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Artificial intelligence algorithms and cloud computing technologies in blockchain-based fintech management

Abstract

Research background: Fintech development shapes corporate investment efficiency and economic growth with innovative tools, and can decrease financing constraints of enterprises, enabling direct and indirect financing and furthering inter-bank competition. Crowdfunding- and blockchain-based fintech operations harness deep and maching learning algorithms, augmented and virtual reality technologies, and big data analytics in mobile payment transactions.

Purpose of the article: We show that fintechs have reconfigured financial service delivery by harnessing AI-based data-driven algorithms and cloud and blockchain technologies. Fintech optimizes financial organization and services, economic structures and growth, data analysis, and digital banking performance.  Machine learning algorithms can streamline payment operation capabilities and process promptness, ensuring smooth operational flows, assessing risks, and detecting frauds and money laundering by historical data and customer behavior analysis across instant payment networks and infrastructures.

Methods: Quality tools: AXIS, Eppi-Reviewer, PICO Portal, and SRDR. Search period: July 2023. Search terms: “fintech” + “artificial intelligence algorithms”, “cloud computing technologies”, and “blockchain technologies”. Selected sources: 40 out of 195. Published research inspected: 2023. Data visualization tools: Dimensions and VOSviewer. Reporting quality assessment tool: PRISMA.

Findings & value added: Fintech development enables organizational innovation by mitigating information asymmetry and financing limitations while providing financial assistance and tax incentives in relation to products and services. The fintech growth has influenced the dynamic intermediary function of financial institutions in terms of sustainability and economic development. Fintech and natural resources negatively influence, while green innovations and financial development further, environmental sustainability.

Keywords

artificial intelligence algorithms, cloud computing, blockchain, fintech, green and sustainable finance, banking

