The most reliable AI returns in a finance team come from documents that arrive in volume and follow a loose but consistent structure: supplier invoices, bank statements, purchase orders and KYC documents.
Extraction is the easy part
Modern language models extract fields from these documents with high accuracy, without training on your data. They handle different layouts, scanned copies and even handwritten notes reasonably well.
Validation is where the value is
- Match invoice lines against purchase orders and goods receipts.
- Check vendor GSTIN, bank details and PAN against your masters.
- Verify tax calculations and flag mismatches.
- Detect duplicates before they are paid twice.
Scope the first automation narrowly
Pick one document type, one system to post into and a clear rule for what goes to review. Measure the time saved and the error rate, then extend to the next document type.
Use cases ranked by payback
| Use case | Volume | Payback |
|---|---|---|
| Supplier invoice capture and three-way match | High | Fast |
| Bank statement import and reconciliation | High | Fast |
| Expense receipts and claims | Medium | Medium |
| Customer KYC and onboarding documents | Medium | Medium |
| Contract and agreement review | Low | Depends on risk value |
What the workflow looks like
- Invoices arrive by email or upload and are captured automatically.
- AI extracts header and line details, GSTIN, totals and tax.
- Validation rules match against PO, GRN and vendor master.
- Clean invoices are posted as drafts to the ERP or Tally for approval.
- Exceptions go to a review queue with the reason highlighted.
