IDEAS home Printed from https://ideas.repec.org/a/rnd/arimbr/v15y2023i4p420-432.html
   My bibliography  Save this article

Has the World of Finance Changed? A Review of the Influence of Artificial Intelligence on Financial Management Studies

Author

Listed:
  • Shahsuzan Zakaria
  • Suhaily Maizan Abdul Manaf
  • Mohd Talmizie Amron
  • Mohd Taufik Mohd Suffian

Abstract

Artificial intelligence (AI) has had a tremendous impact on financial management research, revolutionizing how financial data is analyzed and decisions are made. It has the ability to improve accuracy and efficiency while also lowering costs and providing fresh insights into financial markets and investments. This review will look at some of the important areas of financial management research that AI has influenced. One of the most notable effects of AI on financial management research has been in risk management. AI algorithms, with their ability to analyze vast amounts of data, can detect patterns and anomalies that humans may not notice right away and have had a significant impact on financial forecasting and prediction, generating predictions about future market movements and investment opportunities by analyzing historical data, market trends, and other relevant factors. In conclusion, artificial intelligence (AI) has had a tremendous impact on financial management research, revolutionizing how financial data is analyzed and decisions are made, with significant potential to disrupt traditional financial management techniques. AI has the ability to increase accuracy and efficiency, cut costs, and provide fresh insights into financial markets and investments. As AI technology advances, its impact on financial management studies is likely to rise.

Suggested Citation

  • Shahsuzan Zakaria & Suhaily Maizan Abdul Manaf & Mohd Talmizie Amron & Mohd Taufik Mohd Suffian, 2023. "Has the World of Finance Changed? A Review of the Influence of Artificial Intelligence on Financial Management Studies," Information Management and Business Review, AMH International, vol. 15(4), pages 420-432.
    • Handle: RePEc:rnd:arimbr:v:15:y:2023:i:4:p:420-432
    DOI: 10.22610/imbr.v15i4(SI)I.3617
    as