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References

  1. Abakah, E. J. A., Tiwari, A. K., Ghosh, S., & Doğan, B. (2023). Dynamic effect of Bitcoin, fintech and artificial intelligence stocks on eco-friendly assets, Islamic stocks and conventional financial markets: Another look using quantile-based approaches. Technological Forecasting and Social Change, 192, 122566. DOI: https://doi.org/10.1016/j.techfore.2023.122566
    View in Google Scholar
  2. Ahelegbey, D., Giudici, P., & Pediroda, V. (2023). A network based fintech inclusion platform. Socio-Economic Planning Sciences, 87(B), 101555. DOI: https://doi.org/10.1016/j.seps.2023.101555
    View in Google Scholar
  3. Akmal, S., Talha, M., Faisal, S. M., Ahmad, M., & Khan, A. K. (2023). Perceptions about FinTech: New evidences from the Middle East. Cogent Economics & Finance, 11(1), 2217583. DOI: https://doi.org/10.1080/23322039.2023.2217583
    View in Google Scholar
  4. Alaassar, A., Mention, A. L., & Aas, T. H. (2023). Facilitating innovation in FinTech: A review and research agenda. Review of Managerial Science, 17, 33–66. DOI: https://doi.org/10.1007/s11846-022-00531-x
    View in Google Scholar
  5. Almansour, M. (2023). Artificial intelligence and resource optimization: A study of Fintech start-ups. Resources Policy, 80, 103250. DOI: https://doi.org/10.1016/j.resourpol.2022.103250
    View in Google Scholar
  6. Andronie, M., Lăzăroiu, G., Ștefănescu, R., Uță, C., & Dijmărescu, I. (2021). Sustainable, smart, and sensing technologies for cyber-physical manufacturing systems: A systematic literature review. Sustainability, 13, 5495. DOI: https://doi.org/10.3390/su13105495
    View in Google Scholar
  7. Arora, A., Gupta, S., Devi, C., & Walia, N. (2023). Customer experiences in the era of artificial intelligence (AI) in context to FinTech: A fuzzy AHP approach. Benchmarking: An International Journal. Advance online publication. DOI: https://doi.org/10.1108/BIJ-10-2021-0621
    View in Google Scholar
  8. Awais, M., Afzal, A., Firdousi, S., & Hasnaoui, A. (2023). Is fintech the new path to sustainable resource utilisation and economic development? Resources Policy, 81, 103309. DOI: https://doi.org/10.1016/j.resourpol.2023.103309
    View in Google Scholar
  9. Babaei, G., Giudici, P., & Raffinetti, E. (2023). Explainable FinTech lending. Journal of Economics and Business. Advance online publication. DOI: https://doi.org/10.1016/j.jeconbus.2023.106126
    View in Google Scholar
  10. Barbu, C. M., Florea, D. L., Dabija, D. C., & Barbu, M. C. R. (2021). Customer experience in fintech. Journal of Theoretical and Applied Electronic Commerce Research, 16(5), 1415‒1433. DOI: https://doi.org/10.3390/jtaer16050080
    View in Google Scholar
  11. Ben Romdhane, Y., Kammoun, S., & Loukil, S. (2023). The impact of Fintech on inflation and unemployment: The case of Asia. Arab Gulf Journal of Scientific Research. Advance online publication. DOI: https://doi.org/10.1108/AGJSR-08-2022-0146
    View in Google Scholar
  12. Bhutto, S. A., Jamal, Y., & Ullah, S. (2023). FinTech adoption, HR competency potential, service innovation and firm growth in banking sector. Heliyon, 9, e13967. DOI: https://doi.org/10.1016/j.heliyon.2023.e13967
    View in Google Scholar
  13. Campanella, F., Serino, L., Battisti, E., Giakoumelou, A., & Karasamani, I. (2023). FinTech in the financial system: Towards a capital-intensive and high competence human capital reality? Journal of Business Research, 155(A), 113376. DOI: https://doi.org/10.1016/j.jbusres.2022.113376
    View in Google Scholar
  14. Chaklader, B., Gupta, B. B., & Panigrahi, P. K. (2023). Analyzing the progress of FINTECH-companies and their integration with new technologies for innovation and entrepreneurship. Journal of Business Research, 161, 113847. DOI: https://doi.org/10.1016/j.jbusres.2023.113847
    View in Google Scholar
  15. Chen, S., & Guo, Q. (2023). Fintech, strategic incentives and investment to human capital, and MSEs innovation. North American Journal of Economics and Finance, 68, 101963. DOI: https://doi.org/10.1016/j.najef.2023.101963
    View in Google Scholar
  16. Chen, W., Wu, W., & Zhang, T. (2023). Fintech development, firm digitalization, and bank loan pricing. Journal of Behavioral and Experimental Finance, 39, 100838. DOI: https://doi.org/10.1016/j.jbef.2023.100838
    View in Google Scholar
  17. Cheng, A. (2023). Evaluating Fintech industry’s risks: A preliminary analysis based on CRISP-DM framework. Finance Research Letters, 55(B), 103966. DOI: https://doi.org/10.1016/j.frl.2023.103966
    View in Google Scholar
