See the Nissim monograph on applying AI for assessing earning quality:
Nissim, D. 2024. Earning Quality, Fundamental Analysis and Equity Valuation, Columbia Business School. At https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3794378.
The following paper applies pattern recognition to detect anomalies in accounting data:
Pattern Recognition and Anomaly Detection in Bookkeeping Data, by Pierre Liang, Aluna Wang, Leman Akoglu, and Christos Faloutsos. Obtain the paper from Pierre Liang at the Tepper School of Business, Carnegie Mellon University.
Here’s a paper that applies large language models (LLMs) to identify sustainable earnings, testing alternative approaches. It leaves an open question: Can AI perform better than using business judgment to identify unsustainable items?
Shaffer, M. and C. Wang. 2024. Scaling Core Earnings Measurement with Large Language Models. At https://ssrn.com/abstract=4979504.
This paper applies machine learning to predict misstatements:
Bertomeu, J., E. Cheynel, E. Floyd, and W. Pan. 2021. Using Machine Learning to Detect Misstatements. Review of Accounting Studies 26 (2), 468-519.
