The challenge
A pan-India used-car marketplace was sending 4,000 monthly buyer enquiries to its inside-sales team but only 18% reached a pre-approval stage because documentation was a mess — salary slips, bank statements and PAN photos came in over WhatsApp and had to be manually retyped into 9 different NBFC portals. Median pre-approval was 3 days. Buyers walked into a competitor lot in the meantime.
How we deployed
- Layered an AI qualification agent over inbound enquiries to capture budget, exchange and finance need.
- Built multi-modal document extraction for salary slips, 6-month bank statements, PAN, Aadhaar and Form 16.
- Validated extracted data against CIBIL pulls and EMI affordability checks before any NBFC submission.
- Routed pre-approval requests to the best-fit NBFC based on buyer profile, vehicle age and LTV.
- Pushed application status to buyer WhatsApp — submitted, sanctioned, ready-to-disburse.
What changed
- Pre-approval turnaround fell from 3 days to 4.2 hours median across 6,800 applications.
- Closure rate lifted 2.4× as buyers stayed engaged through a tight feedback loop.
- NBFC rejection rate dropped 31% because pre-validation caught issues upstream.
- Inside-sales team focused on closure conversations, not document chasing.
- Marketplace take-rate improved through finance-attached deal mix.
"Used-car buyers do not have three days to wait for a finance decision — by then a CarTrade or Cars24 lot has them. We compressed the loop and our closure rate went up 2.4×."
— Head of Finance Vertical · Used-Car Platform