AI Agent Profile · LendingIQ · Bengaluru
Bureau & Data Agent AI
DivisionOnboarding
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What this agent does
The Bureau & Data Agent AI manages LendingIQ's bureau pull operations — triggering pulls at the right point in the origination pipeline, interpreting score outputs and tradeline data for the underwriting queue, preparing dispute correspondence when bureau data errors are identified, and maintaining the bureau hit rate and quality metrics. It is the operations layer between LendingIQ's credit process and the credit information companies.
Primary functions
CIBIL / Experian Pulls
Per application and monthly refreshINVOKED WHEN: loan origination pipeline signals application completeness, or monthly batch refresh run is due
- Triggers bureau pulls at the correct pipeline stage — after KYC verification and application completeness are confirmed, before the underwriting assessment begins — using the applicant's PAN and date of birth. Applies the bureau pull sequence per credit policy: CIBIL as primary for retail and MSME, with Experian or CRIF as secondary where dual bureau checks are required by the product policy.
- Manages bureau API failure handling: where a primary bureau API is unavailable, triggers the secondary bureau automatically and flags the primary failure for the operations team to investigate. Does not delay the underwriting pipeline waiting for a failed primary bureau — the secondary bureau result proceeds the file with the failure noted.
- Tracks the bureau pull log — every pull with timestamp, bureau, PAN, result, and cost — for operations monitoring and bureau cost management. Pulls wasteful or duplicate pulls are flagged to the operations team.
Score Interpretation
Per pull — delivered with every bureau reportINVOKED WHEN: bureau report is received and requires an interpretation summary for the underwriting queue
- Reads the full bureau report and produces a structured interpretation summary: score band, score change since last pull (where a prior pull exists in the portfolio), DPD summary across all tradelines, total outstanding obligation, and the most significant negative items (90+ DPD, write-off, settled, or ongoing legal action). The summary is designed to give the credit officer and the Underwriting Agent AI a quick orientation to the bureau picture — not to substitute for reading the full report.
- Flags specific bureau signals that require human credit officer attention regardless of the overall score: a very recent (within 90 days) new secured loan or credit card — possible undisclosed obligation; a tradeline that shows 'settled' (the borrower paid less than the full amount owed) — credit behaviour signal; or multiple hard enquiries in the last 30 days — active loan shopping that may indicate undisclosed applications at other lenders.
Dispute Resolution
On borrower dispute or identified data errorINVOKED WHEN: a borrower raises a data quality concern or the credit team identifies a bureau data error
- Reads the dispute details — what data the borrower or credit team believes is incorrect, the evidence supporting the dispute, and the credit information company whose data is in question — and prepares the dispute correspondence in the format required by CIBIL, Experian, or CRIF's dispute resolution process: the loan reference number, the specific field being disputed, the evidence of the correct data, and the request for correction.
- Tracks the dispute through the bureau's response cycle — logging the submission date, the bureau's acknowledgment, the investigation outcome, and the corrected data once the bureau updates its records. Alerts the credit officer when a dispute has been pending beyond the bureau's published resolution timeline (typically 30 days under RBI's CIC regulations).
Hard guardrails
Known limitations
Important Reads
Learn more about how to deploy Bureau & Data Agent AI to your lending workflow.
- Use case #0001Thin-file scoring: how Bureau AI interprets thin-file CIBIL scores using alternate signalsA CIBIL score of 0 does not mean a borrower is not creditworthy — it means the bureau has no data to generate a score from. In India, an estimated 190 million adults are credit-invisible: they have bank accounts, pay utility bills, and run small businesses, but have never taken a formal loan and therefore have no bureau score. The Bureau AI does not treat a thin file as a failed file — it reads the signals that exist and builds a credit picture from what is actually there.Read article →
- Use case #0002Automated bureau dispute resolution: what Bureau AI can handleA bureau dispute that takes 30 days to resolve is a loan application that sits in limbo for 30 days — or dies because the borrower applies elsewhere rather than wait. The Bureau AI identifies bureau inaccuracies at the moment the credit report is pulled, classifies the dispute by type and urgency, submits the automated dispute to CIBIL's dispute resolution system, and tracks the resolution — without a human compliance officer managing each case. Disputes that can be resolved automatically are. Those that cannot are escalated with a complete brief.Read article →
- Use case #0003Bureau cost optimisation: reducing unnecessary pulls by 40%At ₹60–₹120 per bureau pull, an institution processing 2,000 applications per month spends ₹1.44 to ₹2.88 lakhs on bureau queries. Forty percent of those pulls — across the institution's portfolio — are unnecessary: duplicate queries for the same applicant within 90 days, pulls on applications that fail basic eligibility gates before the bureau is needed, and refreshes on accounts where the existing report is still within its validity window. The Bureau AI eliminates unnecessary pulls before they are triggered — reducing bureau spend without any compromise to credit quality.Read article →
