Design and Implementation of ATM Security System Using AI and Internet of Things

Typical Automated Teller Machine (ATM) security systems are mainly based on Personal Identification Number (PINs) and post-incident monitoring which are reactive in nature and can be compromised through theft of credentials and physical interference. The current paper is a proactive and multi-layered ATM security model based on artificial intelligence (AI), Internet of Things (IoT), and secure web services to thwart fraudulent transactions in real time. The suggested system will consist of AI-supported facial recognition to verify the identity of users, mobile two-factor authentication to verify the intent of the user, real-time IoT-supported tampering detection, and RFID-based access control to authorized maintenance crew. It is a hybrid processing architecture, in which real-time sensing and control are processed at the edge, AI tasks that require significant computation are offloaded to a specific server, and all decisions that require security are handled by a centralized back-end. The functional viability of the proposed approach is experimentally validated with successful fraud protection, low-latency fraud-detection and dependable multi-factor authentication. The findings reveal that the given architecture offers a retrofittable and scalable solution to the issue of improving ATM security, going beyond the conventional reactive procedures.