Blog 109: UPI next act: Payments revolution to credit infrastructure?
- Idea2Product2Business Team
- 1 hour ago
- 4 min read
UPI transformed digital payments in India. Its next evolution could be even bigger.
Explore how UPI is transitioning from payment rails to credit rails.
What it means for product managers, AI leaders, and cloud architects.
For nearly a decade, India celebrated UPI for one simple reason. It made moving money almost invisible. Scan, Authenticate, and Done.
No wallet top-ups. No account numbers. No waiting. No wondering whether the recipient received the money. The transaction simply happened.
Credit infrastructure: A sophisticated challenge
Banks have spent decades refining its judgment (whether to lend to someone or not).
Approve the wrong borrower, and defaults increase.
Reject the right borrower, and revenue disappears.
Price risk incorrectly, and profitability erodes.
Banks are simultaneously balancing growth, capital adequacy, risk exposure, regulatory obligations, fraud, customer trust, and long-term profitability.
Key difference between UPI as a payment medium vs. UPI as a credit infrastructure
For payments, the metrics that matter include:
Daily Active Users
Monthly Transactions
Payment Success Rate
Merchant Acceptance
Average Transaction Time
Transaction Costs
Hence, these metrics optimise for movement.
While, for credit products, the metrics will be:
Are customers repaying on time?
Did underwriting predict behaviour accurately?
Are customers borrowing responsibly?
Is fraud increasing?
Is portfolio risk acceptable?
Does customer lifetime value justify acquisition costs?
Are lending decisions explainable to regulators?
These metrics will be behaviour focussed.
In lending, removing every point of friction can become a source of systemic risk.
We need new layers to enable accountability
The UPI payment system aims to have high availability, low latency, strong security, massive scalability, fault tolerance, and real-time processing.
Credit infrastructure will inherit the above and then add newer layers. Such as:
Identity verification
Credit line eligibility
Available credit calculation
Fraud scoring
Risk evaluation
Regulatory policy checks
Customer consent
Limit utilisation
Settlement routing
Audit logging
Each of the above layer may belong to a different organisation.
Some belong to banks. Others to fintechs. Some to NPCI. Others to credit bureaus.
Some are internal AI models. Others are external APIs.
Hence, this becomes a distributed decision-making workflow.
While latency remains important. But consistency, observability, resilience, explainability, and traceability become equally critical.
For product managers, cloud & AI architects, this changes the product design entirely.
The real product is the decision engine behind
Historically, lending followed a predictable sequence.
Customer > Application > Bank Underwriting > Approval > Loan Account > Disbursement > Repayment
Every stage was visible and took time. With pauses so that risk could be assessed.
Now, imagine a customer scanning a QR code to purchase a refrigerator on credit.
Instead of simply checking the account balance, the ecosystem quietly begins asking several questions in milliseconds.
Does the customer already have an approved credit line?
Is today's purchase behaviour consistent with historical spending?
Has the customer exceeded internal exposure limits?
Is the merchant eligible?
Does this transaction resemble known fraud patterns?
Is repayment capacity still within acceptable thresholds?
Does the regulator require additional disclosures?
Should this purchase qualify for promotional financing? and etc.
The decision engine also needs to plan for credit eligibility service failure
A customer standing at a pharmacy purchasing essential medication and then the credit eligibility service fails unexpectedly. The decision engine must plan for the following:
Can our underwriting services degrade gracefully? Can decisions continue if one external dependency becomes unavailable? How do we trace an approval decision that involved twelve microservices? Can regulators audit every step? How do we replay historical decisions? Can AI models roll back safely after deployment? Can we explain model outputs six months later?
Evolution of UPI: Some examples
Case study 1: Enable RuPay credit cards on UPI
For years, credit cards were primarily accepted at merchants with POS terminals. While effective for organized retail, this limited their reach among India's millions of QR-code-enabled small merchants.
By allowing RuPay credit cards to be linked with UPI, the ecosystem effectively expanded credit acceptance to merchants with no POS terminals.
Instead of asking merchants to adopt new hardware, the existing UPI QR network became a channel for credit-based transactions.
From a product management perspective, this is an elegant example of leveraging existing customer behaviour instead of creating new behaviour.
Case study 2: Credit Line on UPI
The Reserve Bank of India enabled banks to offer pre-sanctioned credit lines through UPI, allowing approved customers to access credit directly through the UPI interface instead of applying for a separate loan each time.
Banks with stronger underwriting, richer risk intelligence, and more effective AI governance will outperform.
Can every recommendation be explained?
Can every decision be audited?
Can every model be monitored for drift?
Can the bank reproduce a decision six months later? etc.
Case study 3: Bank of Baroda's SHG financial inclusion initiative
Bank of Baroda introduced an overdraft facility delivered through a UPI-linked credit line for eligible women in Self-Help Groups (SHGs) with PMJDY (Pradhan Mantri Jan Dhan Yojana) accounts.
This is aiming to make formal credit easier to access for underserved communities.
To conclude: Almost every major player in the payment space can process a UPI payment. Tomorrow, many will also be able to offer credit through the same interface. They will need to answer questions like:
Which customer should receive additional credit today?
Which small business is likely to grow with timely working capital?
Which borrower needs proactive assistance before becoming delinquent?
Which AI recommendation should be overridden by a human?
It's about helping the financial system make better decisions - responsibly, transparently, and at scale.
Jump to blog 100 to refer to the overall product management mind map.
I wish you the best for your journey. 😊
