Continuous Artificial Intelligence-Based Reporting, Monitoring, and Assurance
ABSTRACT This paper introduces Continuous Artificial Intelligence (AI)-based Reporting, Monitoring, and Assurance (CAIBRMA), extending traditional Continuous Auditing/Continuous Monitoring (CACM) frameworks through AI integration. To provide clarity for future research and practice, we establish distinct boundaries between reporting, monitoring, and assurance functions. By addressing implementation barriers that have limited CACM adoption, AI tools enable us to outline specific integration opportunities across the reporting, monitoring, and assurance functions. JEL Classifications: M41; M42; D83
Authors
- Miklos A. Vasarhelyi (ORCID: https://orcid.org/0000-0003-3205-476X)
- Yi Yang (ORCID: https://orcid.org/0000-0002-6107-4607)
- Xiaoyu Hu (ORCID: https://orcid.org/0009-0007-3559-4813)
Institutions
- Rutgers, The State University of New Jersey (US)
- Beijing Institute of Technology (CN)
Publication Details
- Journal
- Journal of Emerging Technologies in Accounting
- Published
- 2026-02-01
- DOI
- https://doi.org/10.2308/jeta-2025-058
- Primary Topic
- Adversarial Robustness in Machine Learning
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Beijing Institute of Technology