The functional metamorphosis of financial reports: From retrospective model to real-time predictive analytics
DOI:
https://doi.org/10.68210/jgeor.a92Keywords:
financial reporting, predictive analytics, machine learning, continuous reporting, information contentAbstract
Financial reports were designed as periodic, historical-cost records that summarize past transactions for external users. Empirical accounting research has shown both the value and the limits of this model, while advances in statistical modeling, machine learning and data infrastructure have raised expectations for timely, forward-looking information. This article develops a conceptual account of that shift as a three-stage functional change: from a retrospective record, through an analytical-predictive tool, to a real-time predictive information service. Using a structured, qualitative synthesis of foundational and recent peer-reviewed studies and one leading monograph, it proposes a layered reference architecture for real-time predictive reporting and identifies governance, assurance and data-quality conditions for reliable use. The argument is conceptual and does not rely on new empirical data.
Downloads
References
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
Ball, R., & Brown, P. (1968). An empirical evaluation of accounting income numbers. Journal of Accounting Research, 6(2), 159–178. https://doi.org/10.2307/2490232
Beaver, W. H. (1968). The information content of annual earnings announcements. Journal of Accounting Research, 6, 67–92. https://doi.org/10.2307/2490070
Bertomeu, J., Cheynel, E., Floyd, E., & Pan, W. (2021). Using machine learning to detect misstatements. Review of Accounting Studies, 26(2), 468–519. https://doi.org/10.1007/s11142-020-09563-8
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. https://doi.org/10.1007/s11142-020-09546-9
Lev, B., & Gu, F. (2016). The end of accounting and the path forward for investors and managers. Wiley.
Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. The Journal of Finance, 66(1), 35–65. https://doi.org/10.1111/j.1540-6261.2010.01625.x
Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109–131. https://doi.org/10.2307/2490395
Vasarhelyi, M. A., & Halper, F. B. (1991). The continuous audit of online systems. Auditing: A Journal of Practice & Theory, 10(1), 110–125.
Vasarhelyi, M. A., Kogan, A., & Tuttle, B. M. (2015). Big data in accounting: An overview. Accounting Horizons, 29(2), 381–396. https://doi.org/10.2308/acch-51071
Downloads
Published
Issue
Section
Categories
License
Copyright (c) 2026 Journal of Green Economy and Optimization Research
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright © The Author(s).
Authors retain copyright in their work and grant the Journal of Green Economy and Optimization Research (JGEOR) the right of first publication.
Articles published in JGEOR are distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits use, sharing, adaptation, distribution, and reproduction in any medium or format, provided that appropriate credit is given to the original author(s) and the source, a link to the Creative Commons licence is provided, and any changes made are indicated.
Authors are responsible for obtaining permission to reproduce any third-party material that is not covered by the article's Creative Commons licence.
