“No credit history? No loan.”
That’s not just a slogan—it’s a painful reality millions of Indians face every single day. In a country where the system hinges on formal financial records, large parts of India are left behind. Not because these people are bad borrowers. But because the financial system isn't built to understand their reality.
The truth is, India’s financial ecosystem is skewed toward formal documentation, but millions of hardworking individuals in India remain invisible to the system. Their behavior, discipline, and trustworthiness remain unseen, simply because they lack the traditional credit score that mainstream finance has come to rely on.
The Problem: Traditional Credit is Blind to India
As of April 25, 2022, over 160 million consumers in India were considered credit underserved. That’s not just a number - it’s a reflection of the many hardworking individuals who are financially responsible, yet invisible to traditional credit systems.
Think about it:
The auto driver who pays his rent on time every month but has never taken a formal loan.
The kirana store owner who accepts UPI payments daily and manages his inventory with precision, but can’t get a business loan because there’s no formal paper trail.
The homemaker who skillfully manages the household budget, saves for emergencies, and plans for her children’s education—yet has no credit score to prove her financial discipline.
These people are not credit risks. They’re just not seen.
The traditional system only checks for a formal credit history, missing the bigger picture of someone’s actual money habits. And that’s where alternative credit assessment methods step in—to give these 160 million+ Indians the financial recognition they deserve.
Who Gets Left Behind?
The people who get sidelined in this system include:
• Daily-wage earners who are part of a huge, vibrant workforce but lack formal documentation of their financial behaviors.
• Migrant laborers who move between cities for work, paying rent and utility bills on time, but who have no established credit history.
• Informal workers like local artisans or small business owners, who don’t have access to formal banking but are financially sound.
• First-time borrowers who might be young or starting out, but are being denied access to financial services due to lack of history.
For these individuals, applying for a loan often feels like knocking on a door that was never meant to open.
A New Way: Trust Through Alternative Data
What if we stopped relying solely on traditional credit scores and started looking at other, more accessible forms of data to evaluate a person's financial trustworthiness?
Enter alternative data. Alternative data refers to any financial activity or behavior that’s not traditionally captured by banks and credit bureaus. This could include:
• UPI payment patterns: Regular use of UPI for payments shows a person’s commitment to financial transactions, making them a more reliable borrower than a score might suggest.
• Rent and utility bill payments: Paying rent and utilities on time reflects a person’s ability to manage regular financial commitments.
• Prepaid mobile recharges: This might seem trivial, but timely top-ups can show an individual’s responsibility toward managing finances.
• E-commerce behavior: Regular online shopping and payment history reflect an individual’s ability to track their expenses, manage finances, and plan for future purchases.
These behaviors reflect financial discipline better than a one-time credit event like a personal loan ever could.
How AI Makes It Work
Alternative data is powerful, but it’s also raw, unstructured, and messy. That’s where Artificial Intelligence (AI) steps in.
AI doesn’t just sift through this data—it understands it, drawing meaningful insights that go beyond what a traditional credit score can provide. Here’s how:
1. Context-Aware Risk Assessment
Traditional systems look at one-time data points (like loan repayments or credit card usage), but AI can consider the nuances in someone’s financial life. It understands:
• Seasonal incomes: Many people in India, like farmers or construction workers, don’t have a consistent monthly income. AI can factor in seasonal spikes and lows in income to gauge financial reliability.
• Location-based costs: AI can account for variations in living costs based on the region. A ₹5,000 monthly rent in rural areas has a different economic impact compared to a ₹5,000 rent in metropolitan areas.
• Cash flow consistency: Rather than focusing on a single credit event, AI evaluates someone’s steady flow of cash in and out of their life.
2. Inclusive, Bias-Free Evaluation
Human decisions are often influenced by bias, whether consciously or unconsciously. This can include biases based on caste, region, education, or profession. AI, when properly trained, doesn’t care about these factors. It looks at behavioral data alone, treating every individual equally based on how they manage their finances.
With the right AI models, the financial system can create a level playing field for those often left out by the traditional system.
3. Adaptive Repayment Design
One of the biggest challenges with traditional loans is the rigid repayment structures that don’t always match people’s earning cycles. AI can create adaptive repayment plans, offering weekly micro-payments instead of the traditional monthly EMIs. This gives borrowers the flexibility to repay based on their income flow—something that works much better for daily-wage workers, migrant laborers, and people with irregular incomes.
Already in Motion: Who’s Leading This Shift?
The world is already embracing these innovations:
• RiskSeal is using phone usage and social signals to build trust for lending, proving that your digital footprints can tell more about your reliability than a credit score.
• Many fintech startups are using psychometric tests and smartphone metadata to offer nano-loans to people without a credit history.
• Government initiatives like Jan Dhan, Aadhaar, and UPI are laying the foundation for a new financial ecosystem in India, where digital footprints are the key to financial trust.
The Ethical Imperative
While we celebrate these technological advancements, we must also acknowledge the responsibility that comes with using alternative data:
• Consent: It must be clear, informed, and active. No one should ever be assessed without their permission.
• Privacy protocols: We must ensure that personal data is kept secure and is used only for its intended purpose.
• AI Bias: Algorithms, even those designed to be neutral, need regular audits to ensure they aren’t unintentionally reinforcing biases.
This isn’t just about tech evolution—it’s about human impact at scale. Financial inclusion must go hand in hand with ethical responsibility.
Why This Really Matters
Imagine a world where financial trust isn’t restricted to those with a credit score:
• A young girl in Chhattisgarh gets an education loan because she’s reliably paid her mobile bill for the last two years.
• A vegetable vendor in Amritsar is able to expand his cart thanks to his steady UPI sales history.
This is the future we’re striving for. The future where millions of people no longer need favors to access finance—they just need recognition of how they live, earn, and spend.
Final Thought
Financial inclusion isn’t about lowering the bar to let more people in. It’s about broadening the lens to see the value in behaviors that don't fit neatly into the traditional framework. India isn’t invisible—it’s just been misunderstood.
It's high time the financial system started to look beyond paper and recognize the financial behaviors that shape the lives of millions.