Detecting Digital Financial Transaction Frauds Using CNN Model

The Unified Payments Interface has become increasingly popular as a convenient and user-friendly digital payment platform. However, the surge in Digital Transaction has also resulted in a rise in fraudulent activities, presenting a significant security challenge. This paper introduces a novel approach to detecting fraudulent Digital Transaction by leveraging convolutional neural networks (CNN). By incorporating the CNN model, this study aims to offer a cost-effective solution for identifying Digital Financial fraud. The CNN's ability to analyse patterns in data makes it particularly suitable for fraud detection in Digital Financial Transactions. We evaluate our proposed methodology using a comprehensive dataset that includes both fraudulent and genuine transactions. Our results demonstrate a significant improvement in fraud detection accuracy and efficiency compared to existing methods. This research contributes to the advancement of secure digital payment systems by presenting an innovative approach to detect fraud transactions. Financial institutions and payment service providers can utilize this methodology to enhance the security of Digital Financial Transactions and protect users from fraudulent activities.