Razorpay Launches AI Model for Payments Built With NVIDIA and AWS

In August 2026, Razorpay announced the launch of Razorpay Vulcan, which is a transformer-based AI foundation model that is designed specifically for payments. Built using advanced technology from NVIDIA and AWS, the model is designed to improve payment success rates, fraud detection, risk assessment and checkout experiences across India’s digital payments ecosystem.

Razorpay also said the model has been trained on approximately 3 trillion data points across 4 billion payments, with around 3,000 signals analyzed per transaction. The company stated that the model is designed as a shared intelligence layer across multiple payment functions rather than as separate models for routing, fraud, risk and checkout.

The development comes as India’s digital payments ecosystem continues to expand across UPI, cards, net banking, wallets and other payment methods. Razorpay said its internal study covering 1.5 million shoppers and more than 51,000 businesses identified recurring issues around failed transactions, payment delays and drop-offs across different markets. Components of the model have already been deployed across Razorpay’s network for routing, fraud and risk decisions. According to the company, early deployments include customers such as Blinkit, Bachat and redBus.

The model is also designed to support several payment functions, including real-time routing, network-level fraud detection, risk assessment for cash-on-delivery orders and personalized checkout recommendations. For merchants, Razorpay said the model is intended to reduce failed transactions, payment drop-offs, fraud losses and return-to-origin orders. For consumers, the objective is to improve the reliability and predictability of digital payments.

Impact on the ICT Market 

Razorpay has built an AI foundation model to reduce failed transactions and strengthen fraud detection across its payments network. Vulcan was trained on nearly three trillion data points across four billion payments. It analyses about 3,000 signals per transaction to support routing, fraud detection, risk assessment and checkout personalization. NVIDIA GPUs powered the model’s training and operation, while Amazon Web Services (AWS) supported its development and deployment through Amazon SageMaker.

Vulcan brings these functions under a shared intelligence layer instead of relying on separate machine learning models. It can select the payment route most likely to succeed, detect fraud across merchants, and assess return-to-origin risks for cash-on-delivery orders. The system also detected and stopped eight times more international card fraud and identified five times more fraudulent or disputed transactions without increasing alerts.

Impact on the Applied AI in Finance Market

The global applied AI in finance market size was calculated at USD 14.82 billion in 2025 and is predicted to increase from USD 17.80 billion in 2026 to approximately USD 92.53 billion by 2035, expanding at a CAGR of 20.10% from 2026 to 2035. 

According to Precedence Research, the market is primarily driven by the increasing adoption of automation solutions in the BFSI sector, coupled with the increase in the number of fintech startups around the world. Numerous AI providers are partnering with consulting companies to develop AI-enabled platforms for the finance sector. Financial institutions are increasingly deploying AI to detect anomalies, prevent fraud, and manage risk in real time. Machine learning models analyze large volumes of transactional data to spot suspicious behavior and reduce false positives.

Several companies all over the world are constantly investing in research and development of quantum computing technologies. In addition, AI developers are rapidly integrating advanced functions in the GPT models to enhance financial operations. Thus, technological advancements in quantum computing and ongoing developments in GPT models are expected to create growth opportunities for market players. Several financial institutions are also increasingly leveraging AI to simplify large datasets, improve decision-making, lower operational costs, and improve customer experience. This market is generally driven by the increasing data volumes, digital transformation, and the surging need for real-time insights and regulatory compliance in the finance sector.

Impact on the Payment Security Market

The global payment security market size was estimated at USD 33.61 billion in 2025 and is predicted to increase from USD 38.29 billion in 2026 to approximately USD 117.38 billion by 2035, expanding at a CAGR of 13.32% from 2026 to 2035.

According to Precedence Research, the market growth is driven by the need to protect consumer data, comply with regulations, and keep up with the evolving threat landscape. The growing adoption of digital payment methods significantly drives the market revenue for payment security. Compliance with PCI DSS guidelines and the rise in fraudulent activities in e-commerce further fuel this market growth. As payment applications become more prevalent across different sectors, the demand for sophisticated payment security solutions in digital commerce continues to rise.

The digital transformation has also led to an increase in cybersecurity threats, such as unauthorized access, fraud, and data breaches in payment security. Cybercriminals are becoming more sophisticated in exploiting vulnerabilities, making it essential for businesses to prioritize comprehensive payment security strategies. Unauthorized access to payment data can result in severe financial losses and reputational damage, driving widespread adoption of payment security solutions and contributing to market growth.

Expert Opinion

Kiran Jagannath, Head of FSI and Conglomerates, AWS India and South Asia, said,

“Razorpay is reimagining payments intelligence at India scale with an AI foundation model, built on Amazon SageMaker that consolidates billions of transaction insights into a single, continuously learning intelligence layer, replacing fragmented ML models with unified AI that delivers higher payment success rates, rapid iteration, and enterprise-grade security for mission-critical payment flows.”