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Is ChatGPT competent? Heterogeneity in the cognitive schemas of financial auditors and robots

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  • Wei, Tian
  • Wu, Han
  • Chu, Gang

Abstract

The constraints of ChatGPT, as an intelligent conversational robot, in mimicking complex human activities have created doubts about its competence in the financial profession. Prior research has shown that the limitations of such robots result from differences between human and artificial cognitive structures, such as differences in structural approaches to interpreting information by organizing knowledge. However, explanations of how such differences are associated with human characteristics remain limited. This study focuses on one specific finance profession, that of financial auditors, to demonstrate how ChatGPT shows advances that mean it can be argued to imitate financial auditors with longer tenures. Our findings suggest possible applications and limitations of ChatGPT in light of these advances.

Suggested Citation

  • Wei, Tian & Wu, Han & Chu, Gang, 2023. "Is ChatGPT competent? Heterogeneity in the cognitive schemas of financial auditors and robots," International Review of Economics & Finance, Elsevier, vol. 88(C), pages 1389-1396.
    • Handle: RePEc:eee:reveco:v:88:y:2023:i:c:p:1389-1396
    DOI: 10.1016/j.iref.2023.07.108
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    References listed on IDEAS

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    1. Bischof, Jannis & Laux, Christian & Leuz, Christian, 2021. "Accounting for financial stability: Bank disclosure and loss recognition in the financial crisis," Journal of Financial Economics, Elsevier, vol. 141(3), pages 1188-1217.
    2. Dowling, Michael & Lucey, Brian, 2023. "ChatGPT for (Finance) research: The Bananarama Conjecture," Finance Research Letters, Elsevier, vol. 53(C).
    3. Berkeley J. Dietvorst & Joseph P. Simmons & Cade Massey, 2018. "Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them," Management Science, INFORMS, vol. 64(3), pages 1155-1170, March.
    4. Payzan-LeNestour, Elise & Woodford, Michael, 2022. "Outlier blindness: A neurobiological foundation for neglect of financial risk," Journal of Financial Economics, Elsevier, vol. 143(3), pages 1316-1343.
    5. Brav, Alon & Cain, Matthew & Zytnick, Jonathon, 2022. "Retail shareholder participation in the proxy process: Monitoring, engagement, and voting," Journal of Financial Economics, Elsevier, vol. 144(2), pages 492-522.
    6. García, Diego & Hu, Xiaowen & Rohrer, Maximilian, 2023. "The colour of finance words," Journal of Financial Economics, Elsevier, vol. 147(3), pages 525-549.
    7. Gao, Ming & Liu, Yu-Jane & Shi, Yushui, 2020. "Do people feel less at risk? Evidence from disaster experience," Journal of Financial Economics, Elsevier, vol. 138(3), pages 866-888.
    8. Kostopoulos, Dimitrios & Meyer, Steffen & Uhr, Charline, 2022. "Ambiguity about volatility and investor behavior," Journal of Financial Economics, Elsevier, vol. 145(1), pages 277-296.
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    Cited by:

    1. Agbon, Gildas, 2024. "Who speaks through the machine? Generative AI as discourse and implications for management," CRITICAL PERSPECTIVES ON ACCOUNTING, Elsevier, vol. 100(C).
    2. Song, Piaopeng & Lu, Hanglin & Zhang, Yongjie, 2024. "Unveiling tone manipulation in MD&A: Evidence from ChatGPT experiments," Finance Research Letters, Elsevier, vol. 67(PA).
    3. Wei, Tian & Wu, Han & Dowling, Michael & Mahdavi Ardekani, Aref, 2025. "Financial perceptions and AI infringement risks," International Review of Economics & Finance, Elsevier, vol. 101(C).
    4. Kun Yang & Ruxin Deng & Yunjie Wei & Shouyang Wang, 2025. "The power of ChatGPT in processing text: Evidence from analysis and prediction in the exchange rate markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-33, December.
    5. Ma, Chao-Qun & Liu, Xukang & Klein, Tony & Ren, Yi-Shuai, 2025. "Decoding the nexus: How fintech and AI stocks drive the future of sustainable finance," International Review of Economics & Finance, Elsevier, vol. 98(C).
    6. Dong, Mengming Michael & Stratopoulos, Theophanis C. & Wang, Victor Xiaoqi, 2024. "A scoping review of ChatGPT research in accounting and finance," International Journal of Accounting Information Systems, Elsevier, vol. 55(C).

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    Keywords

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    JEL classification:

    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • G00 - Financial Economics - - General - - - General
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets
    • M40 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - General

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