Blockchain technology-based FinTech banking sector involvement using adaptive neuro-fuzzy-based K-nearest neighbors algorithm

Husam Rjoub; Tomiwa Sunday Adebayo; Dervis Kirikkaleli

doi:10.1186/s40854-023-00469-3

Blockchain technology-based FinTech banking sector involvement using adaptive neuro-fuzzy-based K-nearest neighbors algorithm

Financ Innov. 2023;9(1):65. doi: 10.1186/s40854-023-00469-3. Epub 2023 Mar 10.

Authors

Husam Rjoub^{1

2}, Tomiwa Sunday Adebayo³, Dervis Kirikkaleli⁴

Affiliations

¹ Department of Accounting and Finance, Palestine Polytechnic University-PPU, Hebron, Palestine.
² Department of Banking and Finance, Faculty of Economics, Administrative and Social Sciences, Bahçeşehir Cyprus University, 99010 Alayköy, Nicosia, Turkey.
³ Department of Economics, Faculty of Economics and Administrative Science, Cyprus International University, Northern Cyprus, 10 Mersin, Nicosia, Turkey.
⁴ Department of Banking and Finance, Faculty of Economics and Administrative Sciences, European University of Lefke, Lefke, Northern Cyprus, 10 Mersin, Turkey.

Abstract

The study aims to investigate the financial technology (FinTech) factors influencing Chinese banking performance. Financial expectations and global realities may be changed by FinTech's multidimensional scope, which is lacking in the traditional financial sector. The use of technology to automate financial services is becoming more important for economic organizations and industries because the digital age has seen a period of transition in terms of consumers and personalization. The future of FinTech will be shaped by technologies like the Internet of Things, blockchain, and artificial intelligence. The involvement of these platforms in financial services is a major concern for global business growth. FinTech is becoming more popular with customers because of such benefits. FinTech has driven a fundamental change within the financial services industry, placing the client at the center of everything. Protection has become a primary focus since data are a component of FinTech transactions. The task of consolidating research reports for consensus is very manual, as there is no standardized format. Although existing research has proposed certain methods, they have certain drawbacks in FinTech payment systems (including cryptocurrencies), credit markets (including peer-to-peer lending), and insurance systems. This paper implements blockchain-based financial technology for the banking sector to overcome these transition issues. In this study, we have proposed an adaptive neuro-fuzzy-based K-nearest neighbors' algorithm. The chaotic improved foraging optimization algorithm is used to optimize the proposed method. The rolling window autoregressive lag modeling approach analyzes FinTech growth. The proposed algorithm is compared with existing approaches to demonstrate its efficiency. The findings showed that it achieved 91% accuracy, 90% privacy, 96% robustness, and 25% cyber-risk performance. Compared with traditional approaches, the recommended strategy will be more convenient, safe, and effective in the transition period.

Keywords: Adaptive neural fuzzy based KNN algorithm; Blockchain technology; Economic growth; FinTech; Rolling window autoregressive lag modelling.