Unit 2: AI & Machine Learning

🏢 How Razorpay Catches Fraud Before You Blink

Every time you tap "Pay" on a Razorpay checkout, something extraordinary happens in the background. Within 50 milliseconds — faster than a human eye blink — a machine learning model analyses over 200 data points: your device fingerprint, transaction history, typing speed, location, time of day, and merchant risk profile. It then decides: legitimate or fraud?

Razorpay processes over 300 million transactions every month across 8 million+ Indian businesses. Their AI-powered fraud detection system, called Thirdwatch, uses deep learning models trained on billions of historical transactions to catch fraudsters with 99.5% accuracy. Every second, these models make thousands of split-second decisions — blocking suspicious payments, flagging risky orders, and protecting both merchants and customers.

Behind the scenes, a team of ML engineers at Razorpay's Bangalore HQ uses Python, TensorFlow, scikit-learn, and AWS SageMaker to continuously train and improve these models. When a new fraud pattern emerges — say, a sudden spike in card-not-present fraud during Diwali sales — the AI adapts within hours, not weeks.

What if YOU had built this? What if you could create systems that think, learn, and make decisions faster than any human? That's exactly what this chapter teaches you — from the fundamentals of AI to building your own intelligent workflows.