IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i18p13725-d1240235.html
   My bibliography  Save this article

The Artificial Intelligence Revolution in Digital Finance in Saudi Arabia: A Comprehensive Review and Proposed Framework

Author

Listed:
  • Heyam H. Al-Baity

    (IT Department, College of Computer and Information Sciences, King Saud University, P.O. Box 145111, Riyadh 4545, Saudi Arabia)

Abstract

Artificial Intelligence (AI) has proliferated in the last few years due to the vast data we pro-duce daily and available computing power. AI can be applied in many different sectors, such as transportation, education, healthcare, banking, and finance, among many others. The financial industry is rapidly embracing AI due to its potential for high-cost savings in financial services. AI could transform the financial sector by creating opportunities for tailored, faster, and more cost-effective services. Saudi Arabia is emerging as a fast-growing market in this industry with a strong commitment to technology-driven institutions. While AI is gaining prominence and receiving government support, it has not yet become a critical component for enhancing the efficiency of financial transactions. Limited published research on AI adoption in the Saudi Arabian financial industry calls for a comprehensive literature review to examine the current state of AI implementation in this sector. Therefore, this study explores the benefits, limitations, and challenges of leveraging AI in finance, highlighting the importance of ethical and regulatory considerations for successful AI adoption in the sector. This study’s findings reveal that research has been conducted on how AI improves processes in the financial sector by integrating critical components and efficient algorithms tailored to the industry’s needs. Based on these findings, this study proposes a sequential framework at the macro and micro levels of management to guide AI’s development and integration into the financial sector. Additionally, the framework draws insights from the existing literature to provide a detailed understanding of opportunities, challenges, and areas for improvement to maximize AI’s potential in the Saudi Arabian financial sector.

