The Lending Boom in India 2 Is Real - So Are the Defaults

Cracking the code on risk, reward, and smarter credit underwriting in Bharat’s biggest lending segment.

It’s often said — all roads in digital India eventually lead to FinTech.
Whatever your business — commerce, mobility, travel, B2B — eventually someone asks:

“Can we lend to our users and earn passive income through credit?”

It sounds simple. But lending isn’t plug-and-play. Especially not in India 2, where the opportunity is enormous — and so is the potential for default.

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🇮🇳 India 1-2-3: Who Are We Lending To?

Understanding India’s credit risk starts with segmentation refer Indus Valley Report 2025

India 1 - Affluent & Formal - Consuming Class

• Salaried, credit-aware, low-risk

• Served by banks and top NBFCs

• Low yields, low defaults

India 2 - The Big Middle - Aspirant Class

• Gig workers, traders, small businesses, low-income salaried

• New-to-credit or semi-formal

• Borrow for aspirations or emergencies

• High yield, high variability

• Biggest opportunity — and biggest risk

India 3 - Financially Excluded - Unmonetisable

• Rural, low-income, no credit footprint

• Access primarily via microfinance

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💣 The Lending Trap: Not Mispricing, But Misjudging

Recent write-offs by leading NBFCs/Bank weren’t about mispricing credit.

They failed because they misjudged borrower risk in India 2.

These lenders had distribution. What they lacked was visibility — on borrower intent, context, and volatility.

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📊 India 2 Lending Landscape (CRIF, March 2025)

We filtered the market for products with ticket sizes <₹50L and borrowers in semi-formal segments:

📌 Total India 2 Portfolio: ~₹43 L Cr and ~30 Cr active loans
🎯 Goal: Keep PAR 30+ under 2% while earning 18–36% yield

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❌ Why Do India 2 Borrowers Default?

Defaults here often reflect volatility, not intent:

• 🧾 Irregular income or job loss

• 🏥 Medical emergencies

• ☀️ Weather shocks (drought, flood)

• 🤝 Proxy borrowing (family/neighbors)

• 🔁 Loan stacking without repayment capacity

• 🧠 No awareness of credit score consequences

• 👤 Mobile/SIM/device switching to avoid tracking

• 💬 Bad advice: “Take the loan - no one will follow up”

Much of this is predictable - if you look beyond the bureau.

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🔍 The Vitto Way: Risk Visibility Before Disbursal

Vitto’s Alternate Risk Score is built to underwrite India 2 with more than just bank and bureau data.

We assess risk across six real-world data categories:

📊 Six Dimensions of Risk

Each borrower is scored (0–100) across these categories and tagged with specific flags.

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📈 Composite Risk Score & Lending Action

Vitto’s engine generates a composite risk score (0–100) that directly drives lending decisions.

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🚀 Final Word

India 2 isn’t too risky. It’s just too poorly understood.

Lenders relying on bureau and bank data alone will keep defaulting - and blaming the market.

With tools like Vitto’s Alternate Risk Score, you can:

• Spot fraud before disbursal

• Underwrite new-to-credit borrowers confidently

• Maintain high yields

• Bring PAR 30+ under 2%

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📬 Ready to test smarter lending?

📘 Read: The Hidden Risk Signals That Will Shape Lending in India 2
💻 Write to us to know more at info@vitto.money

The Hidden Risk Signals That Will Make or Break Your Lending Book

Everyone’s chasing yield. The winners are reading the signals no one else is watching!

Let’s get one thing straight: the art of lending, especially unsecured lending, is fundamentally about managing risk. The lender takes on risk arbitrage, and the borrower pays a premium for the convenience — typically a higher ROI for the lender.

Today, the game has changed. While traditional banks played it safe with secured lending, new-age lenders are hungry for yield — with some chasing returns of 26% and beyond, and a few fintechs even breaching 30%+ IRR targets.

So, what sets the winners apart in this high-risk, high-reward arena?

