Beyond Credit Scores: How NBFCs Are Using Tech to Say 'Yes' to More Borrowers
For a long time, lending decisions were tied to one number. If that number didn’t look good, the conversation usually ended there. Today, that approach is slowly changing. NBFCs are moving away from rigid, score-only checks and leaning into digital systems that look at the borrower as a whole. Technology now helps lenders understand spending behaviour, income patterns, and repayment intent — not just past credit mistakes. This shift has made lending more flexible, faster, and far more practical for people who don’t fit into old banking formats.
Alternative Data Scoring: The New Identity for 'Thin-File' Borrowers
Not everyone has a long credit history. Some people have never taken a formal loan, others had gaps or informal incomes that never showed up on a bureau report. That doesn’t mean they don’t manage money well. NBFCs are now using technology to understand these borrowers differently.
Instead of depending on one credit score, lenders look at how money actually moves in everyday life. They check whether utility bills are paid on time, how bank accounts are used month after month, and whether income comes in regularly even if it’s not salaried. Digital payments, UPI activity, and wallet usage also give clues, not about how much someone spends, but how consistently they operate.
For self-employed borrowers and small traders, things like GST filings or business account movement help build context. Even simple signals, such as how long a phone number or address has stayed the same, add to the picture. None of this replaces credit scores entirely, but it helps lenders say yes to people who would earlier be rejected without a second look.
The Tech Engine: AI, ML, and CIaaS
The Tech Engine: AI, ML, and CIaaS
When people talk about lending tech, it often sounds complex. In reality, most of it exists to reduce confusion, not add to it. These systems are meant to support decisions, not replace human judgement.
AI: Cutting Through the Noise
Applications generate a lot of information. Too much, actually. AI is mainly used to filter this noise. It scans data quickly and points out what looks unusual and what doesn’t. The goal isn’t to approve or reject instantly, but to avoid missing obvious signals that matter.
ML: Learning From What Already Happened
Machine learning doesn’t start smart. It learns slowly. Past repayments, delays, and failures feed into the system over time. Based on this, the model adjusts how similar cases are viewed later. Some patterns hold, some don’t. The system changes as outcomes change.
CIaaS: Keeping Everything Connected
CIaaS isn’t a feature borrowers see. It’s more like plumbing. It connects checks, data pulls, and decision steps so things don’t break in between. Without it, lending journeys become slow and fragmented, with too much manual fixing.
Comparison: Traditional vs. Tech-Driven Underwriting
| Feature | Traditional Underwriting | Tech-Driven NBFC Model |
|---|---|---|
| Primary data used | Mostly bureau credit scores and past loan records | A mix of alternative data,analysed through AI-led systems |
| Who it works best for | Borrowers with an established credit history | First-time borrowers and thin-file profiles |
| Time taken for approval | Can stretch from a few days to even weeks | Often completed within minutes |
| Documentation | Physical paperwork, income proofs, sometimes collateral | Largely paperless with digital verification |
| Reach | Branch-led, usually focused on larger cities | Designed to work across India,including beyond the top 100 cities |
Embedded Finance: Credit at the Point of Need
Credit is no longer something people apply for separately and wait on. In many cases, it now appears exactly where the need arises. Whether it’s inside a shopping app, a service platform, or a property portal, financing options are built directly into the journey. This reduces friction, shortens decision time, and allows borrowers to access credit without stepping out of their original transaction.
Expanding Inclusion: Empowering Women and MSMEs
Access to credit is still uneven. Many women running small businesses struggle to get loans, even when their work is steady. The issue is rarely intent. It’s usually documentation, credit history, or income that doesn’t follow fixed patterns.
NBFC lending has helped in these cases by allowing smaller loan sizes and simpler checks. This gives women entrepreneurs room to manage expenses, buy stock, or handle slow months without depending on informal borrowing. Over time, this also helps them build a usable credit record
MSMEs face a different problem. Timing. A delayed loan can disrupt salaries, suppliers, or daily operations. Quick access to short-term funds often matters more than large amounts. Faster processing and fewer steps help businesses keep running without major interruptions.
Impact seen on the ground:
• More first-time women borrowers entering formal credit
• Small businesses avoiding high-interest informal loans
• Local enterprises maintaining day-to-day cash flow
This kind of credit support doesn’t change things overnight, but it reduces pressure where it matters most.
Challenges Faced in This Process
● Data is not always clean or complete, especially for first-time or informal borrowers
● Alternative data can show behaviour, but it doesn’t always explain intent
● Tech systems still need human judgement when edge cases appear
● Digital onboarding excludes users who are not comfortable with apps or online steps
● Connectivity and device issues slow things down in semi-urban and rural areas
● Faster approvals increase risk if checks are not balanced properly
● Borrowers sometimes take multiple small loans without tracking total exposure
Ayaan Finserve India: RBI certified NBFC
Ayaan Finserve India is an RBI-registered NBFC. The company operates in the personal lending space and focuses only on small-ticket personal loans.
AFI provides short-term personal loans meant for everyday needs. Loan amounts are kept small. Tenures are limited. The idea is to avoid long repayment cycles
Most applications are handled digitally. Basic checks are done online. The process is kept simple so borrowers don’t have to deal with heavy paperwork or repeated follow-ups.
AFI does not offer large or secured loans. The focus remains on personal loans and controlled exposure.
Loan Application
Apply for a personal loan by filling in the form below. Enter your basic details, submit the required information, and proceed with the application. The process is online and designed to be completed in a few steps
Frequently Asked Questions (FAQs)
1. How can I get a loan if I have a “thin” credit file or no credit history?
A thin credit file doesn’t mean you can’t repay a loan. NBFCs look at other signals like income flow, bank activity, and regular payments. These details help assess repayment ability even without past loans.
2. What is a “New-to-Credit” (NTC) borrower?
An NTC borrower is someone who has never taken a formal loan or used a credit card before. There’s no past credit record to refer to, which is why alternative checks become important.
3. What is the benefit of the Account Aggregator framework?
It allows borrowers to share their financial data securely and with consent. This helps lenders see real income and transaction patterns instead of relying only on static documents.
4. How do NBFCs use AI to prevent fraud?
AI is mainly used to spot unusual behaviour. Things that don’t match normal patterns get flagged early so they can be reviewed before a loan is approved.
5. What is “Contextual Credit”
Contextual credit means offering a loan based on the situation and timing of the need. Instead of a generic approval, the credit decision considers where, why, and how the money will be used.
