arbiter
JournalsAccount

A Risk Assessment Framework for Cognitive Process Automation in Audit

Arion Cheong et al.

Journal of Emerging Technologies in Accounting2026https://doi.org/10.2308/jeta-2024-031article
AJG 1ABDC B
Weight
0.50

Abstract

Cognitive process automation (CPA) is the process of automating knowledge-intensive tasks that require reasoning, interpretation, and decision-making using artificial intelligence (AI) agentic workflows. Although CPA offers significant potential in audit, auditors often struggle to determine what tasks are suitable for CPA and manage the risks associated with CPA implementation. This study proposes an AI risk reporting tool that operationalizes an AI Risk Assessment Framework for CPA deployment in auditing. Drawing on Task-Technology Fit Theory and Cognitive Load Theory, our framework includes three sequential stages: assessing audit task suitability for CPA, quantifying the cognitive load measure to proxy for AI implementation risk, and translating the measured risk into prescriptive human oversight requirements. This study contributes to the literature by proposing a framework to bridge the current governance gap in CPA deployment and its associated risk. JEL Classifications: C88; D81; M15; M41; M42; O33.

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.2308/jeta-2024-031

Or copy a formatted citation

@article{arion2026,
  title        = {{A Risk Assessment Framework for Cognitive Process Automation in Audit}},
  author       = {Arion Cheong et al.},
  journal      = {Journal of Emerging Technologies in Accounting},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.2308/jeta-2024-031},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

A Risk Assessment Framework for Cognitive Process Automation in Audit

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.