  18. Cheng, M., & Qu, Y. (2023). Does operational risk management benefit from FinTech? Emerging Markets Finance and Trade. Advance online publication. DOI: https://doi.org/10.1080/1540496X.2022.2164464
    View in Google Scholar
  19. Dong, X., & Yu, M. (2023). Does FinTech development facilitate firms’ innovation? Evidence from China. International Review of Financial Analysis, 89, 102805. DOI: https://doi.org/10.1016/j.irfa.2023.102805
    View in Google Scholar
  20. Durana, P., Musova, Z., & Cuțitoi, A.-C. (2022). Digital twin modeling and spatial awareness tools, acoustic environment recognition and visual tracking algorithms, and deep neural network and vision sensing technologies in blockchain-based virtual worlds. Analysis and Metaphysics, 21, 261–277. DOI: https://doi.org/10.22381/am21202215
    View in Google Scholar
  21. Gajanova, L., Nadanyiova, M., & Lăzăroiu, G. (2020). Specifics in brand value sources of customers in the banking industry from the psychographic point of view. Central European Business Review, 9(2), 1‒18. DOI: https://doi.org/10.18267/j.cebr.232
    View in Google Scholar
  22. Gonçalves, A. R., Breda Meira, A., Shuqair, S., & Costa Pinto, D. (2023). Artificial intelligence (AI) in FinTech decisions: The role of congruity and rejection sensitivity. International Journal of Bank Marketing. Advance online publication. DOI: https://doi.org/10.1108/IJBM-07-2022-0295
    View in Google Scholar
  23. Grupac, M., Husakova, K., & Balica, R.-Ș. (2022). Virtual navigation and augmented reality shopping tools, immersive and cognitive technologies, and image processing computational and object tracking algorithms in the metaverse commerce. Analysis and Metaphysics, 21, 210–226. DOI: https://doi.org/10.22381/am21202213
    View in Google Scholar
  24. Guang-Wen, Z., & Siddik, A. B. (2023). The effect of Fintech adoption on green finance and environmental performance of banking institutions during the COVID-19 pandemic: The role of green innovation. Environmental Science and Pollution Research, 30, 25959–25971. DOI: https://doi.org/10.1007/s11356-022-23956-z
    View in Google Scholar
  25. Guo, J., Fang, H., Liu, X., Wang, C., & Wang, Y. (2023). FinTech and financing constraints of enterprises: Evidence from China. Journal of International Financial Markets, Institutions and Money, 82, 101713. DOI: https://doi.org/10.1016/j.intfin.2022.101713
    View in Google Scholar
  26. Guo, P., & Zhang, C. (2023). The impact of bank FinTech on liquidity creation: Evidence from China. Research in International Business and Finance, 64, 101858. DOI: https://doi.org/10.1016/j.ribaf.2022.101858
    View in Google Scholar
  27. Ha, L. T. (2023). Dynamic connectedness between FinTech innovation and energy volatility during the war in time of pandemic. Environmental Science and Pollution Research, 30, 83530–83544. DOI: https://doi.org/10.1007/s11356-023-28089-5
    View in Google Scholar
  28. He, C., Geng, X., Tan, C., & Guo, R. (2023). Fintech and corporate debt default risk: Influencing mechanisms and heterogeneity. Journal of Business Research, 164, 113923. DOI: https://doi.org/10.1016/j.jbusres.2023.113923
    View in Google Scholar
  29. Ionescu, L. (2021). Big data analytics tools and machine learning algorithms in cloud-based accounting information systems. Analysis and Metaphysics, 20, 102–115. DOI: https://doi.org/10.22381/AM2020217
    View in Google Scholar
  30. Ionescu, L. (2022). Big data processing techniques and algorithmic decision-making tools in cloud-based accounting information systems. Review of Contemporary Philosophy, 21, 256–271. DOI: https://doi.org/10.22381/RCP21202216
    View in Google Scholar
  31. Jareño, F., & Yousaf, I. (2023). Artificial intelligence-based tokens: Fresh evidence of connectedness with artificial intelligence-based equities. International Review of Financial Analysis, 89, 102826. DOI: https://doi.org/10.1016/j.irfa.2023.102826
    View in Google Scholar
  32. Kazachenok, O. P., Stankevich, G. V., Chubaeva, N. N., & Tyurina, Y. G. (2023). Economic and legal approaches to the humanization of FinTech in the economy of artificial intelligence through the integration of blockchain into ESG Finance. Humanities and Social Sciences Communications, 10, 167. DOI: https://doi.org/10.1057/s41599-023-01652-8
    View in Google Scholar
  33. Konhäusner, P., Cabrera, M., & Dabija, D. C. (2021). Monetary incentivization of crowds by platforms. Inftars – Informacios Tarsadalom, 21(2), 97‒118. DOI: https://doi.org/10.22503/inftars.XXI.2021.2.7
    View in Google Scholar
