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Using Hybrid Artificial Intelligence and Machine Learning Technologies for Sustainability in Going-Concern Prediction

Author

Listed:
  • Der-Jang Chi

    (Department of Accounting, Chinese Culture University, No. 55, Hwa-Kang Road, Yang-Ming-Shan, Taipei City 11114, Taiwan)

  • Zong-De Shen

    (Department of Accounting, Chinese Culture University, No. 55, Hwa-Kang Road, Yang-Ming-Shan, Taipei City 11114, Taiwan)

Abstract

The going-concern opinions of certified public accountants (CPAs) and auditors are very critical, and due to misjudgments, the failure to discover the possibility of bankruptcy can cause great losses to financial statement users and corporate stakeholders. Traditional statistical models have disadvantages in giving going-concern opinions and are likely to cause misjudgments, which can have significant adverse effects on the sustainable survival and development of enterprises and investors’ judgments. In order to embrace the era of big data, artificial intelligence (AI) and machine learning technologies have been used in recent studies to judge going concern doubts and reduce judgment errors. The Big Four accounting firms (Deloitte, KPMG, PwC, and EY) are paying greater attention to auditing via big data and artificial intelligence (AI). Thus, this study integrates AI and machine learning technologies: in the first stage, important variables are selected by two decision tree algorithms, classification and regression trees (CART), and a chi-squared automatic interaction detector (CHAID); in the second stage, classification models are respectively constructed by extreme gradient boosting (XGB), artificial neural network (ANN), support vector machine (SVM), and C5.0 for comparison, and then, financial and non-financial variables are adopted to construct effective going-concern opinion decision models (which are more accurate in prediction). The subjects of this study are listed companies and OTC (over-the-counter) companies in Taiwan with and without going-concern doubts from 2000 to 2019. According to the empirical results, among the eight models constructed in this study, the prediction accuracy of the CHAID–C5.0 model is the highest (95.65%), followed by the CART–C5.0 model (92.77%).

Suggested Citation

  • Der-Jang Chi & Zong-De Shen, 2022. "Using Hybrid Artificial Intelligence and Machine Learning Technologies for Sustainability in Going-Concern Prediction," Sustainability, MDPI, vol. 14(3), pages 1-18, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:3:p:1810-:d:742547
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    References listed on IDEAS

    as
    1. Suduan Chen, 2019. "An effective going concern prediction model for the sustainability of enterprises and capital market development," Applied Economics, Taylor & Francis Journals, vol. 51(31), pages 3376-3388, July.
    2. Chyan-Long Jan, 2021. "Detection of Financial Statement Fraud Using Deep Learning for Sustainable Development of Capital Markets under Information Asymmetry," Sustainability, MDPI, vol. 13(17), pages 1-20, September.
    3. Der-Jang Chi & Chien-Chou Chu, 2021. "Artificial Intelligence in Corporate Sustainability: Using LSTM and GRU for Going Concern Prediction," Sustainability, MDPI, vol. 13(21), pages 1-18, October.
    4. Marisa Agostini, 2018. "Corporate Financial Distress," Springer Books, Springer, number 978-3-319-78500-4, December.
    5. Chyan-long Jan, 2018. "An Effective Financial Statements Fraud Detection Model for the Sustainable Development of Financial Markets: Evidence from Taiwan," Sustainability, MDPI, vol. 10(2), pages 1-14, February.
    6. Elizabeth Gutierrez & Jake Krupa & Miguel Minutti-Meza & Maria Vulcheva, 2020. "Do going concern opinions provide incremental information to predict corporate defaults?," Review of Accounting Studies, Springer, vol. 25(4), pages 1344-1381, December.
    7. Elizabeth Gutierrez & Miguel Minutti-Meza & Kay W. Tatum & Maria Vulcheva, 2018. "Consequences of adopting an expanded auditor’s report in the United Kingdom," Review of Accounting Studies, Springer, vol. 23(4), pages 1543-1587, December.
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