Millions of Indians—students, fresh employees, gig workers, homemakers, and first-time borrowers—don’t have a traditional credit history.
Without a CIBIL score, banks often hesitate to approve loans.
But thanks to alternative data, many digital lenders can now evaluate borrowers even without past loan records.
This has opened the credit system to people who were previously “invisible” to banks.
Let’s understand how lenders use this new approach and what it means for you.
What Is Alternative Data?
Alternative data refers to non-traditional information that lenders use to judge your creditworthiness.
Instead of relying only on:
CIBIL score
Past loan history
Credit card behaviour
They now analyse real-life digital signals that reflect your financial discipline.
Types of Alternative Data Lenders Use
1. Mobile Phone Usage Patterns
Recharge history
Call patterns
App usage
Device stability
A stable mobile pattern indicates financial reliability.
2. UPI & Digital Payment Behaviour
Lenders analyse:
Regularity of transactions
Spending patterns
Payment timelines
Savings behaviour in online wallets
Consistent digital payments signal responsible money behaviour.
3. Bank Account Activity
Even without loans, your:
Salary credits
Spending pattern
Balance maintenance
Bill payments
…help lenders assess your financial stability.
4. Employment & Income Data
Job role
Salary slip
EPFO contribution
Gig worker income logs
Freelancer payment receipts
Stable income = lower risk.
5. Utility Bill Payments
Regular payments of:
Electricity bills
Gas bills
Postpaid mobile bills
Broadband bills
…act as proof of timely repayment behaviour.
6. E-commerce Footprints
Platforms share behavioural data such as:
On-time payments
Cash-on-delivery refusal rate
Product return patterns
This helps lenders understand reliability.
7. Social & Device Data (Used With Consent Only)
Some lenders analyse:
Device consistency
Location stability
Contact behaviour (not content)
Fraud patterns
This helps detect high-risk profiles.
Why Lenders Use Alternative Data
✔ To assess first-time borrowers
Since there’s no CIBIL score, alternative signals fill the gap.
✔ To bring more people into formal credit
Helps students, gig workers, and self-employed individuals.
✔ To improve accuracy of risk assessment
AI models combine 100+ signals to predict repayment ability.
✔ To reduce fraud
Device-level checks help identify fake applications.
How Lenders Analyse This Data
Lenders use:
AI
Machine learning
Behavioural scoring
Pattern recognition models
These systems process thousands of data points to generate a “creditworthiness score.”
This score decides:
Loan approval
Interest rate
Credit limit
Repayment tenure
Are There Risks?
Yes — borrowers should know:
❗ Privacy concerns
Data should be shared only with your consent.
❗ Higher interest rates
First-time borrowers may face slightly higher rates.
❗ Shorter loan amounts
Lenders may give small limits initially and increase later.
❗ Not all data is allowed
Regulators restrict what lenders can access.
Benefits for Borrowers
✔ Loans accessible even with no credit score
✔ Faster approval via AI-driven checks
✔ Better credit limits once trust is built
✔ Opportunity to build first CIBIL score
This helps millions join the formal credit ecosystem.
How You Can Improve Your Alternative Data Score
✔ Pay all bills on time
✔ Maintain stable bank balance
✔ Avoid UPI loan apps with poor repayment record
✔ Keep mobile number active & KYC updated
✔ Use digital payments regularly
✔ Keep a stable job or consistent income pattern
Small habits show strong financial discipline.
❓ FAQs
1. Can I get a loan without a CIBIL score?
Yes. Many lenders use alternative data to approve first-time borrowers.
2. Is alternative data reliable?
AI models improve accuracy by analysing real-life financial behaviour.
3. What is analysed most—UPI or bank data?
Bank account and UPI activity are key indicators.
4. Does it affect privacy?
Data is used only with consent and under RBI guidelines.
5. Will alternative data replace CIBIL?
Not fully. It complements CIBIL, especially for new borrowers.
Published on : 20th November
Published by : SMITA
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