ENHANCING PAYMENT SECURITY THROUGH THE IMPLEMENTATION OF DEEP LEARNING-BASED FACIAL RECOGNITION SYSTEMS IN MOBILE BANKING APPLICATIONS

Abstract

The rapid growth of mobile banking and the increasing reliance on smartphones for financial transactions have brought convenience to users but have also exposed them to various security risks. Traditional authentication methods, such as passwords and PINs, are vulnerable to theft, compromise, and unauthorized access. To address these challenges and enhance payment security, deep learning-based facial recognition systems have emerged as a promising solution. This research explores the implementation of deep learning techniques, specifically convolutional neural networks (CNNs), in facial recognition systems for mobile banking applications. By leveraging the advanced capabilities of CNNs in image analysis and pattern recognition, facial recognition systems can provide a secure and user-friendly authentication mechanism for mobile banking users. This study discusses the architecture, benefits, challenges, and future prospects of integrating deep learning-based facial recognition into mobile banking applications, aiming to strengthen payment security and protect users' financial information in the rapidly evolving landscape of mobile banking.