Advanced Credit Card Fraud Detection System

Project Overview

This project implements an advanced fraud detection system for credit card transactions. It leverages machine learning, real-time data streaming, and cloud technologies to detect and flag potentially fraudulent transactions before they happen. The system is designed to be highly scalable and capable of operating in real-time.

Architecture

Technologies Used

• Apache Kafka (Data Ingestion)
• Apache Spark (Real-Time Stream Processing)
• AWS Lambda (Fraud Alert Notifications)
• TensorFlow (Machine Learning Model for Fraud Detection)
• DBT (Data Transformation)
• AWS S3 ( Data Storage)
• Docker (Containerization)

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How to Run

Step 1: Clone the Repository

Clone the repository to your local machine:

git  
cd 

Step 2: Install Dependencies

Install the required dependencies using pip:

pip install -r requirements.txt

Step 3: Set Up Kafka

Ensure Apache Kafka is installed and running:

bin/zookeeper-server-start.sh config/zookeeper.properties &
bin/kafka-server-start.sh config/server.properties

Step 4: Start Services

Run the following components:

1. Run Kafka Producer (Simulating Real-Time Transactions):

   python kafka_producer.py

2. Run Kafka Consumer (Processing Transactions):

   python kafka_consumer.py

3. Start Spark Processing for Fraud Detection:

   python spark_processing.py

4. Run Fraud Detection Model:

   python fraud_detection_model.py

5. Trigger AWS Lambda for Fraud Alerts:

   python lambda_trigger.py

Step 5: Docker Setup (Optional)

To deploy the entire system using Docker:

docker-compose up --build

This starts the Kafka producer, consumer, and other system components.

Outcome

• Reduced fraud losses by up to 40%.
• Improved customer trust and retention through proactive fraud detection.
• Reduced manual intervention in fraud detection processes.

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Testing the System

To test the fraud detection pipeline:

1. Run the Kafka producer to simulate transaction data.
2. Start the Kafka consumer to consume the data.
3. Observe Spark streaming console output for fraud detection logs.
4. If a fraudulent transaction is detected, AWS Lambda triggers an alert via email or SMS.