IDEAS home Printed from https://ideas.repec.org/a/spr/fininn/v9y2023i1d10.1186_s40854-022-00436-4.html
   My bibliography  Save this article

Governance of artificial intelligence applications in a business audit via a fusion fuzzy multiple rule-based decision-making model

Author

Listed:
  • Kuang-Hua Hu

    (Nanfang College)

  • Fu-Hsiang Chen

    (Chinese Culture University)

  • Ming-Fu Hsu

    (National United University)

  • Gwo-Hshiung Tzeng

    (National Taipei University)

Abstract

A broad range of companies around the world has welcomed artificial intelligence (AI) technology in daily practices because it provides decision-makers with comprehensive and intuitive messages about their operations and assists them in formulating appropriate strategies without any hysteresis. This research identifies the essential components of AI applications under an internal audit framework and provides an appropriate direction of strategies, which relate to setting up a priority on alternatives with multiple dimensions/criteria involvement that need to further consider the interconnected and intertwined relationships among them so as to reach a suitable judgment. To obtain this goal and inspired by a model ensemble, we introduce an innovative fuzzy multiple rule-based decision making framework that integrates soft computing, fuzzy set theory, and a multi-attribute decision making algorithm. The results display that the order of priority in improvement—(A) AI application strategy, (B) AI governance, (D) the human factor, and (C) data infrastructure and data quality—is based on the magnitude of their impact. This dynamically enhances the implementation of an AI-driven internal audit framework as well as responds to the strong rise of the big data environment.

Suggested Citation

  • Kuang-Hua Hu & Fu-Hsiang Chen & Ming-Fu Hsu & Gwo-Hshiung Tzeng, 2023. "Governance of artificial intelligence applications in a business audit via a fusion fuzzy multiple rule-based decision-making model," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-31, December.
    • Handle: RePEc:spr:fininn:v:9:y:2023:i:1:d:10.1186_s40854-022-00436-4
    DOI: 10.1186/s40854-022-00436-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1186/s40854-022-00436-4
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1186/s40854-022-00436-4?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. Jarrahi, Mohammad Hossein, 2018. "Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making," Business Horizons, Elsevier, vol. 61(4), pages 577-586.
    2. Chao, Xiangrui & Kou, Gang & Li, Tie & Peng, Yi, 2018. "Jie Ke versus AlphaGo: A ranking approach using decision making method for large-scale data with incomplete information," European Journal of Operational Research, Elsevier, vol. 265(1), pages 239-247.
    3. Serkan Atmaca & Hacı Ahmet Karadaş, 2020. "Decision making on financial investment in Turkey by using ARDL long-term coefficients and AHP," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-22, December.
    4. repec:eme:maj000:maj-08-2013-0910 is not listed on IDEAS
    5. Kou, Gang & Yüksel, Serhat & Dinçer, Hasan, 2022. "Inventive problem-solving map of innovative carbon emission strategies for solar energy-based transportation investment projects," Applied Energy, Elsevier, vol. 311(C).
    6. Sutton, Steve G. & Holt, Matthew & Arnold, Vicky, 2016. "“The reports of my death are greatly exaggerated”—Artificial intelligence research in accounting," International Journal of Accounting Information Systems, Elsevier, vol. 22(C), pages 60-73.
    7. Greco, Salvatore & Matarazzo, Benedetto & Slowinski, Roman, 1999. "Rough approximation of a preference relation by dominance relations," European Journal of Operational Research, Elsevier, vol. 117(1), pages 63-83, August.
    8. Sjödin, David & Parida, Vinit & Palmié, Maximilian & Wincent, Joakim, 2021. "How AI capabilities enable business model innovation: Scaling AI through co-evolutionary processes and feedback loops," Journal of Business Research, Elsevier, vol. 134(C), pages 574-587.
    9. Aapo Länsiluoto & Annukka Jokipii & Tomas Eklund, 2016. "Internal control effectiveness – a clustering approach," Managerial Auditing Journal, Emerald Group Publishing, vol. 31(1), pages 5-34, January.
    10. Pizzi, Simone & Venturelli, Andrea & Variale, Michele & Macario, Giuseppe Pio, 2021. "Assessing the impacts of digital transformation on internal auditing: A bibliometric analysis," Technology in Society, Elsevier, vol. 67(C).
    11. Aapo Länsiluoto & Annukka Jokipii & Tomas Eklund, 2016. "Internal control effectiveness – a clustering approach," Managerial Auditing Journal, Emerald Group Publishing Limited, vol. 31(1), pages 5-34, January.
    12. Zhang, Huanhuan & Kou, Gang & Peng, Yi, 2019. "Soft consensus cost models for group decision making and economic interpretations," European Journal of Operational Research, Elsevier, vol. 277(3), pages 964-980.
    13. Amelia A. Baldwin & Carol E. Brown & Brad S. Trinkle, 2006. "Opportunities for artificial intelligence development in the accounting domain: the case for auditing," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 14(3), pages 77-86, July.
    14. Opricovic, Serafim & Tzeng, Gwo-Hshiung, 2004. "Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS," European Journal of Operational Research, Elsevier, vol. 156(2), pages 445-455, July.
    15. Paulo Cesar Schotten & Danielle Costa Morais, 2019. "A group decision model for credit granting in the financial market," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-19, December.
    16. Yue Meng & Haoyue Wu & Wenjing Zhao & Wenkuan Chen & Hasan Dinçer & Serhat Yüksel, 2021. "A hybrid heterogeneous Pythagorean fuzzy group decision modelling for crowdfunding development process pathways of fintech-based clean energy investment projects," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-34, December.
    17. Carataș Maria Alina & Spătariu Elena Cerasela & Gheorghiu Gabriela, 2018. "Internal Audit Role in Artificial Intelligence," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 441-445, July.
    18. Kua-Hsin Peng & Gwo-Hshiung Tzeng, 2019. "Exploring heritage tourism performance improvement for making sustainable development strategies using the hybrid-modified MADM model," Current Issues in Tourism, Taylor & Francis Journals, vol. 22(8), pages 921-947, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ksenia V. Ekimova, 2023. "Development of the potential of the digital economy of Russian regions through artificial intelligence humanisation," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-9, December.

