TutorialsReal-Time Fraud Detection

Real-Time Fraud Detection

A complete tutorial on using Flink and Kafka to detect credit card fraud anomalies within 50ms, before sinking the flagged events to PostgreSQL.

The Stateful Streaming Challenge

Fraud detection cannot occur in nightly batch Spark loads. If a compromised card is swiped consecutively across three continents within a 4-minute array, the processing must happen instantaneously. This tutorial outlines how DataFlow AI orchestrates stateful Apache Flink streams visually.

Step-by-Step Logic Assembly

The Source: We attach a Kafka topic `financial_transactions` streaming 30,000 JSON events per second into our Bronze layer.
Tumbling Windows: We drag a Window Aggregation node onto the Canvas, configuring a 5-minute Tumbling Window grouped by `credit_card_hash`. The AI Copilot translates this automatically to Flink's time-windowed Watermark syntax.
Outlier Identification: We implement a secondary Map node utilizing an IF-statement. If the Tumbling Window recognizes >$5,000 processed within a 5-minute span geographically dispersed by >1,000 miles, the internal transaction `fraud_flag` mathematically resolves to TRUE.
Low-Latency Sink: The resultant flagged schema is routed instantly to an AWS Aurora setup via a JDBC Connector, allowing internal Banking application logic to deny the transaction in effectively real-time.