CYBER-PHYSICAL CUSTOMER MANAGEMENT FOR INTERNET OF ROBOTIC THINGS-ENABLED BANKING

Abstract

Across the globe, in-person banking continues to play a significant role in financial services. Robotic service personnel at hybrid bank offices may increase productivity while cutting expenses. Hybrid banking needs an effective autonomous Know-Your-Customer (KYC). This research proposes an automated system for interbank KYC in robot-based cyber-physical banking based on deep learning. To protect the customer's privacy, a deep biometric architecture was used to model their KYC and anonymize the visual data that was gathered. The blockchain network and the symmetric-asymmetric encryption-decryption module were used to transmit and validate the biometric data in a secure, decentralized manner. For the safe transmission and storage of in-person financial documents, a high-capacity fragile watermarking technique based on the integer-to-integer discrete wavelet transform combined with the Z6 and A6 lattice vector quantization is also suggested. A Pepper humanoid robot was used to simulate and evaluate the suggested framework for automated biometric-based bank check collecting of handwritten checks from clients following COVID-19 pandemic safety protocols. Using the suggested framework, the biometric data of bank clients—such as their fingerprint and name—is watermarked and integrated in the relevant bank papers. Compared to comparable algorithms, the findings demonstrate that the suggested security protection framework can integrate more biometric data in bank papers. Moreover, compared to other suggested algorithms, the protected bank document quality is 20% better. Additionally, the banks' privacy needs may be met by the hierarchical visual information transmission and storage module that obscures people's identities in films that are gathered by robots. All things considered, the suggested framework may provide a quick, easy, and affordable interbank solution for upcoming in-person banking while abiding by banking laws and security standards.