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, 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.
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., and Pan, W. (2021). Using machine learning to detect misstatements. Review of Accounting Studies, 26(2), 468-519.
Zhang, C., Jiang, L., Cho, S., and Vasarhelyi, M. (2024). Predicting Material Misstatement Using Machine Learning. Working Paper. Presented at the 2021 PCAOB Conference.
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., 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.
Beneish, M. D., and Vorst, P. (2022). The cost of fraud prediction errors. The Accounting Review, 97(6), 91-121.
Jiang, L., Vasarhelyi, M., and Zhang, C. A. (2024). Towards Real-Time Financial Statement Fraud Detection Using Machine Learning. Working paper. Presented at the 2023 PCAOB Conference.
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. (2024). Going Concern Opinions (GCOs) Are Noisy and Biased – How Can We Improve Them? Working paper. Presented at the 2023 PCAOB Conference.
Earnings
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.
Jones, S., Moser, W. J., and Wieland, M. M. (2023). Machine learning and the prediction of changes in profitability. Contemporary Accounting Research, 40(4), 2643-2672.
Accounting estimates
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.
Tax rates
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.
ML to construct variable of interest in empirical research
Hunt, J. O., Rosser, D. M., and 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., and 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., and 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. (2024). Going Concern Opinions (GCOs) Are Noisy and Biased – How Can We Improve Them? Working paper. Presented at the 2023 PCAOB Conference.
Empirical work that studies the impact of AI/ML adoption
Fedyk, A., Hodson, J., Khimich, N., and 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.
Brown. N., Louis., H., Rozario, A., and Zhang, C. A. (2024). Artificial Intelligence and Management Earnings Forecasts. Working paper.
Anantharaman. D., Rozario, A., and Zhang, C. A. (2024). Artificial Intelligence and Financial Reporting Quality. Working Paper. Presented at the 2022 PCAOB Conference.
Ashraf, M. (2024). Does automation improve financial reporting? Evidence from internal controls. Review of Accounting Studies, 1-44.
Qualitative studies regarding AI/ML adoption
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.
Eulerich, M., Masli, A., Pickerd, J., and 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., and Vasarhelyi, M. (2020). The ethical implications of using artificial intelligence in auditing. Journal of Business Ethics, 167, 209-234.
Kokina, J., and 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., and Vasarhelyi, M. (2022). Explainable artificial intelligence (xai) in auditing. International Journal of Accounting Information Systems, 46, 100572.
Related Books
Agrawal, A., Gans, J., and Goldfarb, A. (2018). Prediction Machines: The Simple Economics of Artificial Intelligence. Harvard Business Press.
Agrawal, A., Gans, J., and 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.