DEVELOPING A REAL-TIME CREDIT CARD FRAUD DETECTION SYSTEM USING STREAM MINING TECHNIQUES

Abstract:

Credit card fraud has become a significant concern for financial institutions and cardholders worldwide. With the rapid growth of digital transactions, traditional fraud detection systems often struggle to keep up with the evolving tactics employed by fraudsters. To address this challenge, a real-time credit card fraud detection system is proposed, leveraging stream mining techniques to analyze incoming transaction data and identify fraudulent activities promptly.

This research focuses on developing an efficient and scalable fraud detection system capable of processing high-volume streams of credit card transactions in real-time. The system integrates various stream mining techniques, including online learning, concept drift detection, and ensemble methods, to continuously update and adapt the fraud detection model based on the evolving nature of fraud patterns.

The proposed system follows a multi-stage approach. First, an initial model is trained using historical transaction data labeled as fraudulent or legitimate. Feature engineering techniques are applied to extract relevant features from the transaction data, including transaction amount, merchant information, location, and other contextual variables. The initial model is then deployed to analyze incoming transactions in real-time.

As new transactions arrive, the system dynamically updates the model using online learning algorithms. This enables the system to adapt to changes in fraud patterns and concept drift, ensuring the detection of emerging fraud techniques. Ensemble methods are utilized to combine the outputs of multiple models, improving overall accuracy and reducing false positives.

To evaluate the system’s performance, extensive experiments are conducted using large-scale credit card transaction datasets. The evaluation metrics include accuracy, precision, recall, and F1-score. The proposed system is compared with existing fraud detection approaches to demonstrate its effectiveness in detecting fraudulent transactions accurately and efficiently.

The results show that the developed real-time credit card fraud detection system using stream mining techniques achieves high accuracy and provides timely detection of fraud, minimizing financial losses for both cardholders and financial institutions. The system’s scalability and adaptability make it suitable for handling large transaction volumes, enabling real-time fraud detection in high-speed transaction environments.

In conclusion, this research presents a novel approach to credit card fraud detection using stream mining techniques. By leveraging online learning, concept drift detection, and ensemble methods, the proposed system offers an effective solution for detecting fraudulent activities in real-time. The system’s ability to adapt to evolving fraud patterns and handle high transaction volumes makes it a valuable tool for financial institutions in combating credit card fraud.

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