India's UPI platform processed 23.66 billion transactions in July 2026, marking continued growth. With payment margins under pressure, the fintech industry is pivoting toward digital credit and AI-driven services to boost profitability. Investors are now monitoring how banks and fintech firms balance this expansion with strict regulatory norms and credit quality management.
The Unified Payments Interface (UPI) has set a new benchmark, processing 23.66 billion transactions worth ₹29.88 lakh crore in July 2026. While these figures confirm the platform's role as the backbone of India's digital economy, the focus for banks and fintech companies is rapidly evolving. The industry is moving beyond simply increasing payment volume, shifting attention toward monetization through credit products and AI-powered financial services.
The Shift Toward Profitability
For years, the fintech sector has prioritized mass adoption of digital payments. However, processing billions of transactions brings limited revenue due to the current lack of a Merchant Discount Rate (MDR) for most UPI payments. Companies are now looking at the next phase of growth: digital lending and embedded finance. By integrating credit instruments directly into the UPI flow, lenders can offer loans at the point of sale. This helps them capture a fee or interest income, which is necessary to offset the low margins in payment processing.
Industry leaders are watching how these credit transactions grow. With monthly credit-on-UPI volumes rising, firms are finding ways to use payment data to better understand customer spending habits. This data helps in offering faster, more personalized loans compared to traditional methods.
Role of AI in Financial Services
Artificial Intelligence is becoming a key tool for lenders and payment companies to manage this transition. Beyond customer support, AI is now being used for predictive underwriting—a method of assessing a borrower's ability to repay by analyzing their digital footprint, such as utility bill payments and transaction history, rather than relying solely on traditional credit scores. This is particularly useful for 'new-to-credit' customers who lack formal credit records.
AI is also being deployed to streamline operational efficiency. Companies are using machine learning to detect fraud in real-time, reducing the risk of financial loss. Additionally, AI-based tools are helping smaller businesses automate collections and inventory management, potentially making them more creditworthy over time.
Risks and Regulatory Watch
Investors should keep in mind that this pivot to credit comes with risks. The Reserve Bank of India (RBI) has shown increased concern regarding the rapid growth of unsecured lending. Regulatory oversight remains strict, and any new rules regarding credit products or non-banking financial company (NBFC) lending could impact how quickly these firms can scale their loan books.
There are also operational challenges. As transaction volumes reach record highs, the reliability of the system is vital. Periodic network outages or bank-side issues remain a concern, which could lead to tighter quality-of-service standards from regulators. Furthermore, while AI offers speed, it introduces technical risks like 'black-box' decision-making, where the logic behind a loan approval may not be transparent. Companies will need to ensure their AI models comply with regulatory requirements for explainability and fairness.
The next important trend to track will be the sustainability of these new revenue models. Investors may look for updates on how much credit growth contributes to the bottom line of fintech companies and banks, and whether these firms can maintain healthy asset quality while expanding their loan portfolios.