Enhancing financial security with machine learning-based credit card fraud detection

The increasing rate of credit card fraud is a major concern to the financial systems across the globe, and hence there is a need to come up with efficient and sustainable solutions for detection. This paper proposes a machine learning approach for credit card fraud detection using feature selection and deep learning to enhance the detection rate and speed. The proposed system employs Neighborhood Component Analysis (NCA) for feature selection to select the most important features that can improve the classification process and minimize computational complexity. The feature set is then passed through a Gated Recurrent Unit (GRU) network, a deep learning model that is effective in identifying temporal patterns in transaction data for fraud detection. The proposed framework is compared with other machine learning techniques on a standard credit card fraud detection dataset, and it outperforms the other techniques in terms of accuracy, precision, recall, and F1 score. The use of NCA for feature selection enhances the detection time and scalability of the system and therefore makes it suitable for real-time applications in high-traffic financial environments. This study shows that the integration of the proposed feature selection and deep learning can enhance financial security and help in the fight against credit card fraud.