Enhancing corporate governance through AI: a systematic literature review

被引:1
作者
Ahdadou, Manal [1 ]
Aajly, Abdellah [1 ]
Tahrouch, Mohamed [1 ]
机构
[1] Abdelmalek Essaadi Univ, Natl Sch Business & Management ENCG, Tangier 90000, Morocco
关键词
Corporate governance; Artificial intelligence (AI); risk prediction; CSR; ARTIFICIAL-INTELLIGENCE; SOCIAL-RESPONSIBILITY; FINANCIAL PERFORMANCE; LISTED COMPANIES; PREDICTION; BANKRUPTCY; MODEL; MANAGEMENT; DISTRESS; QUALITY;
D O I
10.1080/09537325.2024.2326120
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
Corporate governance is the system by which companies are controlled and managed. In the era of rapid technological advancements, artificial intelligence (AI) has emerged as a powerful tool with significant potential for enhancing various aspects of corporate governance. This systematic literature review critically examines the role of AI in revolutionising corporate governance, particularly focusing on non-financial sectors. By scrutinising AI's integration in diverse governance areas - including the board of directors' performance, financial distress prediction, fraud detection, and CSR and sustainability efforts - the review reveals the multifaceted and adaptable nature of AI technologies in tackling specific corporate governance challenges. Despite its considerable promise, the review underscores significant gaps in AI's application, especially in its incorporation within boardroom dynamics. These findings not only shed light on the transformative influence of AI but also identify pressing research voids. The review aims to guide future inquiries, inform business practices, and influence policy frameworks, offering essential perspectives for researchers, industry professionals, and policymakers.
引用
收藏
页数:14
相关论文
共 93 条
  • [71] Usage of artificial neural networks for optimal bankruptcy forecasting. Case study: Eastern European small manufacturing enterprises
    Slavici, T.
    Maris, S.
    Pirtea, M.
    [J]. QUALITY & QUANTITY, 2016, 50 (01) : 385 - 398
  • [72] Smith M., 2017, Journal of Financial Crime, V24, P362, DOI 10.1108/JFC-11-2015-0061
  • [73] Application of Machine Learning Methods to Risk Assessment of Financial Statement Fraud: Evidence from China
    Song, Xin-Ping
    Hu, Zhi-Hua
    Du, Jian-Guo
    Sheng, Zhao-Han
    [J]. JOURNAL OF FORECASTING, 2014, 33 (08) : 611 - 626
  • [74] Research on financial early warning of mining listed companies based on BP neural network model
    Sun, Xiaojun
    Lei, Yalin
    [J]. RESOURCES POLICY, 2021, 73
  • [75] Corporate governance performance ratings with machine learning
    Svanberg, Jan
    Ardeshiri, Tohid
    Samsten, Isak
    Ohman, Peter
    Neidermeyer, Presha E.
    Rana, Tarek
    Semenova, Natalia
    Danielson, Mats
    [J]. INTELLIGENT SYSTEMS IN ACCOUNTING FINANCE & MANAGEMENT, 2022, 29 (01) : 50 - 68
  • [76] Corporate fraud culture: Re-examining the corporate governance and performance relation
    Tan, David T.
    Chapple, Larelle
    Walsh, Kathleen D.
    [J]. ACCOUNTING AND FINANCE, 2017, 57 (02) : 597 - 620
  • [77] Corporate governance in SMEs: a systematic literature review and future research
    Teixeira, Jaime Fernandes
    Carvalho, Amelia Oliveira
    [J]. CORPORATE GOVERNANCE-THE INTERNATIONAL JOURNAL OF BUSINESS IN SOCIETY, 2024, 24 (02): : 303 - 326
  • [78] Exploring corporate social responsibility and financial performance through stakeholder theory in the tourism industries
    Theodoulidis, Babis
    Diaz, David
    Crotto, Federica
    Rancati, Elisa
    [J]. TOURISM MANAGEMENT, 2017, 62 : 173 - 188
  • [79] Women on corporate boards and corporate financial and non-financial performance: A systematic literature review and future research agenda
    Thi Hong Hanh Nguyen
    G Ntim, Collins
    Malagila, John K.
    [J]. INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS, 2020, 71
  • [80] Corporate governance
    Tirole, J
    [J]. ECONOMETRICA, 2001, 69 (01) : 1 - 35