Authors: Assistant Professor G.Chandram, Bhargavi Sammeta
Abstract: The rapid growth of digital banking and electronic payment services has increased the demand for secure and reliable authentication mechanisms capable of protecting financial transactions from fraud and unauthorized access. Conventional card-based banking systems remain vulnerable to card theft, skimming attacks, credential compromise, and identity fraud. To address these security challenges, this paper presents an intelligent cardless financial transaction framework that combines Machine Learning (ML) with Multi-Factor Authentication (MFA) to provide a secure and efficient user authentication process. The proposed system utilizes facial recognition as the primary biometric verification method, where facial features are extracted using a deep Convolutional Neural Network (CNN) based on the pretrained ResNet-50 model. Sensitive biometric information and One-Time Passwords (OTPs) are protected using the Advanced Encryption Standard (AES) algorithm to ensure secure storage and transmission. After successful facial verification, an OTP is generated and verified as an additional authentication factor before authorizing the financial transaction. This multi-layered authentication strategy significantly enhances protection against identity theft, fraudulent transactions, and unauthorized account access while maintaining a user-friendly banking experience. The integration of deep learning, biometric authentication, encryption, and OTP verification provides a scalable and robust security framework for next-generation digital banking systems. The proposed approach demonstrates the potential of intelligent authentication technologies to improve transaction security, operational efficiency, and customer trust in modern financial services.
