Investigating the Function of Artificial Intelligence in Audit Judgement

Introduction: Auditors are frequently assigned the responsibility of reviewing intricate financial instruments or assessing the sufficiency of internal controls in light of potential fraud threats. In such circumstances, discernment is necessary to analyse facts, comprehend intent, and evaluate the reliability of answers offered by management. Although AI has demonstrated its disruptive potential across various domains, auditing necessitates a series of intricate and nuanced decisions that demand not only technical study but also human intuition, experience, and ethical considerations. Objectives: This article explores the role of AI in auditing, focusing on its potential to either replicate or enhance auditor judgment, as well as the challenges that remain in achieving full automation in the auditing process. Methods: The approach taken in this study is a literature review, which involves gathering, assessing, and analyzing relevant academic works related to the topic. This review contributes to a deeper understanding of how AI is applied in auditing and its effects on auditor decision-making. Results: AI has the potential to revolutionize the auditing field by automating repetitive tasks, analyzing large volumes of data, and providing data-driven insights. The use of auditor judgment—grounded in experience, context, professional scepticism, and ethical considerations—continues to be a crucial element of auditing. Although AI can aid in data analysis and risk evaluation, it cannot emulate the intricate decision-making process inherent to human auditors. The future of auditing will probably integrate AI-driven tools alongside human discernment, with AI assisting auditors in making better informed and efficient conclusions. AI is expected to collaborate with human auditors, enhancing their judgement instead of supplanting it. By utilising AI technology, auditors may concentrate on advanced decision-making, while AI manages the more mundane elements of data processing and pattern identification. Conclusions: While it’s unlikely that AI will fully replace the judgment of human auditors, it has the potential to significantly enhance the auditing process. AI can serve as a powerful tool that helps auditors with data analysis, identifying risks, and providing insights that support decision-making. However, human judgment will still be essential in interpreting these insights and drawing final conclusions, taking into account ethical, regulatory, and situational factors.

Authors

Publication Details

Journal
Journal of Information Systems Engineering & Management
Published
2025-03-11
DOI
https://doi.org/10.52783/jisem.v10i18s.2984
Primary Topic
Auditing, Earnings Management, Governance
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Investigating the Function of Artificial Intelligence in Audit Judgement

Muhammad Ikbal Abdullah
Journal of Information Systems Engineering & Management
Auditing, Earnings Management, Governance
article

Investigating the Function of Artificial Intelligence in Audit Judgement

Muhammad Ikbal Abdullah
article en

Abstract

Introduction: Auditors are frequently assigned the responsibility of reviewing intricate financial instruments or assessing the sufficiency of internal controls in light of potential fraud threats. In such circumstances, discernment is necessary to analyse facts, comprehend intent, and evaluate the reliability of answers offered by management. Although AI has demonstrated its disruptive potential across various domains, auditing necessitates a series of intricate and nuanced decisions that demand not only technical study but also human intuition, experience, and ethical considerations. Objectives: This article explores the role of AI in auditing, focusing on its potential to either replicate or enhance auditor judgment, as well as the challenges that remain in achieving full automation in the auditing process. Methods: The approach taken in this study is a literature review, which involves gathering, assessing, and analyzing relevant academic works related to the topic. This review contributes to a deeper understanding of how AI is applied in auditing and its effects on auditor decision-making. Results: AI has the potential to revolutionize the auditing field by automating repetitive tasks, analyzing large volumes of data, and providing data-driven insights. The use of auditor judgment—grounded in experience, context, professional scepticism, and ethical considerations—continues to be a crucial element of auditing. Although AI can aid in data analysis and risk evaluation, it cannot emulate the intricate decision-making process inherent to human auditors. The future of auditing will probably integrate AI-driven tools alongside human discernment, with AI assisting auditors in making better informed and efficient conclusions. AI is expected to collaborate with human auditors, enhancing their judgement instead of supplanting it. By utilising AI technology, auditors may concentrate on advanced decision-making, while AI manages the more mundane elements of data processing and pattern identification. Conclusions: While it’s unlikely that AI will fully replace the judgment of human auditors, it has the potential to significantly enhance the auditing process. AI can serve as a powerful tool that helps auditors with data analysis, identifying risks, and providing insights that support decision-making. However, human judgment will still be essential in interpreting these insights and drawing final conclusions, taking into account ethical, regulatory, and situational factors.

Journal of Information Systems Engineering & ManagementVol. 10(18s)
Openalex Percentile: Top 93%
Auditing, Earnings Management, Governance
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.