Feedzai

Feedzai RiskOps Platform Architectural Assessment

Feedzai operates as a high-velocity RiskOps ecosystem designed to process hyper-scale transaction volumes with sub-millisecond latency. The architecture is anchored by Railgun, a cloud-native streaming engine that manages stateful profiling and real-time scoring without relying on traditional heavy database lookups 📑.

Core Risk Orchestration & Intelligence

The platform distinguishes itself through a 'Whitebox' approach to AI, allowing institutions to audit the logic behind every risk decision.

• Feedzai IQ (Federated Learning): A privacy-preserving network that aggregates risk signals (TrustScore) across global banks to detect cross-institutional mule networks without sharing raw PII 📑.
• OpenML Integration: Facilitates the deployment of external models (Python/R/H2O) directly into the Railgun engine, eliminating the latency penalty of external API calls during scoring 📑.
• ScamProtect: Utilizes behavioral biometrics and device intelligence to identify Authorized Push Payment (APP) fraud where the user is technically authenticated but socially manipulated 📑.

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Operational Scenarios

• Real-Time Scoring: Input: ISO 20022 Transaction Stream → Process: Feature extraction via Railgun & Scoring via OpenML Model → Output: Block/Allow Decision + Explanation 📑.
• Model Deployment: Input: Data Scientist uploads Python model → Process: Transpilation to Java byte-code via Feedzai SDK → Output: Hot-swapped production model with zero downtime 📑.

Generative AI & Agentic Frameworks

Feedzai has integrated GenAI primarily for analyst augmentation rather than autonomous execution.

• RiskOps Copilot: Uses LLMs to auto-generate SAR narratives and summarize complex alert clusters, reducing manual investigation time 📑.
• Agentic Limits: While 'ScamAlert' provides interactive advice to consumers, fully autonomous agentic decisioning for transaction blocking remains governed by deterministic rules 🧠.

Evaluation Guidance

Technical evaluators should verify the following architectural characteristics:

• Railgun Latency: Benchmark the end-to-end latency of the scoring pipeline when utilizing complex OpenML models compared to native Feedzai models 🌑.
• State Management: Validate the memory overhead of maintaining stateful profiles for millions of entities in the Railgun memory grid 🧠.
• Explainability: Audit the 'Whitebox' explanations for GenAI-assisted decisions to ensure compliance with Model Risk Management (MRM) standards 📑.