Using QR Codes for Payment Card Fraud Detection

Debit and credit card payments have become the preferred method of payment for consumers, replacing paper checks and cash. However, this shift has also led to an increase in concerns regarding identity theft and payment security. To address these challenges, it is crucial to develop an effective, secure, and reliable payment system. This research presents a comprehensive study on payment card fraud detection using deep learning techniques. The introduction highlights the significance of a strong financial system supported by a quick and secure payment system. It emphasizes the need for advanced methods to detect fraudulent activities in card transactions. The proposed methodology focuses on the conversion of a comma-separated values (CSV) dataset into quick response (QR) code images, enabling the application of deep neural networks and transfer learning. This representation enables leveraging pre-trained image-based architectures by encoding numeric transaction attributes into visual patterns suitable for convolutional neural networks. The feature extraction process involves the use of a convolutional neural network, specifically a residual network architecture. The results obtained through the under-sampling dataset balancing method revealed promising performance in terms of precision, accuracy, recall, and F1 score for the traditional models such as K-nearest neighbors (KNN), Decision Tree, Random Forest, AdaBoost, Bagging, and Gaussian Naïve Bayes. Furthermore, the proposed deep neural network model achieved high precision, indicating its effectiveness in detecting card fraud. The model also achieved high accuracy, recall, and F1 score, showcasing its superior performance compared to traditional machine learning models. In summary, this research contributes to the field of payment card fraud detection by leveraging deep learning techniques. The proposed methodology offers a sophisticated approach to detecting fraudulent activities in card payment systems, addressing the growing concerns of identity theft and payment security. By deploying the trained model in an Android application, real-time fraud detection becomes possible, further enhancing the security of card transactions. The findings of this study provide insights and avenues for future advancements in the field of payment card fraud detection.