  34. Kovacova, M., Horak, J., & Higgins, M. (2022). Behavioral analytics, immersive technologies, and machine vision algorithms in the Web3-powered metaverse world. Linguistic and Philosophical Investigations, 21, 57–72. DOI: https://doi.org/10.22381/lpi2120224
    View in Google Scholar
  35. Krizanova, A., Lăzăroiu, G., Gajanova, L., Kliestikova, J., Nadanyiova, M., & Moravcikova, D. (2019). The effectiveness of marketing communication and importance of its evaluation in an online environment. Sustainability, 11, 7016. DOI: https://doi.org/10.3390/su11247016
    View in Google Scholar
  36. Lăzăroiu, G., Ionescu, L., Andronie, M., & Dijmărescu, I. (2020a). Sustainability management and performance in the urban corporate economy: A systematic literature review. Sustainability, 12, 7705. DOI: https://doi.org/10.3390/su12187705
    View in Google Scholar
  37. Lăzăroiu, G., Ionescu, L., Uță, C., Hurloiu, I., Andronie, M., & Dijmărescu, I. (2020b). Environmentally responsible behavior and sustainability policy adoption in green public procurement. Sustainability, 12, 2110. DOI: https://doi.org/10.3390/su12052110
    View in Google Scholar
  38. Lăzăroiu, G., Pera, A., Ștefănescu-Mihăilă, R. O., Mircică, N., & Negurită, O. (2017). Can neuroscience assist us in constructing better patterns of economic decision-making? Frontiers in Behavioral Neuroscience, 11, 00188. DOI: https://doi.org/10.3389/fnbeh.2017.00188
    View in Google Scholar
  39. Lisha, L., Mousa, S., Arnone, G., Muda, I., Huerta-Soto, R., & Shiming, Z. (2023). Natural resources, green innovation, fintech, and sustainability: A fresh insight from BRICS. Resources Policy, 80, 103119. DOI: https://doi.org/10.1016/j.resourpol.2022.103119
    View in Google Scholar
  40. Mahmud, K., Joarder, M. M. A., & Sakib, K. (2023). Customer Fintech Readiness (CFR): Assessing customer readiness for fintech in Bangladesh. Journal of Open Innovation: Technology, Market, and Complexity, 9(2), 100032. DOI: https://doi.org/10.1016/j.joitmc.2023.100032
    View in Google Scholar
  41. Mikhaylov, A., Dinçer, H., & Yüksel, S. (2023). Analysis of financial development and open innovation oriented fintech potential for emerging economies using an integrated decision-making approach of MF-X-DMA and golden cut bipolar q-ROFSs. Financial Innovation, 9, 4. DOI: https://doi.org/10.1186/s40854-022-00399-6
    View in Google Scholar
  42. Mirza, N., Umar, M., Afzal, A., & Firdousi, S. F. (2023). The role of fintech in promoting green finance, and profitability: Evidence from the banking sector in the euro zone. Economic Analysis and Policy, 78, 33–40. DOI: https://doi.org/10.1016/j.eap.2023.02.001
    View in Google Scholar
  43. Nagy, M., & Lăzăroiu, G. (2022). Computer vision algorithms, remote sensing data fusion techniques, and mapping and navigation tools in the Industry 4.0-based Slovak automotive sector. Mathematics, 10, 3543. DOI: https://doi.org/10.3390/math10193543
    View in Google Scholar
  44. Nagy, M., Lăzăroiu, G., & Valaskova, K. (2023). Machine intelligence and autonomous robotic technologies in the corporate context of SMEs: Deep learning and virtual simulation algorithms, cyber-physical production networks, and Industry 4.0-based manufacturing systems. Applied Sciences, 13, 1681. DOI: https://doi.org/10.3390/app13031681
    View in Google Scholar
  45. Nica, E., Poliak, M., Popescu, G. H., & Pârvu, I.-A. (2022). Decision intelligence and modeling, multisensory customer experiences, and socially interconnected virtual services across the metaverse ecosystem. Linguistic and Philosophical Investigations, 21, 137–153. DOI: https://doi.org/10.22381/lpi2120229
    View in Google Scholar
  46. Pelău, C., Dabija, D. C., & Ene, I. (2021). What makes an AI device human-like? The role of interaction quality, empathy and perceived psychological anthropomorphic characteristics on the acceptance of artificial intelligence in the service industry. Computers in Human Behaviour, 122, 106855. DOI: https://doi.org/10.1016/j.chb.2021.106855
    View in Google Scholar
  47. Peters, M. A., Jackson, L., Papastephanou, M., Jandrić, P., Lăzăroiu, G., Evers, C. W., Cope,C., Kalantzis, M., Araya, D., Tesar, M., Mika, C., Chen, L., Wang, C., Sturm, S., Rider, R., & Fuller, S. (2023). AI and the future of humanity: ChatGPT-4, philosophy and education – Critical responses. Educational Philosophy and Theory. Advance online publication. DOI: https://doi.org/10.1080/00131857.2023.2213437
    View in Google Scholar
  48. Pop, R. A., Dabija, D. C., Pelau, C., & Dinu, V. (2022). Usage intentions, attitudes, and behaviours towards energy-efficient applications during the COVID-19 pandemic. Journal of Business Economics and Management, 23(3), 668‒689. DOI: https://doi.org/10.3846/jbem.2022.16959
