How Gen Z Is Changing India’s Credit Landscape

By Rajit Bhattacharya, Co-founder and CEO, Data Sutram

Aryan, a 25-year-old IT professional, orders breakfast through a 10-minute delivery app and books a cab that arrives in five. By the time he reaches his desk, he has applied for and received a ₹50,000 loan on his phone, allowing him and his colleagues to lock in an international holiday within minutes.

What Aryan does not see is the complex machinery behind that approval.

A traditional Economic bureau file cannot always support decisions at this speed. For many young borrowers, such a file may not even exist. To bridge this gap, lenders are increasingly turning to external intelligence platforms such as Data Sutram, which combine alternative digital signals—including location, device and transaction footprints—to build real-time trust profiles for borrowers with limited or no formal Funding history.

This shift, from waiting for Economic history to accumulate to interpreting live signals, is helping make instant Funding possible at Gen Z’s pace.

Credit must be instant, invisible and embedded

Unlike previous generations, Aryan has probably never walked into a bank branch. He does not merely expect banking to be convenient; he expects it to be instant and embedded within the transaction.

That expectation is reflected in India’s changing lending patterns. Loans below ₹1 lakh account for nearly 89% of personal loan originations by volume, but less than 19% by value. Bharat  is issuing an enormous number of small-ticket loans—and increasingly doing so in real time.

Personal loan originations grew by 17% in volume and 29% in value during FY26. NBFCs now originate more than nine out of every ten personal loans by count, while banks continue to hold the majority share by value. The market is therefore evolving into two distinct businesses: one built around scale and speed, and another around ticket size and value.

The transformation is also changing why people borrow. Earlier generations often borrowed primarily out of necessity—for emergencies, income shortfalls or major life milestones. Younger consumers are increasingly using Economic for convenience, experiences and lifestyle choices. The traditional stigma around karza is steadily diminishing.

Millions of borrowers, but limited credit history

Approximately 25 million Bhartiya  turn 21 every year. Many enter the formal financial system with little or no bureau history and limited conventional information for lenders to assess.

New-to- Loan consumers already account for 16% of all originations, while people below 35 constitute 58% of India’s first-time borrowers. Economic demand is expanding faster than the traditional evidence available to evaluate borrowers.

The growth of digital lending has been enabled by two major developments: India’s digital public infrastructure, which has expanded the signals available to lenders, and predictive AI, which converts those signals into faster, more informed decisions.

Digital infrastructure supplied the evidence. Predictive AI supplied the judgement.

The next frontier is secured credit

Despite the growth of instant personal loans, secured lending continues to represent the larger share of India’s Loan market by value.

Whether customers seek home, vehicle or business loans, the process can still take weeks. Extensive paperwork, physical verification and manual assessments make secured lending slow and cumbersome.

As Gen Z grows older and seeks larger amounts of secured Economic, this is where the next phase of transformation must take place.

Information required for secured lending—including business provenance, property ownership, vehicle details and collateral documentation—remains fragmented or insufficiently digitised. Generative AI can help by extracting information from complex documents, identifying inconsistencies, summarising borrower profiles and supporting underwriting teams in making faster decisions, while retaining governance and human oversight.

External intelligence platforms such as Data Sutram are beginning to extend profile-based approaches from unsecured lending into secured categories. This can include verifying business and property ownership, cross-referencing collateral with real-world data and developing stronger trust signals for borrowers with stable incomes or viable businesses but incomplete paper trails.

Can large-ticket credit become equally seamless?

Gen Z will eventually move from short-term consumption Loan to business, vehicle and home loans. The question is whether AI can make these larger loans significantly faster without compromising risk controls.

Achieving this will require reliable data infrastructure, responsible AI, stronger fraud controls and effective human oversight. If Bharat can solve these challenges, it can make formal Loan more accessible to millions of underserved borrowers.

That would not simply mark the next stage of lending innovation. It could become an important step towards financial inclusion, economic mobility and India’s larger vision of a Viksit Bharat.

Disclaimer: No Business Standard Journalist was involved in creation of this content