Cyber-Physical Customer Management for AI Internet of Things-Enabled Banking

Abstract

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.