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https://github.com/mineshmelvin/fraud-detection-nifi-etl

An ETL pipeline to identify and flag potentially fraudulent transactions in real time. The data is sourced from multiple transactional systems across different regions and processed with historical data to detect patterns.
https://github.com/mineshmelvin/fraud-detection-nifi-etl

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An ETL pipeline to identify and flag potentially fraudulent transactions in real time. The data is sourced from multiple transactional systems across different regions and processed with historical data to detect patterns.

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README 

        
### Real-Time Financial Fraud Detection ETL Pipeline



#### Problem Statement

A financial institution requires an ETL pipeline to identify and flag potentially fraudulent transactions 

in real time. The data is sourced from multiple transactional systems across different regions and 

processed with historical data to detect patterns. The pipeline needs to process high-velocity 

transactional data with high availability and minimal latency, while simultaneously performing batch 

processing on historical data to continuously refine fraud detection models.



#### Requirements

##### Data Sources:



Real-time transaction streams from multiple databases (e.g., MySQL, Oracle).

Historical transactions stored in a data warehouse (e.g., Snowflake or Amazon Redshift).

Additional data from third-party fraud intelligence providers (e.g., IP blacklists, suspicious card data).

Transformations and Enrichments:



##### Real-Time Transformations:

Validate data and remove duplicates.

Enrich transactions with geolocation data (e.g., IP to country mapping).

Perform aggregations like moving averages, transaction frequencies, and time-based anomalies.



##### Batch Transformations:

Identify and profile unusual transaction patterns using machine learning models.

Flag suspicious transactions based on historical fraud patterns.

##### Machine Learning Scoring:

Apply a pre-trained machine learning model for fraud detection on both real-time and batch data.

Use scoring thresholds to label transactions as "potential fraud" or "clear."

##### Output and Action:

Store results in a NoSQL database (e.g., Cassandra) for real-time dashboarding.

Trigger alerts for flagged transactions to a monitoring dashboard.

Store processed data in a data lake for compliance and auditing.



#### ETL Pipeline Design

##### Data Ingestion Layer:



Use Apache Kafka as the central ingestion mechanism for real-time transactions. Kafka serves as the source of truth for all incoming data and provides reliable messaging for distributed systems.

Use NiFi or Kafka Connect to pull data from transactional databases and push to Kafka.

Batch data is extracted periodically from the data warehouse, and new batches are published to Kafka topics for processing.

Stream Processing Layer:



Use Apache Flink for real-time transformation and fraud detection. Flink processes streams from Kafka and enables low-latency transformations and anomaly detection on the fly.

Flink applies geolocation enrichment, calculates transaction patterns, and integrates with an ML model for immediate scoring.

Real-time results are stored in Cassandra, where dashboards pull from a dedicated table optimized for read-heavy operations.

Batch Processing Layer:



Use Apache Spark to perform complex historical analysis and refine fraud detection models. Spark jobs run periodically (e.g., every 24 hours) on new data in the data warehouse.

Spark aggregates transaction patterns, builds new features, and retrains models with up-to-date data.

The updated model is exported to a model registry (e.g., MLflow) and redeployed to the Flink application for scoring.

Machine Learning Layer:



##### Model Registry and Serving: 

Use a model management tool (like MLflow or Seldon) to store, version, and serve models.

Real-time models run directly in Flink, while batch models run in Spark.

Models flag transactions based on predefined probability thresholds and update these thresholds dynamically as new data is processed.



##### Data Storage Layer:



Data Lake: Store raw and processed data in a data lake (e.g., Amazon S3 or Hadoop HDFS) to support ad-hoc analysis and meet audit requirements.

Data Warehouse: Load aggregated data and processed results into a data warehouse like Snowflake for business intelligence and further analytics.

NoSQL Database: Use Cassandra for low-latency reads for fraud alerts on a real-time dashboard.

Monitoring and Alerts:



Implement Prometheus and Grafana to monitor the health of Kafka, Flink, and Spark jobs.

Integrate an alerting system like PagerDuty or Slack notifications for high-probability fraud cases.

Orchestration Layer:



Use Apache Airflow or AWS Step Functions to orchestrate batch ETL jobs and manage dependencies between ingestion, processing, and model retraining.

Schedule periodic model retraining and redeployment, ensuring the most accurate model is always in use for real-time scoring.



##### Security and Compliance:



Encrypt sensitive data both at rest and in transit.

Apply role-based access control (RBAC) and audit logging on data flows and model usage.

Regularly check compliance with regulations such as GDPR for data handling and privacy.



##### Technical Complexity

This ETL pipeline involves:



Multi-layer processing: Real-time and batch processing work together to improve the fraud detection model.

Distributed, low-latency processing: Flink enables real-time transformations and scoring with low latency requirements.

Machine Learning integration: Models are deployed in both streaming and batch environments, with a continuous feedback loop from processed historical data.

Advanced orchestration: Dependencies are managed across streaming and batch jobs, with scheduled retraining and redeployment of models.

Data storage diversity: Data is managed in NoSQL databases, data lakes, and data warehouses to suit varying requirements.

Challenges

Scalability: Processing large volumes of transactional data in real-time while balancing batch workloads.

Data Quality: Handling duplicate or incomplete data in a streaming context.

Model Drift: Monitoring the performance of fraud detection models to address changes in transaction patterns over time.

Operational Complexity: Ensuring high availability and failover mechanisms for critical pipeline components.



#### Solution



##### 1. Project Structure and Component Overview

   

###### Components:



Kafka for data streaming.

NiFi for real-time data ingestion, routing, and initial transformations.

Spark for real-time and batch processing.

MySQL/PostgreSQL for storage of processed data and metadata.

Docker Compose for orchestrating these services.



###### Directory Structure:



etl-fraud-detection/



├── Dockerfile



├── docker-compose.yml



├── nifi/           # Contains NiFi-specific flows and configs



├── kafka/          # Contains Kafka topics and consumer/producer scripts



├── spark/          # Spark job scripts



├── db/             # Database schema setup scripts



├── scripts/        # Utility scripts for ETL jobs and data transformations



└── README.md



##### 2. Dockerizing the Components

Dockerfile: Create Docker images for each service. Below is a general example for NiFi, Kafka, and Spark.

Docker Compose: Define each service in a docker-compose.yml file to streamline setup and networking.



##### 3. Implementing the ETL Pipeline

Kafka Topics: Set up a topic for raw transaction data (transactions_raw) and a second for processed/filtered data (transactions_cleaned).

NiFi Flow: Use NiFi to pull data from Kafka, apply initial transformations, and send clean data back to Kafka or directly to the database.

Spark Jobs: Write batch and streaming jobs to process and analyze data from Kafka and ingest data into the database.



##### 4. Testing and Running

Docker Compose Up: Start all services with docker-compose up.

Testing Workflow: Run NiFi and Spark jobs, ensure Kafka messages flow correctly, and verify data persistence in the database.