Real-time Fraud Signal Orchestration with Didit, Flink, & Feature Stores

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Modern digital fraud, including synthetic identities, account takeovers, and deepfakes, requires real-time detection systems that can combine identity, behavioral, device, and transaction signals quickly. The proposed architecture uses Apache Flink to process high-volume event streams with low latency and identify suspicious patterns as they occur, while feature stores centralize reusable machine-learning features for consistent use in real-time inference and offline model training. Didit is presented as an AI-native identity verification platform that contributes fraud signals through document verification, passive and active liveness detection, face matching, AML screening, contact verification, IP analysis, and other checks. Integrating these outputs into Flink streams and feature stores can support immediate actions such as alerts, reviews, or transaction blocks, while reducing false positives through stronger identity evidence. The approach emphasizes a modular, scalable fraud-prevention system that can adapt to emerging threats, and notes Didit’s APIs, sandbox access, and free KYC tier as options for implementation.

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