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The Digital Illusion: KYC ≠ Credit Assessment

Most lenders have digitized their workflows:

• Digital KYC ✅

• Bureau Pulls ✅

• E-Agreements ✅

These help prevent identity fraud or document forgery — but they don’t tell you if the borrower will pay you back.

And that’s the real risk.

So in a market where everyone is chasing the same segment with the same product, the real question is:

What’s your edge?

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Ask Yourself:

• Are you picking up early signals of future default from your current pipeline?

• Can you bucket and price borrowers based on actual risk?

• Do you have unique data points your competitors don't?

Because unsecured lending is all about identifying risk from unconventional signals — not just from a credit bureau.

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Risk Arbitrage Through Better Signals

Here’s what your typical credit model might miss:

Device and SMS Data » Stability of device, loan-seeking behavior, presence of fraud patterns

Granular Bank Transactions »True income patterns, cash flow health, bounce rates

Credit Bureau Snippets »Thin-file risks, repayment consistency, new credit spikes

Demographic Profile »Age, family size, dependents, location vulnerability

Business Profile »Vintage, seasonality, local economy exposure

Let’s say:

• A user has applied for 6 loans in 10 days.

• Just changed phone numbers.

• Has high spending on gambling or liquor apps.

• Shows unstable income and 4 dependents.

On paper? They may still pass KYC and show a 742 credit score.
In reality? That’s a high-risk borrower.

And now imagine having 200+ such variables to analyze. Manual underwriting? Herculean.

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Enter: Risk Scoring for Every Data Type

At Vitto, we’ve broken this down into 5 intelligent sub-scores:

1. 📱 Device & SMS Stability

2. 🏦 Banking Behavior

3. 🧾 Credit Bureau Data

4. 👤 Demographics

5. 🧳 Business Profile

Each gets a score (out of 100), then we compute a combined risk score — real-time, transparent, and auditable.

This isn’t just risk scoring — it’s risk intelligence.

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🤖 Loan Recommendation Before Disbursement

Instead of waiting till it’s too late, we use AI to:

• Recommend the right loan amount, EMI, interest rate, and tenure

• Flag borrowers likely to default (even if bureau score is high)

• Help partners build healthier, more profitable loan books

And it gets better — our system learns and improves as more data flows in.

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Embedded Credit Infra – Built for India

Vitto’s model doesn’t just assess — it embeds into your workflows. Whether you're a bank, NBFC, fintech, or platform, you can plug into our credit risk engine and underwrite with context and clarity.

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✳️ TL;DR:

Credit is not about who you lend to. It's about what you know about who you lend to.

And that’s the real differentiator in the lending game.

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Want to build a sharper, safer loan book?
Let’s talk → info@vitto.money

MFIs Can Now Lend Beyond Microfinance. Will We Use This Moment to Back MSMEs?

RBI reduced qualifying assets and opportunity of MSME Lending!

Image credit: Shutterstock

Last week, the RBI did something important — without a big headline, without a long speech. It quietly lowered the qualifying asset criteria for NBFC-MFIs from 75% to 60%.

To many, this might feel technical — another policy tweak.

But to those of us who’ve been working in the trenches of financial inclusion, it means this:

Why MFIs Are Best Placed to Serve MSMEs

• 🇮🇳 60Mn+ MSMEs in India, most are micro/nano and undeserved

• 🧑🏽‍🤝‍🧑🏽 MFIs have deep last-mile reach of 100Mn+ and borrower trust

• 📲 India Stack (UPI, GST, AA) has made MSMEs more visible digitally

With this, MFIs are in the perfect position to extend responsible credit to growing entrepreneurs — and the RBI just opened the door.

Now, the question is: Will they step up?

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From Group Loans to Growth Loans

MFIs have spent the last two decades building something extraordinary:

• Last-mile trust

• Deep borrower insights

• Discipline in small-ticket credit

But in the background, something else has been happening.

Many of those women who once started with ₹20,000 loans are now traders, tailors, shopkeepers, and agri-processors.. Their needs are different now. And yet, their credit access hasn't kept up.

This RBI move gives MFIs permission to evolve with their borrowers.

