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.
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.
Stratopoulos, T. C., & Wang, V. X. (2025). Artificial intelligence and accounting research: A framework and agenda. International Journal of Accounting Information Systems, 56, 100760.
Dong, M. M., Stratopoulos, T. C., & Wang, V. X. (2024). A scoping review of ChatGPT research in accounting and finance. International Journal of Accounting Information Systems, 55, 100715.
de Kok, T. (2025). ChatGPT for textual analysis? How to use generative LLMs in accounting research. Management Science.
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.
Cao, S., W. Chen, G. Ma, and S. Srinivasan (2026). Generative AI in capital markets: information production, dissemination, and processing. Journal of Accounting Research 64.
Ludwig, J., S. Mullainathan, and A. Rambachan (2026). Large language models: an applied econometric framework. Annual Review of Economics. (NBER WP 33344.)
Graham, G. M., & Tucker, J. W. (2026). The Use of Artificial Intelligence in Academic Research. Accounting Horizons, 40(1), 1-10.
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.
Parker, C., Jiang, L., Cho, S., & Vasarhelyi, M. (2025). Predicting Material Misstatements Using Machine Learning. The Accounting Review.
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.
Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223-2273.
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.
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.
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.
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.
Kim, A., M. Muhn, and V. Nikolaev (2023). Bloated disclosures: can ChatGPT help investors process information? SSRN 4425527.
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.
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.
Cao, Y., Chen, L., Tucker, J. W., & Wan, C. (2025). Can generative AI help identify peer firms?. Review of Accounting Studies, 1-43.
Bernard, D., E. Blankespoor, T. de Kok, and S. Toynbee (2026). Using GPT to measure business complexity. The Accounting Review 101(3): 67–102.
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.
Cao, S., W. Jiang, B. Yang, and A. L. Zhang (2023). How to talk when a machine is listening: corporate disclosure in the age of AI. Review of Financial Studies 36(9): 3603–3642.
Anantharaman, D., Rozario, A., & Parker, C. A. (2023). Artificial intelligence and financial reporting quality. Available at SSRN 4625279.
Brown, N. C., Louis, H., Rozario, A., & Parker, C. A. (2024). Artificial intelligence and management earnings forecasts. Available at SSRN 3850823.
Tucker, J., Wang, J. J., & Zhao, R. (2024). Corporate use of AI and subsequent product market performance. Available at SSRN 5044024.
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.
Böke, J., De la Parra, D., Gallemore, J., & Glaeser, S. (2024). Artificial intelligence and white-collar work: Evidence from the accounting profession. Available at SSRN 5039640.
Bertomeu, J., Lin, Y., Liu, Y., & Ni, Z. (2025). The impact of generative AI on information processing: Evidence from the ban of ChatGPT in Italy. Journal of Accounting and Economics, 101782.
Cheng, Q., Lin, P., & Zhao, Y. (2025). Does generative AI facilitate investor trading? Early evidence from ChatGPT outages. Journal of Accounting and Economics, 101821.
Blankespoor, E., E. deHaan, and Q. Li (2026). Generative AI in financial reporting. Journal of Accounting Research 64. (SSRN 4986017; GenScore data at github.com/ed-dehaan/GenScore.)
Bradshaw, M. T., Ma, C., Yost, B. P., & Zou, Y. (2026). Generative AI use by capital market information intermediaries: Evidence from Seeking Alpha. Journal of Accounting Research, 64(3), 1233-1286
Jia, N., Li, N., Ma, G., & Xu, D. (2026). Corporate responses to generative AI: Early evidence from conference calls. Review of Accounting Studies, 31(2), 649-703.
Chang, A. Y., Dong, X., Martin, X., & Zhou, C. (2026). AI democratization and trading inequality. Journal of Accounting Research, 64(3), 1287-1331.
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.
Bhaskar, L. S., Jones, A. K., & Kadous, K. (2024). An Investigation into How Generative AI Can Improve Auditors' Decisions. Available at SSRN 5042792.
Choi, J. H., & Xie, C. (2025). Human+ AI in accounting: Early evidence from the field.
Christ, M. H., Kim, M. J., & Yip, M. A. (2025). Word on the street: Field evidence of artificial intelligence use in sell-side equity research. Available at SSRN 5071836.
Aku, H., Zhang Parker, C., & Stone, C. W. (2026). Applications of Generative Artificial Intelligence in Tax Practices: Evidence from Tax Professionals. Accounting Horizons, 1-16.
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.
Cao, S., W. Jiang, J. Wang, and B. Yang (2024). From man vs. machine to man + machine: the art and AI of stock analyses. Journal of Financial Economics 160: 103910.
Liu, M. (2022). Assessing human information processing in lending decisions: A machine learning approach. Journal of Accounting Research, 60(2), 607-651.
Gu, Y., Parker, C. A. Z., & Vasarhelyi, M. (2026). Synergizing machine and human judgment: evidence from going concern assessment. Journal of Accounting and Public Policy, 57, 107435.
AI exposure: captures how firms are likely to be disrupted by AI
Webb (2020) AI Exposure score: use NLP to compute textual overlap between the verb–noun pairs in AI patent texts and those in O*NET task descriptions. An occupation is exposed to the extent that patented AI inventions describe doing the things that occupation does.
AIOE by Felten, Raj, and Seamans (2021): The measure maps roughly 10 AI applications (image recognition, image generation, reading comprehension, translation, strategic game play) onto the 52 human abilities catalogued in O*NET, using crowd-sourced ratings of application–ability relatedness, then aggregates ability-level exposure to the occupation level using O*NET's ability importance and level data
Eloundou, Manning, Mishkin, and Rock (2024): Classify O*NET tasks using both human annotators and GPT-4 against an explicit rubric: would direct LLM access reduce the time to complete this task by at least half, at equivalent quality? Tasks are labelled by exposure tier (direct LLM access alone, versus requiring additional software built on top), then averaged to occupations.
Eisfeldt, Schubert, Taska, and Zhang (2026): first firm-level Gen AI workforce exposure meausre. First, score O*NET tasks against a detailed rubric using GPT-4: label a task exposed if direct LLM access can halve completion time at equivalent quality. Second, aggregate to firms using LinkedIn occupational employment structures.
AI investment: captures firms' investment in AI
Acemoglu, Autor, Hazell, and Restrepo (2022): identify AI-related job postings from burning glass and lightcast by skill keywords
Babina, Fedyk, He, and Hodson (2024): Identify AI-skilled employees from individual employee résumés (Cognism data, covering roughly 64% of the US workforce as of 2019), and measure firm AI investment as the share of the workforce with AI skills. The same approach generates parallel measures for robotics, non-AI IT, and non-AI data analytics.
Artificial Intelligence Patent Dataset (AIPD) developed by the Office of Chief Economist (Giczy, Pairolero, and Toole 2022).
AI investment intent extracted from corporate text, such as conference calls (ChatGPT investment scores by Jha, Qian, Weber, and Yang 2024).
AI adoption: captures firms' actual adoption of AI
public disclosure with manual validation: Brown et al. (2024); Tucker, Wang, and Zhao (2024); Anatharaman et al. (2023)