    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. Nikita Moiseev & Alexey Mikhaylov & Hasan Dinçer & Serhat Yüksel, 2023. "Market capitalization shock effects on open innovation models in e-commerce: golden cut q-rung orthopair fuzzy multicriteria decision-making analysis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-25, December.
    2. Li, Yanhong & Kou, Gang & Li, Guangxu & Peng, Yi, 2022. "Consensus reaching process in large-scale group decision making based on bounded confidence and social network," European Journal of Operational Research, Elsevier, vol. 303(2), pages 790-802.
    3. Chao, Xiangrui & Kou, Gang & Peng, Yi & Viedma, Enrique Herrera, 2021. "Large-scale group decision-making with non-cooperative behaviors and heterogeneous preferences: An application in financial inclusion," European Journal of Operational Research, Elsevier, vol. 288(1), pages 271-293.
    4. Moll, Jodie & Yigitbasioglu, Ogan, 2019. "The role of internet-related technologies in shaping the work of accountants: New directions for accounting research," The British Accounting Review, Elsevier, vol. 51(6).
    5. Uju Violet Alola & Ojonugwa Usman & Andrew Adewale Alola, 2023. "Is pass-through of the exchange rate to restaurant and hotel prices asymmetric in the US? Role of monetary policy uncertainty," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-19, December.
    6. Kou, Gang & Yüksel, Serhat & Dinçer, Hasan, 2022. "Inventive problem-solving map of innovative carbon emission strategies for solar energy-based transportation investment projects," Applied Energy, Elsevier, vol. 311(C).
    7. Mustafa Tevfik Kartal & Özer Depren, 2023. "Asymmetric relationship between global and national factors and domestic food prices: evidence from Turkey with novel nonlinear approaches," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-24, December.
    8. Thomas L. Saaty & Daji Ergu, 2015. "When is a Decision-Making Method Trustworthy? Criteria for Evaluating Multi-Criteria Decision-Making Methods," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 14(06), pages 1171-1187, November.
    9. Xiangrui Chao & Yucheng Dong & Gang Kou & Yi Peng, 2022. "How to determine the consensus threshold in group decision making: a method based on efficiency benchmark using benefit and cost insight," Annals of Operations Research, Springer, vol. 316(1), pages 143-177, September.
    10. Osman Taylan & Rami Alamoudi & Mohammad Kabli & Alawi AlJifri & Fares Ramzi & Enrique Herrera-Viedma, 2020. "Assessment of Energy Systems Using Extended Fuzzy AHP, Fuzzy VIKOR, and TOPSIS Approaches to Manage Non-Cooperative Opinions," Sustainability, MDPI, vol. 12(7), pages 1-27, March.
    11. Amjad Taha & Mucahit Aydin & Taiwo Temitope Lasisi & Festus Victor Bekun & Narayan Sethi, 2023. "Toward a sustainable growth path in Arab economies: an extension of classical growth model," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-24, December.
    12. Xiaohang Wu & Hasan Dinçer & Serhat Yüksel, 2022. "Analysis of crowdfunding platforms for microgrid project investors via a q-rung orthopair fuzzy hybrid decision-making approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-22, December.
    13. Kao-Yi Shen, 2017. "Compromise between Short- and Long-Term Financial Sustainability: A Hybrid Model for Supporting R&D Decisions," Sustainability, MDPI, vol. 9(3), pages 1-17, March.
    14. DunGang Zang & Krishna P. Paudel & Yan Liu & Dan Liu & Yating He, 2023. "Financial decision-making behaviors of Ethnic Tibetan Households based on mental accounting," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-26, December.
    15. Song, Yongming & Li, Yanhong & Zhu, Hongli & Li, Guangxu, 2023. "A decision support model for buying battery electric vehicles considering consumer learning and psychological behavior," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    16. Du, Junliang & Liu, Sifeng & Liu, Yong, 2022. "A limited cost consensus approach with fairness concern and its application," European Journal of Operational Research, Elsevier, vol. 298(1), pages 261-275.
    17. Kuang-Hua Hu & Ming-Fu Hsu & Fu-Hsiang Chen & Mu-Ziyun Liu, 2021. "Identifying the key factors of subsidiary supervision and management using an innovative hybrid architecture in a big data environment," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-27, December.
    18. Yongming Song & Jun Hu, 2017. "Vector similarity measures of hesitant fuzzy linguistic term sets and their applications," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-13, December.
    19. Yi Peng, 2015. "Regional earthquake vulnerability assessment using a combination of MCDM methods," Annals of Operations Research, Springer, vol. 234(1), pages 95-110, November.
    20. Zheng, Guozhong & Wang, Xiao, 2020. "The comprehensive evaluation of renewable energy system schemes in tourist resorts based on VIKOR method," Energy, Elsevier, vol. 193(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:spr:fininn:v:9:y:2023:i:1:d:10.1186_s40854-022-00436-4. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.