How India's Top Banks Are Using AI to Fight Real-Time Payment Fraud

The Fraud Landscape (2025–2026)

• UPI fraud trends: SIM swap, QR manipulation, social engineering, account takeover
• Why rule-based detection hits a ceiling at transaction volume
• The latency problem: detecting fraud in < 100ms at scale

AI Approaches That Are Working

• Anomaly detection: behavioral baselines per user
• Graph neural networks: mapping fraud rings across accounts
• NLP: analyzing transaction metadata and customer communications
• Ensemble models: combining multiple signals for precision

Architecture of a Real-Time Fraud Detection System

Model inference (< 50ms) → Decision engine → Alert / Block

• Event streaming (Kafka) → Feature computation (real-time) →
• Feature engineering: velocity checks, geolocation, device fingerprinting
• Model retraining: how often, what triggers it, how to avoid drift

Results from the Field

• Detection time: hours → minutes → milliseconds
• False positive reduction: 60–75% improvement
• Annual savings: millions in prevented losses

Getting Started

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• The minimum viable fraud AI: what you need before you build
• Data requirements: transaction logs, user profiles, labeled fraud cases
• Build vs. partner decision for banking CISOs