Beyond transactions: How PhonePe and Razorpay are using AI to improve digital payments

PhonePe and Razorpay are extending payments beyond simple money movement. Their AI systems use transaction information to make payments more reliable, detect risk, recommend relevant financial products, and support merchants’ online growth. This matters because payment acceptance is only the starting point for many small businesses.

Razorpay’s Vulcan 5.2 model studies transaction attributes to predict missing information, fraud risk, and conversion likelihood. PhonePe’s Kill Switch considers bank response times and recent success rates. It can warn customers when a bank is probably unavailable, helping avoid pending transactions. PhonePe also uses context to suggest products such as travel insurance.

Razorpay’s broader goal is helping merchants attract demand, increase conversions, and encourage repeat purchases. The supplied excerpt also introduces Ray and Agent Studio, but does not explain their functions. The panel’s larger message is that AI can connect payment infrastructure with more accessible financial services and stronger digital commerce.

Contextual payments connect a transaction with the customer’s immediate situation. Instead of treating every payment as an isolated transfer, a platform uses transaction context to understand what the person may need next. This can make financial services more relevant and accessible.

PhonePe gives a clear example. Someone booking an international flight may benefit from travel insurance. A customer travelling only within India may receive a more suitable suggestion, such as a bus-booking option. The recommendation is linked to the activity rather than shown randomly.

PhonePe says it uses algorithmic guardrails to avoid irrelevant recommendations. This matters because useful context can improve discovery without overwhelming customers with unsuitable products. The article presents contextual payments as a way to move beyond transactions while preserving relevance and connecting consumers with financial products that fit their circumstances.

The article describes a large opportunity at several levels. More than 500 million people already use UPI for everyday payments, while another 500 million could potentially be brought into the digital payments ecosystem. Existing users may also need financial products beyond basic payments.

The data opportunity is similarly large in detail. Haria says each transaction contains nearly 3,000 attributes. Razorpay’s Vulcan 5.2 uses those attributes to assess fraud, fill missing information, and estimate whether a payment will convert. These signals can support both customers and merchants.

The supplied excerpt does not state how many businesses use AI agents. It only says Razorpay discussed two initiatives, Ray and Agent Studio, and introduces autonomous agents without explaining their deployment or reach. Therefore, no reliable business-user figure can be given from the provided text.

Transaction data gives an AI system a detailed view of payment activity. It can include signals about the payment attempt, the bank’s response, recent outcomes, and other attributes. Studying these signals helps platforms identify patterns that are difficult to find through manually designed rules alone.

Razorpay’s Vulcan 5.2 is described as a transaction-led foundation model. It analyzes nearly 3,000 attributes in each transaction. The model can predict missing information, estimate the likelihood of fraud, and determine whether a transaction is likely to convert. PhonePe’s Kill Switch uses bank response times and recent success rates to estimate payment success.

These predictions can guide action. A platform may flag suspicious activity, improve the chance that a buyer completes a purchase, or warn a customer that a bank is probably down. The article contrasts this with conventional machine learning, saying transformer-based models can detect smaller patterns.

Reducing failed or pending payments improves the customer experience at a critical moment. PhonePe says customers care less about a failure itself than about a transaction getting stuck in a pending state. Early warnings can prevent wasted attempts and make the platform feel more dependable.

For merchants, stronger payment reliability can preserve potential sales. Razorpay’s AI also estimates whether transactions will convert and helps identify suspicious activity. These capabilities can reduce risk while improving the chance that a potential buyer becomes a paying customer. Merchant tools aimed at demand, conversion, and retention address problems beyond payment collection.

The likely consequence described by the panel is greater trust and repeat use. PhonePe says reliable experiences encourage users to return. Razorpay links better online operations with small-business growth. The article does not quantify the resulting gains, so exact improvements in sales or retention are not stated.

PhonePe’s Kill Switch is an internal machine-learning model for payment reliability. It predicts whether a transaction is likely to succeed by considering signals such as bank response times and recent transaction success rates. Its purpose is to prevent customers from entering an avoidable pending state.

Razorpay’s Ray and Agent Studio are introduced as AI initiatives for helping merchants manage everyday business operations. However, the supplied excerpt ends immediately after naming them. It does not describe their features, workflows, or relationship to autonomous agents. The article therefore supports only a limited explanation of their roles.

The broader process is clear: AI is being applied not only to payment authorization, but also to merchant operations and digital commerce. Yet details about autonomous agents, including what actions they take or how widely businesses use them, are absent from the provided text and should not be inferred.

In a typical UPI payment, a customer authorizes money to move from their bank account to a merchant’s bank account through the UPI network and participating payment applications. The payment platform presents the payment interface and status, while banks process and confirm the transfer. The supplied article does not describe these mechanics directly.

AI can improve several points around that flow. PhonePe’s Kill Switch evaluates bank response times and recent success rates before a transaction begins. It can warn customers when a bank is probably unavailable. Razorpay’s model examines nearly 3,000 transaction attributes to estimate fraud risk and conversion likelihood.

These improvements can reduce uncertainty, avoid unnecessary pending attempts, and help merchants complete more sales. AI can also support relevant product recommendations and merchant operations. The article focuses on these uses rather than explaining UPI’s technical architecture or the exact movement of funds between participants.