AI/ML Literature in Accounting

The intersection of Accounting and Artificial Intelligence (AI)/Machine Learning (ML) is a dynamic and evolving field. This page is to assist peer researchers and Ph.D. students in gathering relevant literature. 

This page may not contain a comprehensive list of literature but I will update this page constantly to reflect the latest development in major accounting journals.

Methodology overview

• Krupa, J., & Minutti-Meza, M. (2021). Regression and Machine Learning Methods to Predict Discrete Outcomes in Accounting Research. Journal of Financial Reporting.

• Bertomeu, J. (2020). Machine learning improves accounting: discussion, implementation and research opportunities. Review of Accounting Studies, 25(3), 1135-1155.

• Perols, J. L., Bowen, R. M., Zimmermann, C., & Samba, B. (2017). Finding needles in a haystack: Using data analytics to improve fraud prediction. The Accounting Review, 92(2), 221-245.

ML to develop models for prediction

• Cecchini, M., Aytug, H., Koehler, G. J., & Pathak, P. (2010). Detecting management fraud in public companies. Management Science, 56(7), 1146-1160.

• Perols, J. (2011). Financial statement fraud detection: An analysis of statistical and machine learning algorithms. Auditing: A Journal of Practice & Theory, 30(2), 19-50.

• Jones, S. (2017). Corporate bankruptcy prediction: a high dimensional analysis. Review of Accounting Studies, 22, 1366-1422.

• Ding, K., Lev, B., Peng, X., Sun, T., and Vasarhelyi, M. A. (2020). Machine learning improves accounting estimates: Evidence from insurance payments. Review of Accounting Studies, 25(3), 1098-1134.

• Bao, Y., Ke, B., Li, B., Yu, Y. J., and Zhang, J. (2020). Detecting accounting fraud in publicly traded US firms using a machine learning approach. Journal of Accounting Research, 58(1), 199-235.

• Bertomeu, J., Cheynel, E., Floyd, E., and Pan, W. (2021). Using machine learning to detect misstatements. Review of Accounting Studies, 26(2), 468-519.

• Chen, X., Cho, Y. H., Dou, Y., and Lev, B. (2022). Predicting Future Earnings Changes Using Machine Learning and Detailed Financial Data. Journal of Accounting Research, 60(2), 467-515.

• Guenther, D. A., Peterson, K., Searcy, J., and Williams, B. M. (2023). How Useful Are Tax Disclosures in Predicting Effective Tax Rates? A Machine Learning Approach. The Accounting Review, 1-26.

• Geertsema, P., & Lu, H. (2023). Relative valuation with machine learning. Journal of Accounting Research, 61(1), 329-376.

• Jones, S., Moser, W. J., & Wieland, M. M. (2023). Machine learning and the prediction of changes in profitability. Contemporary Accounting Research, 40(4), 2643-2672.

• Xu, X., Xiong, F., & An, Z. (2023). Using machine learning to predict corporate fraud: evidence based on the gone framework. Journal of Business Ethics, 186(1), 137-158. 

• Kim, A. G., & Nikolaev, V. V. (2024). Context‐Based Interpretation of Financial Information. Journal of Accounting Research.

ML to improve measurement

• Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223-2273.

• Starica, C., & Marton, J. P. (2024). Identifying the Relationship between Earnings and Prices. The Accounting Review, 1-38.

• Bertomeu, J., Cheynel, E., Liao, Y., & Milone, M. (2024). Using machine learning to measure conservatism. Management Science.

AI’s Impact

-          Empirical archival

• Fedyk, A., Hodson, J., Khimich, N., & Fedyk, T. (2022). Is artificial intelligence improving the audit process?. Review of Accounting Studies, 27(3), 938-985.

• Chen, W., and Srinivasan, S. (2023). Going digital: Implications for firm value and performance. Review of Accounting Studies, 1-47.

• Law, K. K., & Shen, M. (2024). How does artificial intelligence shape audit firms?. Management Science.