Suggested Citation

  • Heyam H. Al-Baity, 2023. "The Artificial Intelligence Revolution in Digital Finance in Saudi Arabia: A Comprehensive Review and Proposed Framework," Sustainability, MDPI, vol. 15(18), pages 1-16, September.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13725-:d:1240235
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/18/13725/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/18/13725/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Periklis Gogas & Theophilos Papadimitriou, 2021. "Machine Learning in Economics and Finance," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 1-4, January.
    2. Nayef Shaie Alotaibi & Awad Hajran Alshehri, 2023. "Prospers and Obstacles in Using Artificial Intelligence in Saudi Arabia Higher Education Institutions—The Potential of AI-Based Learning Outcomes," Sustainability, MDPI, vol. 15(13), pages 1-18, July.
    3. Jian Huang & Junyi Chai & Stella Cho, 2020. "Deep learning in finance and banking: A literature review and classification," Frontiers of Business Research in China, Springer, vol. 14(1), pages 1-24, December.
    4. Ahmet Murat Ozbayoglu & Mehmet Ugur Gudelek & Omer Berat Sezer, 2020. "Deep Learning for Financial Applications : A Survey," Papers 2002.05786, arXiv.org.
    5. Nir Kshetri, 2021. "The Role of Artificial Intelligence in Promoting Financial Inclusion in Developing Countries," Journal of Global Information Technology Management, Taylor & Francis Journals, vol. 24(1), pages 1-6, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Amin Aminimehr & Ali Raoofi & Akbar Aminimehr & Amirhossein Aminimehr, 2022. "A Comprehensive Study of Market Prediction from Efficient Market Hypothesis up to Late Intelligent Market Prediction Approaches," Computational Economics, Springer;Society for Computational Economics, vol. 60(2), pages 781-815, August.
    2. Pedro M. Mirete-Ferrer & Alberto Garcia-Garcia & Juan Samuel Baixauli-Soler & Maria A. Prats, 2022. "A Review on Machine Learning for Asset Management," Risks, MDPI, vol. 10(4), pages 1-46, April.
    3. Kanzari, Dalel & Nakhli, Mohamed Sahbi & Gaies, Brahim & Sahut, Jean-Michel, 2023. "Predicting macro-financial instability – How relevant is sentiment? Evidence from long short-term memory networks," Research in International Business and Finance, Elsevier, vol. 65(C).
    4. Hanyao Gao & Gang Kou & Haiming Liang & Hengjie Zhang & Xiangrui Chao & Cong-Cong Li & Yucheng Dong, 2024. "Machine learning in business and finance: a literature review and research opportunities," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-35, December.
    5. Ajitha Kumari Vijayappan Nair Biju & Ann Susan Thomas & J Thasneem, 2024. "Examining the research taxonomy of artificial intelligence, deep learning & machine learning in the financial sphere—a bibliometric analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(1), pages 849-878, February.
    6. Jianian Wang & Sheng Zhang & Yanghua Xiao & Rui Song, 2021. "A Review on Graph Neural Network Methods in Financial Applications," Papers 2111.15367, arXiv.org, revised Apr 2022.
    7. Adebayo Oshingbesan & Eniola Ajiboye & Peruth Kamashazi & Timothy Mbaka, 2022. "Model-Free Reinforcement Learning for Asset Allocation," Papers 2209.10458, arXiv.org.
    8. Tomoshiro Ochiai & Jose C. Nacher, 2020. "Unveiling the directional network behind the financial statements data using volatility constraint correlation," Papers 2008.07836, arXiv.org, revised Jun 2023.
    9. Afaq Khattak & Hamad Almujibah & Ahmed Elamary & Caroline Mongina Matara, 2022. "Interpretable Dynamic Ensemble Selection Approach for the Prediction of Road Traffic Injury Severity: A Case Study of Pakistan’s National Highway N-5," Sustainability, MDPI, vol. 14(19), pages 1-18, September.
    10. Adil EL Fakir & Richard Fairchild & Youssef Lamrani Alaoui & Dora Chan & Mohamed Tkiouat & Zaid Amer, 2024. "Kinship, gender and social links impact on micro group lending defaults," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 2527-2542, April.
    11. Kéa Baret & Amélie Barbier-Gauchard & Théophilos Papadimitriou, 2021. "Forecasting the Stability and Growth Pact compliance using Machine Learning," Working Papers of BETA 2021-01, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    12. Mahsa Tavakoli & Rohitash Chandra & Fengrui Tian & Cristi'an Bravo, 2023. "Multi-Modal Deep Learning for Credit Rating Prediction Using Text and Numerical Data Streams," Papers 2304.10740, arXiv.org, revised Nov 2024.
    13. Yuze Lu & Hailong Zhang & Qiwen Guo, 2023. "Stock and market index prediction using Informer network," Papers 2305.14382, arXiv.org.
    14. Bartosz Bieganowski & Robert Ślepaczuk, 2024. "Supervised Autoencoder MLP for Financial Time Series Forecasting," Working Papers 2024-03, Faculty of Economic Sciences, University of Warsaw.
    15. Emmanouil Sofianos & Emmanouil Zaganidis & Theophilos Papadimitriou & Periklis Gogas, 2024. "Forecasting East and West Coast Gasoline Prices with Tree-Based Machine Learning Algorithms," Energies, MDPI, vol. 17(6), pages 1-14, March.
    16. Wang, Yijun & Andreeva, Galina & Martin-Barragan, Belen, 2023. "Machine learning approaches to forecasting cryptocurrency volatility: Considering internal and external determinants," International Review of Financial Analysis, Elsevier, vol. 90(C).
    17. Longbing Cao, 2021. "AI in Finance: Challenges, Techniques and Opportunities," Papers 2107.09051, arXiv.org.
    18. Joel Ong & Dorien Herremans, 2024. "DeepUnifiedMom: Unified Time-series Momentum Portfolio Construction via Multi-Task Learning with Multi-Gate Mixture of Experts," Papers 2406.08742, arXiv.org.
    19. Ni Zhan, 2021. "Where does the Stimulus go? Deep Generative Model for Commercial Banking Deposits," Papers 2101.09230, arXiv.org.
    20. Nwafor, Chioma Ngozi & Nwafor, Obumneme Zimuzor, 2023. "Determinants of non-performing loans: An explainable ensemble and deep neural network approach," Finance Research Letters, Elsevier, vol. 56(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13725-:d:1240235. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.