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Real-Time Fraud Detection with Streaming ML

Fraud Detection

Real-Time Fraud Detection with Streaming ML

  • Use Case: Financial institutions (banks, payment gateways, e-commerce) need to detect fraudulent transactions in real time. Traditional batch fraud detection is too slow, leading to revenue loss and customer dissatisfaction. With streaming ML on GCP, fraudulent activity can be identified instantly.

Objective

  • Build a low-latency fraud detection system.

    Process millions of financial transactions per second.

    Apply machine learning models in real time to classify normal vs. fraudulent activity.

    Ensure scalability, fault tolerance, and integration with enterprise systems.

Project Description

  • This project designs a streaming fraud detection pipeline using GCP. Transactions are ingested from different payment sources and processed in real time using Dataflow. ML models deployed on Vertex AI classify anomalies. If a fraud is detected, alerts are sent to admins and blocked in the system. Data is also stored for audits, compliance, and model retraining.

    The architecture ensures real-time decision-making, privacy-preserving storage, and automated retraining of fraud models using federated or batch learning approaches.

Key Technologies & Google Cloud Platform Services

  • Google Cloud Service Purpose
    Pub/Sub Ingests and streams large volumes of transaction events in real time.
    Dataflow Real-time stream processing (filtering, aggregation, anomaly detection logic).
    Vertex AI Trains, deploys, and serves ML models for fraud detection.
    BigQuery Stores historical transaction data for analysis, reporting, and retraining models.
    Cloud Functions Triggers actions like blocking transactions, sending fraud alerts, or updating records.
    Cloud Logging & Monitoring (Operations Suite) Monitors pipeline performance and detects anomalies in system behavior.
    Cloud Storage Stores raw transaction logs, training datasets, and model artifacts.
    Looker Studio (Data Studio) Visual dashboards for fraud monitoring, trends, and compliance reporting.
    Cloud Key Management Service (KMS) Manages encryption keys for sensitive financial data.