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 or working papers presented at high-profile confereces.

Introduction of ML to accounting academics

Perols, Johan L., Robert M. Bowen, Carsten Zimmermann, and Basamba Samba. "Finding needles in a haystack: Using data analytics to improve fraud prediction." The Accounting Review 92, no. 2 (2017): 221-245.
Bertomeu, J. (2020). Machine learning improves accounting: discussion, implementation and research opportunities. Review of Accounting Studies, 25(3), 1135-1155.
Krupa, J., & Minutti-Meza, M. (2021). Regression and Machine Learning Methods to Predict Discrete Outcomes in Accounting Research. Journal of Financial Reporting.

ML as a modeling technique to make predictions

Misstatement/restatement

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

Fraud

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.
Bao, Y., Ke, B., Li, B., Yu, Y. J., & Zhang, J. (2020). Detecting accounting fraud in publicly traded US firms using a machine learning approach. Journal of Accounting Research, 58(1), 199-235.
Beneish, M. D., & Vorst, P. (2022). The cost of fraud prediction errors. The Accounting Review, 97(6), 91-121.
Jiang, L., Vasarhelyi, M., & Zhang, C. A. (2022). Using Semi-Supervised Learning to Detect and Predict Unlabeled Restatements. Working paper.

Default/bankruptcy

Jones, S. (2017). Corporate bankruptcy prediction: a high dimensional analysis. Review of Accounting Studies, 22, 1366-1422.
Gu, Y., Vasarhelyi, M., and Zhang, C. (2023). Going Concern Opinions (GCOs) Are Noisy and Biased – How Can We Improve Them? Working paper.

Earnings

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

Accounting estimates

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

Tax rates

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

ML to construct variable of interest in empirical research

Hunt, J. O., Rosser, D. M., & Rowe, S. P. (2021). Using machine learning to predict auditor switches: How the likelihood of switching affects audit quality among non-switching clients. Journal of Accounting and Public Policy, 40(5), 106785.
Hunt, E., Hunt, J., Richardson, V. J., & Rosser, D. (2022). Auditor Response to Estimated Misstatement Risk: A Machine Learning Approach. Accounting Horizons, 36(1), 111-130.

ML used in other ways:

Bertomeu, Jeremy, Edwige Cheynel, Yifei Liao, and Mario Milone. "Using machine learning to measure conservatism." Available at SSRN 3924961 (2021).
Geertsema, P., & Lu, H. (2023). Relative Valuation with Machine Learning. Journal of Accounting Research, 61(1), 329-376.

Man vs Machine

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.
Gu, Y., Vasarhelyi, M., and Zhang, C. (2023). Going Concern Opinions (GCOs) Are Noisy and Biased – How Can We Improve Them? Working paper.

Empirical work that studies the impact of AI/ML adoption

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., & Srinivasan, S. (2023). Going digital: Implications for firm value and performance. Review of Accounting Studies, 1-47.
Brown. N., Louis., H., Rozario, A., & Zhang, C. A. (2023). The Effect of Artificial Intelligence on the Accuracy of Management Earnings Forecasts. Working paper
Anantharaman. D., Rozario, A., & Zhang, C. A. (2022). The Effect of Artificial Intelligence on Financial Reporting Quality. Working Paper

Qualitative studies regarding AI/ML adoption

Estep, C., Griffith, E. E., & 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.
Eulerich, M., Masli, A., Pickerd, J., & Wood, D. A. (2023). The Impact of Audit Technology on Audit Task Outcomes: Evidence for Technology‐Based Audit Techniques. Contemporary Accounting Research, 40(2), 981-1012.
Munoko, I., Brown-Liburd, H. L., & Vasarhelyi, M. (2020). The ethical implications of using artificial intelligence in auditing. Journal of Business Ethics, 167, 209-234.
Kokina, J., & Davenport, T. H. (2017). The emergence of artificial intelligence: How automation is changing auditing. Journal of emerging technologies in accounting, 14(1), 115-122.

Explainable AI

Zhang, C. A., Cho, S., & Vasarhelyi, M. (2022). Explainable artificial intelligence (xai) in auditing. International Journal of Accounting Information Systems, 46, 100572.

Related Books

Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction Machines: The Simple Economics of Artificial Intelligence. Harvard Business Press.
Agrawal, A., Gans, J., & Goldfarb, A. (2022). Power and Prediction. Harvard Business Press.
Kahneman, D., Sibony, O., & Sunstein, C. R. (2021). Noise: A flaw in human judgment. Little, Brown.
Molnar, C. (2020). Interpretable machine learning. Lulu. com.