Distributed hyperledger technology in FinTech with artificial intelligence assisted internet of things platform

• Published in: Expert systems Vol. 41; no. 6
• Main Authors: Li, Dengjia, Ma, Chaoqun, Zhou, Zhongding, Kuang, Xianhua, Lin, Zijun
• Format: Journal Article
• Language: English
• Published: Oxford Blackwell Publishing Ltd 01-06-2024
• Subjects: Artificial intelligence; Bank technology; Cybersecurity; distributed hyper ledger technology; Distributed ledger; FinTech; internet of technology; Internet of Things; security; Value creation
• ISSN: 0266-4720; 1468-0394
• DOI: 10.1111/exsy.13001

Artificial intelligence (AI) and distributed hyper ledger technology are both at the height of their hype cycles, which means they have the potential to be disruptive. This raises issues about new technologies' influence on future business structures, particularly in service‐based industries like finance. Even though numerous assumptions in reality point to the complementary use of these two technologies to produce new value creation potentials, there is little contemporary literature and study on the subject. Different perspectives on any of the two technologies mentioned above are insufficient for understanding potential synergies in financial services. Similarly, the internet of things (IoT) assistance for FinTech solutions has risen its significance in the upcoming transaction world. Therefore, this research proposes a distributed hyper ledger technology and AI for IoT‐based FinTech platform (DHLT‐AI‐FTP), which incorporates the benefits of each technology for secured and energy‐efficient financial transactions and communication. The AI technology and distributed hyper ledger technology handle the security part, and the communication part holds the efficient design of IoT implementation. The simulation evaluation of the proposed model ensures the improved security and efficiency of FinTech management and processing for real‐time business scenarios. The experimental analysis shows the significant performance of the proposed DHLT‐AI‐FTP with a better adaptability ratio of 98.56% compared to other existing approaches.