UPI Inflows as a Mirror of MSME Creditworthiness

BEYOND RATIOS SERIES - EPISODE 1

It started with a kirana store in Tirunelveli.

The bank had rejected his ₹4 lakh loan request — citing insufficient collateral and lack of formal ITRs.

DISCLAIMER : This is an AI Generated image - only for illustration

Frustrated, the store owner showed his GPay dashboard: ₹7.8 lakh worth of receipts over 6 months, from 300+ unique customers, all paying through QR.

The credit officer paused. He’d never been taught how to read a digital trail as a proxy for creditworthiness.

The Central Question on UPI Inflows:

Can digital inflows substitute financials?

Let’s look at what UPI inflows tell us — if interpreted right.

Indicator	What it Suggests	Comfort Lever
Consistency of inflows	Business stability	±10–15% monthly fluctuation is acceptable
Unique payer count	Retail depth, diversity	>100 distinct payers shows genuine activity
Repeat customer pattern	Product stickiness, loyalty	High repeat ratio = customer trust
Ticket size variation	Organic transactions	Uniform ₹ values → red flag for round-tripping
Time-of-day patterns	Natural business cycles	Clustered or odd-hour flows = investigate

“But how do we know it’s not fake?”

This is every credit officer’s first question. And it’s valid. UPI-based inflows can be manipulated — just like fake invoices, dummy sales in ITRs, or bloated inventories.

The answer lies in triangulation and behavioral signals.

• Are the payer names diverse?

• Is the timing organic?

• Are flows linked to Meesho/WA orders or delivery trails?

• Is GST voluntary (even if not mandatory)?

  
  

You don’t need perfect proof. You need converging signals.

How Credit Teams Can Use This:

1. Bank statement deep dive: via AA, scan 6–12 months UPI credits.

2. Match for signal pattern: look for consistency, diversity, oddity.

3. If comforted, use this to:

   
   

   • Loosen traditional income norms

   • Extend ticket size

   • Offer shorter-tenure working capital lines

     
     

Tool	What to Observe	What it Tells You
Bank statement	Source and type of credits	Real sales, or just capital infused?
UPI dashboards	Time-stamped transactions	Active business? Diverse payers?
WhatsApp/WA Biz	Order volume, chat logs	Are they really selling or inflating?
GST filings	Monthly vs. quarterly data	Compliant behavior or workaround?
Behavior trail	Cash withdrawals, internal transfers	Are books clean or camouflaged?

Is it possible?

Absolutely — the tools already exist. Here's why:

• Account Aggregators (AA) are now live and allow real-time access to bank and UPI transaction data across institutions — with borrower consent.

  
  

• Cash-flow-based lending models are evolving rapidly, with many fintechs using UPI/QR, SMS, and bank data to assess income stability and repayment capacity.

  
  

• GST, BBPS, and utility payment patterns are being triangulated with bureau and bank statements by platforms like Perfios, Karza, FinBox, Crediwatch etc.

  
  

• WhatsApp Business APIs, invoice scanning, and geo-location metadata are being used in underwriting by B2B supply chain financiers.

  
  

  Who's doing this already?

  
  

  🟢 Neo-lenders and Fintechs:

  
  

  • Indifi, Flexiloans, NeoGrowth, Lendingkart, Kinara Capital→ All rely on digital trails for underwriting thin-file MSMEs

    
    

  • Jupiter, Fi, and Slice use transaction behavioral analytics for even consumer credit.

    
    

  🟢 Traditional Banks (in pilots or specific segments):

  
  

  • SBI has partnered with AA ecosystem and launched pilots on flow-based lending.

    
    

  • Axis Bank, ICICI Bank, and IDFC FIRST have been actively integrating GST + bank + bureau signals into SME loan appraisal workflows.

    
    

  • HDFC Bank’s SmartHub Vyapar enables merchant QR-based sales data collection to power credit assessments.

So why isn’t it mainstream yet?

Because:

• Legacy systems are siloed and slow to integrate new data streams.

• Credit officers are not yet trained to "read" behavioral and digital signals the way they are taught to read audited financials.

• There's no regulatory push yet mandating digital trail integration — so it remains "good to have" and not "must-have."

• Credit committees still prefer PDFs and traditional templates over dashboards.

Closing Thought:

“The future of MSME underwriting isn’t in more paperwork. It’s in reading new data trails with an old-fashioned lender’s eye.”

QRs won’t replace balance sheets. But they might just unlock a generation that was always fund-worthy, but never form-worthy.

Disclaimer:

The views and insights shared in this article are based on publicly available information, regulatory guidance, industry reports, and real-world observations. They are intended for educational and discussion purposes only. The mention of any companies, platforms, or practices does not constitute an endorsement. Readers are advised to exercise discretion and consult institutional policies or regulatory guidelines before applying any of the concepts discussed. The author does not accept any liability for actions taken based on this content.