Razorpay Unveils Vulcan: India’s First AI Payments Foundation Model

In a major technological leap for financial infrastructure, Indian fintech pioneer Razorpay has launched Vulcan, India’s first transformer-based AI payments foundation model. Built to address transaction drops, digital fraud, and processing delays across the country’s vast digital economy, the platform marks a fundamental shift in how digital transactions are evaluated. Rather than relying on isolated machine learning tools for individual tasks, the unified model evaluates billions of real-time signals across every stage of a transaction. Developed in collaboration with global technology leaders, the infrastructure provides a continuous intelligence layer capable of serving India’s growing digital marketplace.

A Unified Intelligence Layer Powered by NVIDIA and AWS

Historically, digital payment gateways relied on separate, specialized machine learning models to manage fraud detection, smart routing, risk scoring, and checkout personalization. Because these traditional models operated independently, they often missed cross-functional insights hidden within broader transaction patterns. Razorpay Vulcan solves this fragmentation by adapting transformer technology—the architecture underlying modern Large Language Models—specifically for financial transaction data.

To train a model at this scale, Razorpay processed nearly 3 trillion data points spanning 4 billion transactions across its merchant network. This computationally intensive training was powered by NVIDIA accelerated computing hardware and deployed on the cloud via the Amazon SageMaker platform. By analyzing roughly 3,000 distinct signals per transaction in real time, the model constantly learns from ecosystem-wide trends, creating compounding intelligence that improves continuously with every processed payment.

Performance Metric	Traditional ML Approach	Razorpay Vulcan Foundation Model
Model Architecture	Disconnected, single-use ML models	Single unified transformer foundation model
Transaction Signals Analyzed	Limited per-silo parameters	~3,000 real-time signals per transaction
Transaction Success Rates	Baseline routing efficiency	8% to 10% uplift in successful payments
International Card Fraud Detection	Standard rules-based alerts	8x increase in fraud detection accuracy
Dispute & Fraud Identification	High rate of false-positive alerts	5x more fraud identified without extra alerts
UPI Personalization (Magic Checkout)	Generic payment option listing	40% increase in preferred UPI app presentation

Core Capabilities: Hyper-Routing, Fraud Defense, and RTO Intelligence

By unifying data processing into a single neural network, Vulcan delivers noticeable improvements across several critical operational fronts for merchants and consumers:

• Hyper-Precision Payment Routing: Before a payment is initiated, the model evaluates bank health, network traffic, and historical gateway reliability in real time. It automatically routes the transaction down the path most likely to succeed, significantly cutting down on technical declines.
• Network-Level Fraud Prevention: Unlike localized security checks, Vulcan identifies cross-merchant fraud instantly. If a compromised card or suspicious identity is flagged on one platform, the model alerts the broader network instantly, increasing international card fraud detection by eight times.
• Return-to-Origin (RTO) Risk Intelligence: For e-commerce businesses accepting Cash on Delivery (COD), the system evaluates buyer risk metrics before checkout, protecting growing e-commerce sellers from costly unfulfilled shipments.
• Personalized Checkout Experiences: Through Razorpay’s Magic Checkout, the model predicts individual buyer preferences, resulting in a 40 percent increase in shoppers seeing their preferred UPI app, driving over 100,000 additional completed sales every month.

Fortifying India’s $350 Billion E-Commerce Future

As India’s e-commerce market advances toward an estimated $350 billion valuation by 2030, transaction reliability and consumer trust remain top priorities. Building seamless, secure checkout experiences is crucial as broader demographics adopt digital financial apps for daily commerce.

By eliminating point-of-sale friction and drastically reducing false fraud alerts, foundation models represent a turning point for national fintech infrastructure. The launch highlights how domestic deep-tech innovations can solve large-scale structural challenges. As detailed on the official Razorpay Vulcan platform, embedding real-time artificial intelligence directly into the payments stack establishes a new global benchmark for financial technology.