Cyber-Physical Customer Management for AI Internet of Things-Enabled Banking - Panjala Srinu; Mr. Farooq Hussain Mohammad (Utilitas Mathematica, 2025)
In this project, an automated deep learning-based framework for interbank KYC in AI-based cyber-physical banking is proposed. A deep biometric architecture was used to model the customer’s KYC and anonymize the collected visual data to ensure the customer’s privacy. The symmetric-asymmetric encryption-decryption module in addition to the blockchain network was used for secure and decentralized transmission and validation of the biometric information. A high-capacity fragile watermarking algorithm based on the integer-to-integer LSB algorithm for the secure transmission and storage of in-person banking documents is also proposed. The proposed framework was developed and validated using a web application for the collection of biometric finger prints of customers. The biometric information of bank customers such as fingerprint and name is embedded as a watermark in the related bank documents using the proposed framework. The results show that the proposed security protection framework can embed more biometric data in bank documents in comparison with similar algorithms.