4.5 Article

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

Journal

FINANCIAL INNOVATION
Volume 9, Issue 1, Pages -

Publisher

SPRINGER
DOI: 10.1186/s40854-023-00469-3

Keywords

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

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The study investigates the impact of FinTech on the performance of Chinese banking. FinTech has the potential to reshape financial expectations and global realities, bringing automation and personalization to the forefront of financial services. Technologies like the Internet of Things, blockchain, and artificial intelligence will shape the future of FinTech and have significant implications for global business growth. The study proposes a blockchain-based FinTech approach for the banking sector, aiming to address transition issues and improve efficiency, convenience, safety, and effectiveness.
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.

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