    Download full text from publisher

    File URL: https://ojs.amhinternational.com/index.php/imbr/article/view/3617/2344
    Download Restriction: no
    File URL: https://ojs.amhinternational.com/index.php/imbr/article/view/3617
    Download Restriction: no
    File URL: https://libkey.io/10.22610/imbr.v15i4(SI)I.3617?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Jingguang Han & Yuyun Huang & Sha Liu & Kieran Towey, 2020. "Artificial intelligence for anti-money laundering: a review and extension," Digital Finance, Springer, vol. 2(3), pages 211-239, December.
    2. Shanmuganathan, Manchuna, 2020. "Behavioural finance in an era of artificial intelligence: Longitudinal case study of robo-advisors in investment decisions," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    3. De Bruyn, Arnaud & Viswanathan, Vijay & Beh, Yean Shan & Brock, Jürgen Kai-Uwe & von Wangenheim, Florian, 2020. "Artificial Intelligence and Marketing: Pitfalls and Opportunities," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 91-105.
    4. Soviany, Cristina, 2018. "The benefits of using artificial intelligence in payment fraud detection: A case study," Journal of Payments Strategy & Systems, Henry Stewart Publications, vol. 12(2), pages 102-110, July.
    5. Lee, In & Shin, Yong Jae, 2020. "Machine learning for enterprises: Applications, algorithm selection, and challenges," Business Horizons, Elsevier, vol. 63(2), pages 157-170.
    6. Dev Shah & Haruna Isah & Farhana Zulkernine, 2019. "Stock Market Analysis: A Review and Taxonomy of Prediction Techniques," IJFS, MDPI, vol. 7(2), pages 1-22, May.
    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. Robertson, Jeandri & Ferreira, Caitlin & Botha, Elsamari & Oosthuizen, Kim, 2024. "Game changers: A generative AI prompt protocol to enhance human-AI knowledge co-construction," Business Horizons, Elsevier, vol. 67(5), pages 499-510.
    2. Kamaladdin Fataliyev & Aneesh Chivukula & Mukesh Prasad & Wei Liu, 2021. "Stock Market Analysis with Text Data: A Review," Papers 2106.12985, arXiv.org, revised Jul 2021.
    3. Bavaresco, Rodrigo Simon & Nesi, Luan Carlos & Victória Barbosa, Jorge Luis & Antunes, Rodolfo Stoffel & da Rosa Righi, Rodrigo & da Costa, Cristiano André & Vanzin, Mariangela & Dornelles, Daniel & J, 2023. "Machine learning-based automation of accounting services: An exploratory case study," International Journal of Accounting Information Systems, Elsevier, vol. 49(C).
    4. Ionut Anica-Popa & Liana Anica-Popa & Cristina Radulescu & Marinela Vrincianu, 2021. "The Integration of Artificial Intelligence in Retail: Benefits, Challenges and a Dedicated Conceptual Framework," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 23(56), pages 120-120, February.
    5. Neubert, Mitchell J. & Montañez, George D., 2020. "Virtue as a framework for the design and use of artificial intelligence," Business Horizons, Elsevier, vol. 63(2), pages 195-204.
    6. Daiki Matsunaga & Toyotaro Suzumura & Toshihiro Takahashi, 2019. "Exploring Graph Neural Networks for Stock Market Predictions with Rolling Window Analysis," Papers 1909.10660, arXiv.org, revised Nov 2019.
    7. Alina Köchling & Marius Claus Wehner, 2020. "Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development," Business Research, Springer;German Academic Association for Business Research, vol. 13(3), pages 795-848, November.
    8. Kamoonpuri, Sana Zehra & Sengar, Anita, 2023. "Hi, May AI help you? An analysis of the barriers impeding the implementation and use of artificial intelligence-enabled virtual assistants in retail," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    9. Ming‐Lang Tseng & Hien Minh Ha & Thi Phuong Thuy Tran & Tat‐Dat Bui & Chih‐Cheng Chen & Chun‐Wei Lin, 2022. "Building a data‐driven circular supply chain hierarchical structure: Resource recovery implementation drives circular business strategy," Business Strategy and the Environment, Wiley Blackwell, vol. 31(5), pages 2082-2106, July.
    10. Hakan Pabuccu & Adrian Barbu, 2023. "Feature Selection with Annealing for Forecasting Financial Time Series," Papers 2303.02223, arXiv.org, revised Feb 2024.
    11. Arjun Prakash & Nick James & Max Menzies & Gilad Francis, 2020. "Structural clustering of volatility regimes for dynamic trading strategies," Papers 2004.09963, arXiv.org, revised Nov 2021.
    12. Mustak, Mekhail & Hallikainen, Heli & Laukkanen, Tommi & Plé, Loïc & Hollebeek, Linda D. & Aleem, Majid, 2024. "Using machine learning to develop customer insights from user-generated content," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).
    13. Canhoto, Ana Isabel & Clear, Fintan, 2020. "Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential," Business Horizons, Elsevier, vol. 63(2), pages 183-193.
    14. Hoyer, Wayne D. & Kroschke, Mirja & Schmitt, Bernd & Kraume, Karsten & Shankar, Venkatesh, 2020. "Transforming the Customer Experience Through New Technologies," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 57-71.
    15. Mustak, Mekhail & Salminen, Joni & Plé, Loïc & Wirtz, Jochen, 2021. "Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda," Journal of Business Research, Elsevier, vol. 124(C), pages 389-404.
    16. Rahman, Muhammad Sabbir & Bag, Surajit & Hossain, Md Afnan & Abdel Fattah, Fadi Abdel Muniem & Gani, Mohammad Osman & Rana, Nripendra P., 2023. "The new wave of AI-powered luxury brands online shopping experience: The role of digital multisensory cues and customers’ engagement," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    17. L.J. Basson & Sune Ferreira-Schenk & Zandri Dickason-Koekemoer, 2022. "Fractal Dimension Option Hedging Strategy Implementation During Turbulent Market Conditions in Developing and Developed Countries," International Journal of Economics and Financial Issues, Econjournals, vol. 12(2), pages 84-95, March.
    18. Anderson, Brian S., 2022. "What executives get wrong about statistics: Moving from statistical significance to effect sizes and practical impact," Business Horizons, Elsevier, vol. 65(3), pages 379-388.
    19. Veronika Staňková, 2021. "Can Machine Learning Be Useful in Corporate Finance and Business Valuation? Overview of Current Research [Může být strojové učení užitečné ve financích podniku a jeho ocenění? Přehled současného vý," Oceňování, Prague University of Economics and Business, vol. 14(4), pages 53-66.
    20. Mónika-Anetta Alt & Vizeli Ibolya, 2021. "Identifying Relevant Segments of Potential Banking Chatbot Users Based on Technology Adoption Behavior," Tržište/Market, Faculty of Economics and Business, University of Zagreb, vol. 33(2), pages 165-183.

    More about this item

    Statistics

    Access and download statistics

    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:rnd:arimbr:v:15:y:2023:i:4:p:420-432. 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: Muhammad Tayyab (email available below). General contact details of provider: https://ojs.amhinternational.com/index.php/imbr .

    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.