    View in Google Scholar
  49. Rafiuddin, A., Gaytan, J. C. T., Mohnot, R., Sisodia, G. S., & Ahmed, G. (2023). Growth evaluation of fintech connectedness with innovative thematic indices – An evidence through wavelet analysis. Journal of Open Innovation: Technology, Market, and Complexity, 9(2), 100023. DOI: https://doi.org/10.1016/j.joitmc.2023.100023
    View in Google Scholar
  50. Rjoub, H., Adebayo, T. S., & Kirikkaleli, D. (2023). Blockchain technology-based FinTech banking sector involvement using adaptive neuro-fuzzy-based K-nearest neighbors algorithm. Financial Innovation, 9, 65. DOI: https://doi.org/10.1186/s40854-023-00469-3
    View in Google Scholar
  51. Rowland, Z., Lăzăroiu, G., & Podhorská, I. (2021). Use of neural networks to accommodate seasonal fluctuations when equalizing time series for the CZK/RMB exchange rate. Risks, 9, 1. DOI: https://doi.org/10.3390/risks9010001
    View in Google Scholar
  52. Sharma, S. K., Ilavarasan, P. V., & Karanasios, S. (2023b). Small businesses and FinTech: a systematic review and future directions. Electronic Commerce Research. Advance online publication. DOI: https://doi.org/10.1007/s10660-023-09705-5
    View in Google Scholar
  53. Sharma, S., Aggarwal, V., Dixit, N., & Yadav, M. P. (2023a). Time and frequency connectedness among emerging markets and QGREEN, FinTech and artificial intelligence-based index: Lessons from the outbreak of COVID-19. Vision. Advance online publication. DOI: https://doi.org/10.1177/09722629221141553
    View in Google Scholar
  54. Su, F., & Xu, C. (2023). Curbing credit corruption in China: The role of FinTech. Journal of Innovation & Knowledge, 8(1), 100292. DOI: https://doi.org/10.1016/j.jik.2022.100292
    View in Google Scholar
  55. Sun, R., & Zhang, B. (2023). Can fintech make corporate investments more efficient? A study on financing constraints and agency conflicts. Economic Research-Ekonomska Istraživanja, 36(3), 2185795. DOI: https://doi.org/10.1080/1331677X.2023.2185795
    View in Google Scholar
  56. Sun, Y., Li, S., & Wang, R. (2023). Fintech: From budding to explosion – An overview of the current state of research. Review of Managerial Science, 17, 715–755. DOI: https://doi.org/10.1007/s11846-021-00513-5
    View in Google Scholar
  57. Tan, Z., Wang, H., & Hong, Y. (2023). Does bank FinTech improve corporate innovation? Finance Research Letters, 55(A), 103830. DOI: https://doi.org/10.1016/j.frl.2023.103830
    View in Google Scholar
  58. Thomas, N. M., Mendiratta, P., & Kashiramka, S. (2023). FinTech credit: Uncovering knowledge base, intellectual structure and research front. International Journal of Bank Marketing. Advance online publication. DOI: https://doi.org/10.1108/IJBM-01-2023-0039
    View in Google Scholar
  59. Valaskova, K., Horak, J., & Bratu, S. (2022). Simulation modeling and image recognition tools, spatial computing technology, and behavioral predictive analytics in the metaverse economy. Review of Contemporary Philosophy, 21, 239–255.
    View in Google Scholar
  60. Yang, X., Yang, J., Hou, Y., Li, S., & Sun, S. (2023). Gamification of mobile wallet as an unconventional innovation for promoting Fintech: An fsQCA approach. Journal of Business Research, 155(A), 113406. DOI: https://doi.org/10.1016/j.jbusres.2022.113406
    View in Google Scholar
  61. Zauskova, A., Miklencicova, R., & Popescu, G. H. (2022). Visual imagery and geospatial mapping tools, virtual simulation algorithms, and deep learning-based sensing technologies in the metaverse interactive environment. Review of Contemporary Philosophy, 21, 122–137. DOI: https://doi.org/10.22381/RCP2120228
    View in Google Scholar
  62. Zhang, Y., Ye, S., Liu, J., & Du, L. (2023). Impact of the development of FinTech by commercial banks on bank credit risk. Finance Research Letters, 55(A), 103857. DOI: https://doi.org/10.1016/j.frl.2023.103857
    View in Google Scholar
  63. Zvarikova, K., Frajtova Michalikova, K., & Rowland, M. (2022a). Retail data measurement tools, cognitive artificial intelligence algorithms, and metaverse live shopping analytics in immersive hyper-connected virtual spaces. Linguistic and Philosophical Investigations, 21, 9–24. DOI: https://doi.org/10.22381/lpi2120221
    View in Google Scholar
  64. Zvarikova, K., Machova, V., & Nica, E. (2022b). Cognitive artificial intelligence algorithms, movement and behavior tracking tools, and customer identification technology in the metaverse commerce. Review of Contemporary Philosophy, 21, 171–187.
    View in Google Scholar

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