Journal of Economic, Finance Research and Review (JEFRR) · Research article · Open access
A Comprehensive Review of Predictive Analytics in Audit Risk Assessment for Advancing Data-Driven Decision-Making and Financial Transparency
Solomon Doe Adjaottor, Emmanuel Okyem Afriyie, Godson Teye Apaflo
4 September 2026 · Vol. 2, Issue 9, pp. 593-601
DOI: 10.65150/EP-jefrr/V2E9/2026-01
Download full text (PDF)Cite this article
What is this article about?
Audit risk assessment remains a foundational element of the financial statement audit, yet traditional approaches that rely primarily on professional judgment and sampling face growing limitations. Rising volumes of transactional data, increasing complexity in financial reporting, and more sophisticated forms of fraud and misstatement have reduced the effectiveness of conventional risk assessment methods.
Article at a glance
- Published
- 4 September 2026
- Volume / issue / pages
- Vol. 2, Issue 9, pp. 593-601
- ISSN
- 3051-3650 (online) · 3051-3642 (print)
- Licence
- CC BY 4.0 — free to read and reuse with attribution
- Peer review
- Double-blind, at least two independent reviewers
- Publisher
- EP Journals Group
Abstract
Audit risk assessment remains a foundational element of the financial statement audit, yet traditional approaches that rely primarily on professional judgment and sampling face growing limitations. Rising volumes of transactional data, increasing complexity in financial reporting, and more sophisticated forms of fraud and misstatement have reduced the effectiveness of conventional risk assessment methods. Although predictive analytics, including machine learning, anomaly detection, deep learning, and generative artificial intelligence, has been proposed as a means of addressing these challenges, existing research remains fragmented across techniques, applications, and institutional contexts. As a result, it is difficult to determine how predictive analytics advances audit risk assessment, under what conditions its benefits are realized, and where important constraints persist. This review addresses the problem by systematically synthesizing the relevant literature. It examines the predictive techniques employed, the audit activities in which they are applied, the conditions that shape their effectiveness, and the barriers that limit implementation. Building on this synthesis, the review develops the Integrated Predictive Analytics–Audit Risk Assessment Framework, which organizes technical capabilities, operating mechanisms, moderating conditions, and outcomes related to audit quality and financial transparency. By clarifying the current state of knowledge and providing an integrative structure, the review offers a foundation for more coherent research and practice in data-driven audit risk assessment.
Keywords: Predictive analytics, Audit risk assessment, Machine learning in auditing, Fraud detection, Financial transparency
Cite this article
Solomon Doe Adjaottor, Emmanuel Okyem Afriyie, & Godson Teye Apaflo (2026). A Comprehensive Review of Predictive Analytics in Audit Risk Assessment for Advancing Data-Driven Decision-Making and Financial Transparency. Journal of Economic, Finance Research and Review, 2(9), 593-601. https://doi.org/10.65150/EP-jefrr/V2E9/2026-01
@article{Solomon2026,
title = {A Comprehensive Review of Predictive Analytics in Audit Risk Assessment for Advancing Data-Driven Decision-Making and Financial Transparency},
author = {Solomon Doe Adjaottor and Emmanuel Okyem Afriyie and Godson Teye Apaflo},
journal = {Journal of Economic, Finance Research and Review},
year = {2026},
volume = {2},
number = {9},
pages = {593-601},
doi = {10.65150/EP-jefrr/V2E9/2026-01},
url = {https://doi.org/10.65150/EP-jefrr/V2E9/2026-01}
}Questions about this article
Who wrote this article?
Solomon Doe Adjaottor, Emmanuel Okyem Afriyie, Godson Teye Apaflo.
Where and when was it published?
In Journal of Economic, Finance Research and Review, Volume 2, Issue 9, pages 593-601, published by EP Journals Group on 4 September 2026.
Is this article free to read?
Yes. It is open access under a Creative Commons Attribution 4.0 (CC BY 4.0) licence, so anyone can read, share and reuse it with attribution.
What is the DOI of this article?
10.65150/EP-jefrr/V2E9/2026-01 (https://doi.org/10.65150/EP-jefrr/V2E9/2026-01).
How do I cite this article?
APA: Solomon Doe Adjaottor, Emmanuel Okyem Afriyie, & Godson Teye Apaflo (2026). A Comprehensive Review of Predictive Analytics in Audit Risk Assessment for Advancing Data-Driven Decision-Making and Financial Transparency. Journal of Economic, Finance Research and Review, 2(9), 593-601. https://doi.org/10.65150/EP-jefrr/V2E9/2026-01
More research from JEFRR
- Understanding Green Purchase Intention in the Digital Era: Green Communication, Brand Image, And Brand Trust
Dini Safitri, Sharon Claudia Rambu Podu Diah, Olaf Tri Wilopo Simanjuntak, Muhammad Fadli · Oct 2026
- Digital Marketing Transformation and Consumer Purchase Behaviour in Post-Pandemic Markets
Rezwan Hossain, Muhammad Shoaib, Darda Rahman · Oct 2026
- Effectiveness of Financial Literacy and Financial Inclusion on Competitiveness of Small and Medium Sized Enterprises in Uganda: The Mediating Role of Financial Planning
Eric Nzibonera, Godfrey Akileng, David Namanya · Oct 2026
- Relationship Between Community Policing Participation and Crime Reduction Among Lagos State Neighbourhood Safety Corps (LNSC) in Ikorodu West and Ikorodu Central, Lagos State
Oluwaseun Segun Adejuwon, Jatau Seikhan Nathan, Dr. Ajagun Olusegun Peter, Adebisi Gbenga Adejuwon · Oct 2026
- The Double Divide Revisited: Sector Composition, Within-Sector Adoption and Artificial Intelligence Use Among Minority- And Women-Owned U.S. Businesses
Olufemi Olumayowa Oni, Abimbola Usman, Olusegun Opeyemi Oni, Promise Okwarajiaku · Oct 2026