Bajaj Finance to roll out AI facial recognition across 3,000 stores by March 2027
Bajaj Finance plans to expand its AI-powered camera system from 544 to 3,000 stores, using facial recognition to identify existing customers and trigger personalised offers. It is targeting a 70% identification rate, up from about 40%.
What happened
Bajaj Finance will deploy AI-powered facial recognition across 3,000 stores by March 2027 to identify customers and trigger personalized offers. The lender aims
Key facts
- Facial-recognition system to expand from 544 stores to 3,000 stores by March 2027
- Nearly 10 million images captured in four months
- 3.2 million existing customers identified
- Identification rate targeted at 70% by March 2027 from about 40%
- Consolidated net profit: ₹6,081 crore, up 28% year-on-year
- AUM: ₹5.5 trillion as of 30 June, up 24% year-on-year
Why this matters
The rollout increases the strategic value of partnerships or acquisitions in computer vision, consent management, in-store analytics and privacy-preserving identity technology.
What to watch
- Identification rate progression from roughly 40% toward the stated 70% target, segmented by store format and city.
- Reported incremental conversion, loan disbursal, cross-sell or customer-retention uplift attributable to the camera system.
- Customer opt-out rates, privacy complaints, adverse media coverage or biometric-data incidents.
- Enforcement actions or guidance under India's Digital Personal Data Protection framework affecting facial-recognition deployment.
- Whether competitors deploy comparable in-store identity and offer-orchestration systems.
- Expansion beyond existing-customer recognition into prospect profiling, fraud prevention or retailer-partner data integration.
- Build explicit in-store biometric notice, consent capture, opt-out and non-biometric service paths before mass deployment.
- Tie facial identification to a closed-loop measurement system tracking offer exposure, staff action, conversion, incremental loan value, complaint rates and false-match incidents.
- Prioritise rollout in stores with high repeat visitation, strong CRM coverage and high-value financing categories rather than deploying uniformly.
- Use identified visits to trigger assisted-selling workflows, pre-approved eligibility checks and post-visit digital follow-up, not only on-screen offers.
- Establish governance for biometric data retention, access controls, model bias audits, vendor accountability and incident response.