Rjoub Husam, Adebayo Tomiwa Sunday, Kirikkaleli Dervis
Department of Accounting and Finance, Palestine Polytechnic University-PPU, Hebron, Palestine.
Department of Banking and Finance, Faculty of Economics, Administrative and Social Sciences, Bahçeşehir Cyprus University, 99010 Alayköy, Nicosia, Turkey.
Financ Innov. 2023;9(1):65. doi: 10.1186/s40854-023-00469-3. Epub 2023 Mar 10.
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.
本研究旨在调查影响中国银行业绩的金融科技（FinTech）因素。金融科技的多维度范围可能会改变金融预期和全球现实，而传统金融部门缺乏这一点。随着数字时代在消费者和个性化方面经历了一段转型期，利用技术实现金融服务自动化对经济组织和行业变得越来越重要。金融科技的未来将由物联网、区块链和人工智能等技术塑造。这些平台参与金融服务是全球商业增长的一个主要关注点。由于这些优势，金融科技越来越受客户欢迎。金融科技推动了金融服务行业的根本性变革，将客户置于一切的中心。由于数据是金融科技交易的一个组成部分，保护已成为主要关注点。由于没有标准化格式，整合研究报告以达成共识的任务非常繁琐。尽管现有研究提出了某些方法，但它们在金融科技支付系统（包括加密货币）、信贷市场（包括点对点借贷）和保险系统方面存在一定缺陷。本文为银行业实施基于区块链的金融科技以克服这些转型问题。在本研究中，我们提出了一种基于自适应神经模糊的K近邻算法。采用混沌改进觅食优化算法对所提方法进行优化。滚动窗口自回归滞后建模方法分析金融科技的增长。将所提算法与现有方法进行比较以证明其效率。研究结果表明，该算法实现了91%的准确率、90%的隐私性、96%的稳健性和25%的网络风险性能。与传统方法相比，推荐策略在转型期将更便捷、安全和有效。
