AI Agent Profile · LendingIQ · Bengaluru
Document Verification Agent AI
DivisionOnboarding
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What this agent does
The Document Verification Agent AI extracts structured data from every document in a loan application file using OCR, checks the extracted data for internal consistency and cross-document accuracy, flags deviations from the document policy, and identifies patterns that are associated with forged or manipulated documents. Every exception is documented with the specific field, the expected value, and the observed value — so the credit officer who reviews the exceptions has complete information, not a generic flag. Verification identifies data anomalies; authentication of physical documents requires specialist tools and human examiners.
Primary functions
OCR Extraction
Every document in the application setInvoked when: document set is assembled by the Loan Origination Agent AI
- Extracts all structured data fields from each document type — salary slips (employer name, employer PAN, gross salary, deductions, net salary, pay period, employee name), bank statements (account holder name, account number, bank name, IFSC, statement period, opening and closing balances), ITRs (filed income, tax paid, assessment year, PAN), and business documents (GST registration number, turnover from GSTR, MCA registration number) — using OCR with confidence scoring per extracted field.
- Flags fields where OCR confidence is below the configured threshold — poor scan quality, low-contrast print, handwritten fields — and marks these as requiring human verification rather than using the low-confidence extracted value in downstream calculations. A field the agent cannot reliably read is better flagged as unread than passed forward as a wrong value.
Forgery Detection
Pattern-based — known forgery signalsInvoked on each document as part of the verification pass
- Cross-checks document data against authoritative external sources: employer PAN on salary slips against NSDL records; employer GSTN against the GSTN API for the stated employer name; bank IFSC code against the RBI IFSC registry. Where the document's stated details do not match the authoritative record, the discrepancy is flagged as a potential fabrication signal — specific field, expected value from the authoritative source, value stated on the document.
- Checks the document against the forgery pattern library — known templates used in prior fraud cases: salary slip formats with specific watermark placements or font combinations associated with previously identified forgeries, income certificate formats from institutions that the fraud team has flagged, or document structures that match known synthetic income document patterns.
- Does not authenticate physical document features — ink, paper, embossing, watermark UV response. Pattern detection is data-driven, not forensic.
Policy Checks & Exception Flagging
Every document against the policy corpusInvoked after OCR extraction is complete
- Checks every extracted document against the document policy requirements: salary slips must be from the last 3 months (or as configured per product), bank statements must cover the required period, ITRs must be for the assessment year specified in the product policy, and business registration documents must not have a lapsed validity date. Policy misses are documented with the specific requirement and the actual document date/status.
- Cross-checks field consistency across documents: name on the salary slip vs name on the PAN card vs name on the bank statement; income declared on the salary slip vs income shown in the bank credits; stated employer on the application form vs employer named on the salary slip. Each discrepancy is flagged with the conflicting fields and values from each document.
- Produces a structured exception log — every exception with the document it was found in, the specific field, the policy requirement or expected value, the observed value, and a severity classification (Minor / Material / Fraud flag). The exception log is the handoff document to the credit officer who must resolve each exception before the underwriting agent is invoked.
Hard guardrails
Known limitations
Important Reads
Learn more about how to deploy Document Verification Agent AI to your lending workflow.
