Why Choose This Project?

Fraudulent activities in banking, e-commerce, and online transactions are a growing threat. Traditional fraud detection systems often analyze data in batches, which introduces delays. A real-time fraud detection streaming pipeline enables immediate analysis of transactions, flagging anomalies as they happen.

This project helps students understand stream processing, anomaly detection with ML, and cloud-based data pipelines, which are critical in fintech, cybersecurity, and big data analytics.

What You Get

• Real-time ingestion and processing of transaction data

• ML-based fraud detection using anomaly detection or classification models

• Streaming dashboards with alerts for suspicious activity

• Scalable, fault-tolerant streaming pipeline

• Integration with databases for storing transaction history

• Automated notifications (email/SMS) for flagged transactions

Key Features

Technology Stack

Cloud Services Used

Working Flow

1. Transaction Ingestion

   • Incoming transactions are streamed via Kafka, AWS Kinesis, or Pub/Sub.

2. Stream Processing

   • Transactions are processed in real-time using Flink, Spark, or Lambda functions.

3. Fraud Detection Model

   • ML models evaluate transactions for anomalies (suspicious patterns, unusual amounts, location mismatches).

4. Alert Trigger

   • If suspicious activity is detected, an alert is sent via email/SMS/Slack.

5. Storage

   • All transactions (fraudulent and legitimate) are stored in DynamoDB, S3, or BigQuery for future reference.

6. Monitoring & Visualization

   • Dashboards (Grafana/Kibana) show fraud detection metrics, flagged cases, and system performance.

7. Scaling

   • Pipeline automatically scales with incoming traffic using cloud auto-scaling features.