From Call Centres to Credit Underwriting: How AI Is Becoming Central to NBFC Strategy

When Bajaj Finance reported its Q3 FY26 results, it did not merely speak about margins, AUM or credit costs. It spoke about machines listening.

During the earnings call, Rajeev Jain, Managing Director, Bajaj Finance, laid out one of the most detailed AI deployments disclosed by any Indian NBFC so far. “AI listened to 2 crore calls, converted voice to text, and gave us data. Text-to-data conversion happened for 5.2 lakh customers. As a result, we generated 100,000 new offers for which we did not have information earlier,” he said. 

He added that “loan disbursements through AI-powered call centres stood at about Rs 1,600 crore”, roughly 10% of the Rs 16,545 crore disbursed in Q3 FY26. “We’ll be able to listen to 100 million calls next year”.

The company also disclosed it conducted “46 million face matches” to validate returning customers and mapped “43 such documents…which an image extracts with a 95%–96% accuracy.”

Bajaj Finance is not alone. Across the sector, Q3 commentary suggests that AI is steadily moving from pilot projects into enterprise systems. In its Q3 FY26 earnings call, Tata Capital said it had moved “from AI pilots to enterprise-wide deployment” across marketing, sales, credit, operations, service and collections. The company referred to “voice-based, agentic AI platforms deployed across sales, customer service and collections,” and described itself as an early investor in “Gen-AI driven underwriting.” 

A similar shift was evident at Poonawalla Fincorp, which talked about an “AI-first” approach in underwriting, customer interaction and operational workflows.

Beyond Chat

By all indications, AI is now entering the core of credit decisioning and customer servicing, not just front-end automation. 

According to Ashutosh Taparia, chief operating officer and managing director of digital banking platform CredAble, said the shift is not part of a fad, but grounded in real and measurable outcomes: “The AI conversation in financial services has moved beyond signalling. What we are seeing now is a separation between narrative adoption and operational integration…It’s not just shareholder appeasement.” 

He said the use of AI to process over 20 million customer queries into actionable insights and disburse Rs 1,600 crore of loans constitutes “execution at scale.” Drawing from his own operational experience, he added that AI deployments have delivered “over 40% efficiency gains and close to 90% AI-led accuracy wins.”

He argues that AI adoption in India’s BFSI sector has moved decisively from pilots to enterprise-wide integration, particularly in credit underwriting and risk engines. “Agentic AI is moving across the entire lending lifecycle, from borrower onboarding and origination to underwriting, fraud detection, compliance monitoring and collections intelligence, areas that directly affect risk and profitability.”

Industry data support this argument. An EY report indicates that 42% of Indian financial institutions were actively investing in AI and GenAI initiatives, with 74% running proof-of-concepts that are scaling into operations and underwriting.

Concrete Gains

AI is taking over processes such as document processing, data extraction and real-time analysis to reduce manual intervention and turnaround time. “What was once a 14-day manual process can become a 14-minute autonomous workflow, with human-in-the-loop oversight,” he said, referring particularly to MSME lending processes that are traditionally data-intensive.

The financial benefits of this AI transformation seem to be significant for NBFCs. Taparia estimates AI deployment could improve profit by 10-20% over the next few years through cost efficiencies, revenue uplift and stronger risk mitigation. 

Reliability, however, remains central in finance. Taparia emphasised that AI in lending must be explainable, auditable and governed. “In a regulated environment, explainability and accountability are as important as speed,” he said.

Jobless Growth?

The rapid integration of AI has also raised concerns about employment impact, in a sector where a lot of entry level jobs could be at risk.

Balasubramanian A, Senior Vice President, TeamLease Services, however, dismisses the notion that AI is a job killer in financial services. “AI has never emerged solely as a ‘job killer’ in the BFSI sector. Rather, it is a role evolver and productivity multiplier,” he said.

Companies are using AI to identify missed opportunities and expand into new growth segments, which in turn create new roles, he argues. “The sector still needs field officers, credit managers and grievance redressal teams to cater to this expanded demand.” 

According to him, the recent slowdown in BFSI hiring is more closely linked to higher delinquency rates and a greater focus on collections. Delinquency rate refers to the percentage of loans where borrowers have missed their payments.

Balasubramanian added that AI enables financial inclusion at a scale that human-only teams could not achieve. “As NBFCs expand into Tier 2, 3 and 4 cities, physical presence and trust-building remain essential. These markets require long-term relationship management, which necessitates opening physical offices, thereby creating a different set of employment opportunities. As AI enables deeper penetration into underserved regions, the demand for localised human touchpoints increases, even as the global back office shrinks,” he explained.