Real-Time Fraud Detection and Prevention System for Financial Transactions with Data Quality, Governance, and Observability

Description:

This project aims to develop a comprehensive real-time fraud detection and prevention system for financial transactions that leverages big data technologies while ensuring data quality, governance, and observability. The system will analyze and classify financial transactions as genuine or potentially fraudulent, helping financial companies prevent fraud and reduce losses.

The solution will encompass the following components:

• Ingesting real-time transaction data from various sources using Apache Kafka
• Preprocessing, cleaning, and standardizing transaction data using Apache Spark
• Engineering features to identify fraudulent transactions
• Training and deploying a machine learning model for fraud classification
• Storing transaction data, processed data, and prediction results in Delta Lake
• Orchestrating and automating the data pipeline using Apache Airflow
• Monitoring and alerting stakeholders when potential fraud is detected using Prometheus and Grafana
• Ensuring data quality by validating, cleaning, and profiling data throughout the pipeline
• Implementing data governance by securing sensitive data, ensuring data privacy compliance, and maintaining a data
• catalog
• Incorporating data observability through monitoring, logging, tracing, and alerting capabilities