• Babina, T., Fedyk, A., He, A., & Hodson, J. (2024). Artificial intelligence, firm growth, and product innovation. Journal of Financial Economics, 151, 103745.

• Ashraf, M. (2024). Does automation improve financial reporting? Evidence from internal controls. Review of Accounting Studies, 1-44.

-          Experimental/Surveys/Interviews/Opinions

• Munoko, I., Brown-Liburd, H. L., and Vasarhelyi, M. (2020). The ethical implications of using artificial intelligence in auditing. Journal of Business Ethics, 167, 209-234.

• Estep, C., Griffith, E. E., and MacKenzie, N. L. (2023). How do financial executives respond to the use of artificial intelligence in financial reporting and auditing?. Review of Accounting Studies, 1-34.

• Libby, R., & Witz, P. D. (2024). Can artificial intelligence reduce the effect of independence conflicts on audit firm liability?. Contemporary Accounting Research.

• Estep, C., Griffith, E. E., & MacKenzie, N. L. (2024). How do financial executives respond to the use of artificial intelligence in financial reporting and auditing?. Review of Accounting Studies, 29(3), 2798-2831.

• Lanz, L., Briker, R., & Gerpott, F. H. (2024). Employees adhere more to unethical instructions from human than AI supervisors: Complementing experimental evidence with machine learning. Journal of Business Ethics, 189(3), 625-646.Costello, A. M.,

• Commerford, B. P., Eilifsen, A., Hatfield, R. C., Holmstrom, K. M., & Kinserdal, F. (2024). Control issues: How providing input affects auditors' reliance on artificial intelligence. Contemporary Accounting Research, 41(4), 2134-2162.

• Samiolo, R., Spence, C., & Toh, D. (2024). Auditor judgment in the fourth industrial revolution. Contemporary accounting research, 41(1), 498-528.

Use AI/ML to create constructs

• Li, F. (2010). The information content of forward‐looking statements in corporate filings—A naïve Bayesian machine learning approach. Journal of accounting research, 48(5), 1049-1102.

• Brown, N. C., Crowley, R. M., & Elliott, W. B. (2020). What are you saying? Using topic to detect financial misreporting. Journal of Accounting Research, 58(1), 237-291.

• Hsieh, T. S., Kim, J. B., Wang, R. R., & Wang, Z. (2020). Seeing is believing? Executives' facial trustworthiness, auditor tenure, and audit fees. Journal of Accounting and Economics, 69(1), 101260.

• Peng, L., Teoh, S. H., Wang, Y., & Yan, J. (2022). Face value: Trait impressions, performance characteristics, and market outcomes for financial analysts. Journal of Accounting Research, 60(2), 653-705.

• Call, A. C., Flam, R. W., Lee, J. A., & Sharp, N. Y. (2024). Managers’ use of humor on public earnings conference calls. Review of Accounting Studies, 29(3), 2650-2687.

Man vs Machine

• Costello, A. M., Down, A. K., & Mehta, M. N. (2020). Machine+ man: A field experiment on the role of discretion in augmenting AI-based lending models. Journal of Accounting and Economics, 70(2-3), 101360.

• Commerford, B. P., Dennis, S. A., Joe, J. R., & Ulla, J. W. (2022). Man versus machine: Complex estimates and auditor reliance on artificial intelligence. Journal of Accounting Research, 60(1), 171-201.

• Liu, M. (2022). Assessing human information processing in lending decisions: A machine learning approach. Journal of Accounting Research, 60(2), 607-651.

ML as a research tool

• Hallman, N. J., Kartapanis, A., & Schmidt, J. J. (2022). How do auditors respond to competition? Evidence from the bidding process. Journal of Accounting and Economics, 73(2-3), 101475.

Large Language Models

• Huang, Allen H., et al. “FinBERT: A Large Language Model for Extracting Information from Financial Text.” Contemporary Accounting Research, vol. 40, no. 2, 2023, pp. 806–41, https://doi.org/10.1111/1911-3846.12832.