It’s no longer about just managing default risk — it’s about unlocking opportunity.

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MSME Lending ≠ Just Bigger Microloans

Lending to MSMEs is complex. No balance sheets. Seasonal income. Messy bank data. Partial GST. Cash flows mixed with UPI.

But the opportunity is real — if done right.

MFIs must avoid past mistakes of lending without structure. Today, we have the tools:

• Verifiable income via UPI, GST, Bank, AA

• Debt obligation checks

• Alternate data for deeper insight

• Intelligence to assess true repayment capacity

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How We Think About MSME Income Assessment at Vitto

When we started building for MSMEs, we kept running into the same problem: “How do we underwrite income if we don’t have standard documents?”

We broke it down into three layers:

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🟣 UPI-Based Income

A growing number of small businesses use UPI for everything — payments, inventory, collections.

But UPI data is messy. A single account shows rent, chai, diesel, and customer payments all in one thread.

What we do:

• Use AI to separate personal vs business inflows

• Look for consistency, not just volume

• Spot sudden drops or erratic behavior as early warnings

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🟠 GST-Based Income

GST returns give a formal lens on turnover. But we know many small business owners file just enough to stay compliant.

What we do:

• Cross-check GST with bank credits

• Look at multiple months to smooth seasonal volatility

• Flag mismatches between reported sales and actual receipts

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🟢 Alternate Data

This includes device data, SMS transactional data, fraud checks, social media presence, psychometric assessments, household assets, and more.

It might sound fuzzy, but combined, these data points create a strong, reliable profile.

What we do:

• Train income estimation models on diverse alternate data

• Generate confidence scores to support credit assessment

• Provide explainable insights for credit teams and auditors

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Vitto’s Role: Plug In Where You Need Support

We’re not trying to replace what MFIs already do well. We’re here to amplify it — with data, tech, and capital.

Here’s how we partner:

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🏪 Distribution

We already work with agent networks and on-ground partners who serve MSMEs. If you have a product but no reach — we can help.

💻 Tech Stack

From field officer apps to borrower-facing apps in regional languages. From AA and GST integrations to LOS/LMS and risk engines. Our system is modular — take what you need.

💰 Capital Partners

We’ve built relationships with NBFCs, HFCs, and wholesale funders who are keen to support responsible MSME portfolios. If you need capital — we can connect.

Vitto enables MSME lending with distribution, intelligence, and infrastructure.
To explore a partnership, write to us at info@vitto.money.

How Mobile Number Revocation List (MNRL) can impact your lending business?

Latest on RBI's directive to check MNRL by 31st Mar 2025

The Reserve Bank of India (RBI) dropped a game-changer on January 17, 2025: Regulated Entities (REs) must now align with the Mobile Number Revocation List (MNRL) and tighten fraud prevention by March 31, 2025. Why? Because a recycled phone number in the wrong hands can unlock your customers’ accounts—or worse, their loans.

At Vitto, we’ve already cracked this. Our Fraud Detection API now integrates MNRL checks, ensuring you’re compliant and protected. Here’s how we’re turning RBI’s mandate into your advantage:

Why MNRL Matters

When telecom operators recycle inactive numbers (every 3-6 months), a fraudster could inherit a number tied to your customer’s financial history. Risks? Unauthorized OTP access, loan fraud, or identity theft.

The RBI’s fix: mandatory MNRL verification and stringent due diligence.

Vitto’s Answer: Seamless Compliance + Fraud Defense

Our latest MNRL solution syncs with TRAI’s Digital Intelligence Platform monthly, scrubbing revoked numbers from your database—automatically. Paired with our Fraud Detection API, you get:

• Real-Time MNRL Checks: No recycled number slips through.

• Fraud Signals: UPI tracking, social media scans, cyber crime hotspot flags, and more.

• Plug-and-Play Compliance: Scrub existing customers or onboard new ones, effortlessly.

We’ve already helped lenders hit near 0% fraud rates and 90%+ auto-collection success. Now, we’re adding RBI compliance to the mix—without breaking a sweat.

