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Research Paper | Computer Science | India | Volume 12 Issue 11, November 2023 | Rating: 5.6 / 10
Credit Card Fraud Detection Using Machine Learning Algorithms
Jai Gupta
Abstract: This study explores the application of machine learning algorithms for the detection of credit card fraud. Utilizing a comprehensive dataset of 284,807 credit card transactions, we implemented anomaly detection methods to identify fraudulent activities. The methods include autoencoders, which have shown effectiveness in handling imbalanced datasets. Our results demonstrate a high accuracy of the model, suggesting its potential in real-time fraud detection and prevention in financial systems. This study contributes to the evolving field of cybersecurity by providing a robust framework for credit card fraud detection. The purpose of this study is to develop and evaluate a machine learning-based model for the detection of credit card fraud, addressing the challenge of data imbalance and focusing on anomaly detection methods. The significance of this research lies in its potential to enhance the security of financial transactions and protect consumers from fraud. By leveraging advanced machine learning techniques, this study contributes to the development of more effective fraud detection systems.
Keywords: Credit Card Fraud, Machine Learning, Anomaly Detection, Autoencoders, Cybersecurity
Edition: Volume 12 Issue 11, November 2023,
Pages: 1774 - 1779