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

Author

Listed:
  • Husam Rjoub

    (Palestine Polytechnic University-PPU
    Bahçeşehir Cyprus University)

  • Tomiwa Sunday Adebayo

    (Cyprus International University)

  • Dervis Kirikkaleli

    (European University of Lefke)

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.

Suggested Citation

  • Husam Rjoub & Tomiwa Sunday Adebayo & Dervis Kirikkaleli, 2023. "Blockchain technology-based FinTech banking sector involvement using adaptive neuro-fuzzy-based K-nearest neighbors algorithm," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.
    • Handle: RePEc:spr:fininn:v:9:y:2023:i:1:d:10.1186_s40854-023-00469-3
    DOI: 10.1186/s40854-023-00469-3
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    References listed on IDEAS

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    1. Binh Nguyen Thanh & Ha Xuan Son & Diem Thi Hong Vo, 2024. "Blockchain: The Economic and Financial Institution for Autonomous AI?," JRFM, MDPI, vol. 17(2), pages 1-20, January.
    2. Serkan Eti & Hasan Dinçer & Hasan Meral & Serhat Yüksel & Yaşar Gökalp, 2024. "Insurtech in Europe: identifying the top investment priorities for driving innovation," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-24, December.

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