Real-Time Payment Fraud Prevention with Event-Driven KYC

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Real-time payment systems require fraud prevention tools that can detect and stop suspicious activity within seconds, as traditional batch-based methods may be too slow to prevent losses, reputational damage, and compliance risks. ISO 20022 is presented as a key enabler because its standardized, detailed transaction data can improve contextual analysis, anomaly detection, customer profiling, and false-positive reduction. Event-driven KYC extends identity verification beyond onboarding by monitoring behavior and transactions continuously, triggering targeted checks such as biometric authentication when risk indicators arise. The proposed fraud-prevention framework combines AI and machine learning, real-time document and biometric verification, AML and watchlist screening, behavioral analysis, and adaptable workflows. Didit positions its modular AI-native identity platform, including ID verification, liveness detection, face matching and search, AML monitoring, APIs, and no-code workflow tools, as a way for institutions to implement these capabilities within existing payment infrastructure.

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