.

  • InterviewGodHandles candidate document review for Banao's own engineering hires.
  • VikaasProcesses structured and unstructured data inputs for Banao's demand-gen pipeline.

When document automation is the wrong investment

Not every document problem needs an AI model. We will tell you before you spend on one:

  • Low volume: if your team handles fewer than a few hundred documents a week, the build cost will not pay back within a year. We will say so.
  • Extremely variable formats: if every counterpart sends a different document structure with no consistent field positions, week one is document standardisation, not modelling — and sometimes the honest answer is to fix intake first.
  • Upstream gaps: if the real problem is that documents arrive late or incomplete, automation moves the bottleneck one step earlier; it does not remove it.
  • Existing tools that cover the case: if your core banking or ERP already has an extraction module that meets your accuracy bar, adding a new model layer adds cost without adding value.

How we start — prove extraction accuracy before you build

We do not quote a document automation build from a description. We read a sample of your real documents first.

  1. AI Discovery Sprint2 weeks · fixed price

    We run your real document samples through extraction, measure field-level accuracy on your hardest cases, and hand back a feasibility report with an accuracy baseline and ROI estimate — yours to keep. If you proceed, the Sprint fee is credited against the build.

  2. Build

    Model training on your document types and field definitions, integration with your core banking or ERP, and exception-handling logic with the review queue your team will use daily.

  3. Production & continuous improvement

    Deployed pipeline with an audit trail, SLA monitoring, and a reviewer feedback loop — so model accuracy improves as your document mix evolves rather than drifting against it.

Frequently asked questions

Structured PDFs, scanned pages, handwritten fields, and mixed-format packets — bank statements, income documents, invoices, contracts, ID documents, and business registration papers. The Discovery Sprint establishes accuracy on your specific formats before any build commitment.

Accuracy depends on scan quality and document consistency. The Discovery Sprint measures this directly on your hardest cases and gives you a field-level accuracy baseline. We will not quote a production build until we know the accuracy floor on your real inputs.

Yes. Integration with your core banking, ERP, or workflow system is part of the build deliverable. We have worked with major Indian banking cores and ERP platforms. Where a direct API does not exist, we build a secure adapter rather than requiring a system change on your side.

Low-confidence or rule-failing extractions go into a review queue with the relevant document context and the specific field highlighted. The reviewer corrects in one click; the correction is logged for compliance and fed back to improve the model over time.

Every document ingested, every field extracted, every validation result, every exception and its disposition, and every reviewer correction — all timestamped with model version. The log is exportable in the format your compliance and audit teams require.

Send us a sample of your hardest documents

In 45 minutes we will tell you whether AI extraction is worth building for your document types — and what accuracy you can expect before you commit to anything.

Book a 45-min scoping call