Credit Card Fraud Detection System Using Machine Learning

Credit Card Fraud Detection System Using Machine Learning

Nikita Nigade, Akanksha Nimbalkar, Shubhangi Patil, Priyanka Salve

1Computer Engineer, KJ College of Enggineering and Mangement Research

2Computer Engineer, KJ College of Enggineering and Mangement Research

3Computer Engineer, KJ College of Enggineering and Mangement Research

4Computer Engineer, KJ College of Enggineering and Mangement Research

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Abstract - credit card fraud detection is a growing challenge due to the rise in online transactions. This study explores machine learning techniques to address this issue, focusing on methods that handle imbalanced data, where fraudulent transactions are rare. We evaluate several models, including Logistic Regression, Decision Trees, Random Forests, and deep learning, using metrics like precision, recall, and F1 score. Data balancing techniques, such as SMOTE, and anomaly detection methods are also employed to enhance fraud detection. The results highlight the strengths and limitations of different approaches, providing insights for real-time fraud detection implementation. Effective fraud detection can reduce financial losses, improve security, and build customer trust. This work offers a comprehensive approach to credit card fraud detection with potential for real-world application and scalability.

Key Words: credit card fraud, machine learning, fraud detection, imbalanced data, anomaly detection, real-time implementation.