By Anil Sinha, CTO, Fibe
Somewhere in a small town in Bihar or Bundelkhand, a vegetable vendor is about to take a formal loan for the first time in her life. She has no credit history, no collateral, and no relationship with a bank that has ever known her name. What she has is a smartphone, an income that shows up in fragments across UPI transactions, and a decision to make about whether she trusts this app with her money, her data, and her future repayment obligations.
That single moment of trust, repeated tens of millions of times a year, is what India’s digital lending industry has actually been building towards. It is easy to describe this as a story about technology, faster onboarding, instant approvals and paperless disbursals. It is really a story about whether formal credit can finally reach the people it has excluded for decades and whether it can do so without breaking their trust the first time it tries.
India’s middle-income segment has grown from 161 million households in FY20 to 173 million in FY26 and is projected to reach 208 million by FY31, according to the 1Lattice Report. Much of this expansion is being driven by Tier 2 and Tier 3 cities, signalling income growth beyond metropolitan regions. Behind that number is a generation of first-time borrowers being asked to trust institutions they cannot see and systems they may not understand, all through an app and a few seconds of onboarding. It is a big step, and the industry has not always earned that trust.
The speed race is over
For years, fintech companies focused on getting customers from downloading an app to getting a loan as quickly as possible. Instant approvals and easy application processes were the main selling points. That race is now largely over, as these features have become standard across the industry rather than something that sets one lender apart.
What has emerged instead is a harder and more important race to make speed safe. Deepfake-led onboarding, synthetic identities that combine real and fake data, mule account networks, and AI-powered impersonation scams have turned fraud from an isolated crime into an organised, evolving threat. Fraudsters now test, improve and scale their attacks much like legitimate fintechs scale their products. Earlier this year, authorities across India flagged a rise in fake loan app scams and AI-generated voice fraud, in which criminals impersonated customer support executives to extract OTPs and banking credentials from unsuspecting borrowers.
This is the uncomfortable truth every lending platform now has to face. Every convenience built for a genuine customer is also available to someone trying to impersonate one. The industry that wins the next decade will not be the one that ignores this tension. It will be the one that resolves it by building systems that are fast for genuine borrowers and tough on fraud, without making the process visible to the customer.
Trust is the product
For someone taking a formal loan for the first time, the decision is not just about the interest rate or how quickly the money arrives. It also comes down to a simple question, “Can I trust this?” Will my data be safe? Are the terms clear? And if something goes wrong, will I be able to reach someone who can help?
This is why innovation in digital lending is moving from visible features to the systems working quietly in the background. Behavioural signals such as device details, transaction patterns, location and repayment history are checked in real time. This helps genuine borrowers move faster while suspicious activity can be flagged before the loan is disbursed. Document checks, contextual signals and liveness detection are also built into the onboarding process without making it difficult for genuine customers. The best fraud prevention is often the kind that honest customers do not even notice.
Multilingual AI-powered engagement is also making digital lending more accessible by allowing borrowers to interact in the language they are most comfortable with. This helps build trust through better communication. When borrowers clearly understand what they are agreeing to, they are less likely to be confused, misled, or leave the platform.
What matters most is that borrowers who feel safe are more likely to come back. They repay on time, recommend the platform to others, and return when they need credit again instead of simply choosing the lowest rate. In a competitive market, trust is not just a soft value. It can help build stronger, long-term customer relationships.
Regulation as a catalyst, not a constraint
The RBI’s evolving digital lending framework, covering digital lending apps, key fact statements, direct disbursal protocols and stricter due diligence, is often seen as a compliance burden. It is better understood as a way to strengthen the industry. Regulators have increased scrutiny this year following concerns around fraudulent loan apps, misuse of customer data and coercive recovery practices that have affected trust in digital credit. The platforms responding well to this shift are not treating governance as a checklist. They are building explainability, data localisation, and responsible AI into their systems from the start.
This matters beyond any single institution’s risk exposure. Every fintech operates on a shared reputational balance sheet. When one fraudulent app harms a borrower in a small town, the damage radiates to every legitimate platform trying to earn that same borrower’s trust. Compliance-led design is, in this sense, an act of collective self-interest for the entire industry, and the platforms that internalise this first will be the ones regulators and borrowers trust with the next phase of growth.
Better security is better underwriting
Traditional credit models relied on bureau scores and formal financial records, instruments that, by design, excluded much of India’s informal economy. AI-led underwriting is beginning to close that gap by looking at a wider range of signals, including transaction consistency, digital payment behaviour, cash flow patterns and real repayment history, even for borrowers who have never held a credit card.
Intelligent document analysis can help identify tampered invoices and manipulated salary records before they reach an underwriter, while behavioural checks can spot unusual or coordinated fraud patterns early. This allows lenders to act before a loan is disbursed rather than after a loss occurs. The result is not just fewer bad loans but a wider and fairer way to assess who can be considered creditworthy. When security is built well, it does not slow down financial inclusion but makes responsible inclusion possible at scale.
What this is actually building towards
It is easy to see all of this as a competition around market share and differentiation. But the bigger story is about something more important. India is building a new foundation of trust for millions of people entering the formal financial system for the first time. Many of them are doing it through a screen, without ever visiting a branch, meeting a relationship manager or having a family history with a bank.
This is a new kind of trust-building happening at a scale and speed the world has rarely seen. The lenders that succeed over the next decade will not be remembered only for how quickly they disbursed loans. They will be remembered for whether borrowers, from a vegetable vendor in Bihar to a gig worker in a Tier 3 city, could trust them with their money and have that trust respected every time.
Technology has become easier to access, but trust has not. In the years ahead, the lenders that build trust through strong, secure and well-governed technology will have the strongest moat.