📈 Finance Use Case: Real-Time Loan Monitoring with Kafka

Overview

This project demonstrates how a bank can use Apache Kafka to process loan applications in real-time, enabling quick decision-making for loan approvals or rejections. The system enhances the efficiency and reliability of loan processing, providing a scalable solution for handling high data volumes.

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📝 Scenario

A bank aims to process loan applications in real-time to provide instant decisions: approved or rejected. By leveraging Apache Kafka, the bank can ensure high-performance, fault-tolerant data streaming and processing.

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🔄 Workflow

1️⃣ Producer: Sending Loan Application Data

• Role:
  The banking application sends customer data (e.g., income, credit score, loan amount) to a Kafka topic named "loan_applications".

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2️⃣ Broker: Managing Data Flow

• Kafka's Responsibilities:
  • Divides the "loan_applications" topic into partitions (e.g., by region) for parallel processing.
  • Ensures high availability with data replication, even in case of server failure.
  • Retains data for a specified duration for potential reprocessing.
• Benefits:
  Kafka guarantees data reliability and enables parallel processing, resulting in faster decisions.

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3️⃣ Consumer: Loan Decision Prediction

• Consumer's Role:
  • Reads loan application data from the Kafka topic.
  • Uses a machine learning model to analyze features like income and credit score to decide:
    • ✅ Approved: High credit score and sufficient income.
    • ❌ Rejected: Low credit score or excessive loan amount.
• Output:
  For this project, the results are displayed in a Jupyter Notebook console for simplicity.

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Features

• Real-time data processing using Apache Kafka.
• Reliable and scalable data streaming with partitioning and replication.
• Machine learning integration for automated loan decision-making.

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Technologies Used

• Apache Kafka: For real-time data streaming.
• Python: For building producers and consumers.
• Jupyter Notebook: For demonstrating the consumer workflow and ML decision logic.

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Getting Started

1. Clone this repository:

   git clone https://github.com/dwiknrd/spark_kafka_integration.git

2. Install dependencies:

   pip install -r requirements.txt

3. Start Kafka brokers and create the required topics.
4. Run the producer script to simulate loan applications.
5. Start the consumer to process and display loan decisions.

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Contributing

Feel free to fork this repository and submit pull requests. Contributions are welcome!

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License

This project is licensed under the MIT License.