Biometric Authentication for Secure Digital Banking

Abstract

Digital banking has revolutionized financial services by enabling convenient online transactions, account management, investments, and payments. However, it has also increased security risks such as identity theft, phishing, credential compromise, and unauthorized access. Traditional authentication methods, including passwords and PINs, are vulnerable to cyberattacks, leading financial institutions to adopt biometric authentication technologies. Biometric authentication uses unique physiological and behavioral traits such as fingerprints, facial recognition, iris patterns, voice recognition, and behavioral biometrics to verify user identities. Enhanced by Artificial Intelligence (AI), Machine Learning (ML), and computer vision, modern biometric systems offer improved accuracy, security, and user convenience. This study analyzes biometric authentication mechanisms in digital banking, comparing various biometric modalities based on authentication accuracy, security, usability, and implementation complexity. It also examines multimodal biometric systems that combine multiple biometric traits to enhance reliability and prevent spoofing attacks. A secure biometric framework is proposed, integrating fingerprint recognition, facial verification, liveness detection, encrypted biometric templates, and multi-factor authentication. Experimental results show that biometric authentication provides higher security, lower error rates, and a better user experience than traditional methods. The findings highlight biometric authentication as a key technology for securing digital banking systems. Future advancements in AI, blockchain, and privacy-preserving biometrics are expected to further strengthen authentication mechanisms and support the development of secure, scalable, and user-friendly financial services.