Fraud Parameters

• Social Media

• UPI

• Cyber crime Hot-spots

• Telecom

• MNRL

• IP Address and VPN

Deadline: March 31, 2025. Miss it, and you’re exposed—to penalties and fraudsters. Our API keeps you ahead, updating with TRAI’s MNRL data monthly and handling number recycling like clockwork.

Do reach out to us to implement the same - info@vitto.money

RBI's Guidelines on Mobile Number Revocation (MNR) and Preventing Fraud

Latest update on Financial Fraud and DeepSeek development for BFSI

The Reserve Bank of India (RBI) has issued a circular on 17th Jan 2025 directing Regulated Entities (REs) to implement stricter measures to combat fraud. Key among these is the mandatory check on the Mobile Number Revocation List and the use of specific number series for customer communication. These steps will significantly enhance customer confidence and prevent fraudulent activities.

Why is it Crucial to Conduct Monthly Fraud Checks Alongside Mobile Number Revocation List Verification?

Understanding Mobile Number Revocation

When a mobile number becomes inactive, telecom operators reassign it to a new user after a cooling-off period. This means a number previously registered with a financial institution can end up in the hands of a different individual.

How Does This Lead to Fraud?

If a financial institution’s database still associates the revoked number with the original customer, the new user can gain access to sensitive financial data. Potential risks include:

• Unauthorized Account Access: The new owner of the reassigned number may receive OTPs, allowing them to log into mobile banking apps and perform transactions.

• Loan Fraud: A fraudster using a reassigned number can impersonate the previous owner to apply for loans, leveraging the original customer’s good credit history.

• Personal Data Exposure: Many apps rely on mobile numbers for authentication, increasing the risk of identity theft and financial fraud.

Vitto’s Fraud Detection API: A Robust Defense

To mitigate these risks, Vitto has integrated Mobile Number Revocation List checks into its Fraud Detection API. This feature complements our existing suite of fraud prevention measures, including:

• Social Media Monitoring

• UPI Presence Tracking

• Cyber Crime Footprint Analysis

• Telecom Data Verification

• Cyber Crime Hotspot Detection

• IP Address and VPN Scrutiny

• Mobile Number Revocation Checks (New)

By ensuring real-time verification of customer identities, financial institutions can prevent fraudulent transactions and enhance lending security.

Emerging Threats: AI-Powered Fraud and DeepSeek’s Impact on Finance

The recent emergence of DeepSeek, an advanced AI model from China, has disrupted both the AI and financial industries. While it presents opportunities for financial innovation, it also increases the risk of AI-driven fraud.

How Fraudsters Are Exploiting AI:

• Synthetic Identity Fraud: AI-generated identities make it harder to detect fraudulent loan applications.

• Deepfake Scams: Criminals clone voices and faces to deceive financial institutions.

• Automated Scam Operations: AI chatbots impersonate customer service representatives, tricking users into revealing sensitive information.

How Businesses Can Stay Ahead

1. AI-Powered Fraud Detection

Traditional fraud detection methods are inadequate. AI-driven solutions analyze transaction patterns in real-time, identifying anomalies before fraud occurs.

2. Behavioral Risk Monitoring

Analyzing user behavior over time helps detect fraudulent activities, such as synthetic identities or unusual spending patterns.

3. AI-Powered Document Forensics

Advanced AI verification systems detect inconsistencies in documents, invoices, and financial records.

4. Continuous Learning and Adaptation

Fraud techniques evolve daily. Continuous updates to fraud detection models based on real-world data are essential.

The Future of AI-Driven Fraud Prevention

AI’s rapid advancements present both opportunities and threats. Institutions that fail to adapt will be vulnerable to sophisticated fraud schemes. By implementing advanced AI-driven fraud detection strategies, businesses can safeguard their financial ecosystem.

Stay Ahead of Financial Fraud with Vitto

Want to learn more about how AI-powered fraud detection can protect your business? Reach out to us at info@vitto.money and explore how we can help you build a fraud-proof financial ecosystem.