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Real-Time Fraud Detection System
Built a streaming-based decisioning system to identify and prevent fraudulent transactions in real time across high-volume financial workloads.
Context
Financial platforms needed faster, real-time fraud detection without blocking legitimate users or slowing transactions.
Challenge
Traditional batch-based systems created delays in detection, leading to financial exposure and poor user experience.
Approach
Streaming-first event processing for incoming transactions
Real-time scoring layer for anomaly detection
Decision engine for approve / flag / block outcomes
Architecture Overview
Transaction Events
↓
Streaming Ingestion Layer
↓
Real-Time Processing & Scoring
↓
Decision Engine (Approve / Block / Flag)
↓
Alerts + Audit + Storage
Outcome
Real-time transaction decisions
Improved fraud detection responsiveness
Stable performance under high traffic load