This review work makes significant contributions to both theoretical and practical areas. For theory, it identifies unexplored study areas (research gaps), hence influencing future research orientations and theoretical underpinnings. Furthermore, this review contributes to the development of a theoretical framework (CACS), which will help stakeholders comprehend and conceptualize the use of AI in the IAF and can serve as a foundation for future research. In reality, this study assists internal auditors in assessing and understanding the possible benefits and risks of using AI in their organization's IAF.
Table of Contents
1. Introduction
2. Artificial Intelligence
3. Research Questions
4. Literature Review
4.1 Technology
4.2 Internal audit function
4.2.1 An Investigation of Artificial Intelligence Application in Auditing.
4.2.2 Artificial Intelligence and Ethical Professional Judgments in a Small Audit Firm Context
4.2.3 Artificial intelligence application in auditing
4.2.4 Ethics, Artificial Intelligence, Auditing
4.2.5 Artificial Intelligence (AI) in the Education of Accounting and Auditing Profession
4.2.6 Opportunities for developing artificial intelligence in tuning the quality of internal auditing
4.2.7 Artificial intelligence and auditing in small- and medium-sized firms
4.3 Framework Addresses Critical Factors
4.3.1 AI Governance
4.3.2 Data Architecture and Infrastructure
4.3.3 Data Quality
4.3.4 Measuring Performance of AI
4.3.5 The Human Factor
4.3.6 The Black Box Factor
4.4 AI – The Basics
4.4.1 AI Opportunities and Risks
4.4.1.1 Opportunities
4.4.1.2 Risks
4.4.2 Internal Audit’s Role
4.4.3 AI Competencies: Filling the Understanding Gap
4.5 AI Auditing Framework
4.5.1 Strategy
4.5.2 AI Governance
4.5.3 Data Architecture and Infrastructure
4.5.4 Data Quality
4.5.5 Measuring Performance of AI
4.5.6 The Human Factor
4.6 The Role of Artificial Intelligence in Auditing
4.6.1 Recent trends related to the topic
5. Results
6. Conclusions
Research Objectives & Topics
This systematic literature review aims to analyze the current state of artificial intelligence (AI) application within the internal audit function (IAF). It investigates how AI influences audit effectiveness, identifies research gaps in existing literature, and provides a theoretical framework (CACS) to help organizations evaluate the risks and benefits of integrating AI into their auditing processes.
- Technological integration and modernization of internal audit processes.
- Theoretical foundations and adoption frameworks for AI in auditing.
- The human-AI collaborative dynamic and associated competence gaps.
- Ethical considerations, risk management, and governance of AI-driven audit systems.
- Evolution of audit methodologies from sample-dependent to predictive analysis.
Excerpt from the Book
The Role of Artificial Intelligence in Auditing
In the financial sector, auditing is similar to a company check-up. It's a strategy to ensure that their financial accounts are correct and reliable. Historically, auditing required a great deal of physical labor, with auditors spending hours verifying documents and records. However, with the advent of Artificial Intelligence (AI), auditing is becoming more efficient and effective.
For auditors, AI functions similarly to a smart assistant. It can handle vast amounts of data faster than humans, assisting auditors in detecting errors and potential fraud. One of the most common applications of AI in auditing is data analytics. AI algorithms can instantly scan hundreds of documents and transactions, flagging any that appear suspect. This saves auditors time and allows them to focus on more important tasks.
Another key application of artificial intelligence in auditing is risk assessment. AI may evaluate data to uncover financial hazards, such as fraud or accounting problems. Auditors can assist businesses manage risks and avoid future problems by identifying them early on.
Artificial intelligence is also utilized to increase audit quality. For example, AI systems can examine financial data to detect trends and patterns that may indicate fraudulent conduct. They can also help auditors comprehend a company's financial health by providing information about its financial performance.
One of the primary advantages of AI in auditing is its capacity to manage massive amounts of data. Companies generate more data than ever before as financial records are increasingly digitized. AI can handle this data fast and efficiently, allowing auditors to study more information than they could manually.
Summary of Chapters
Introduction: Provides context on how digitalization necessitated the use of AI in internal audits by increasing data complexity and management requirements. It outlines the scope of the study and justifies the relevance of AI in the modern auditing profession.
Artificial Intelligence: Discusses the broader impact of the Fourth Industrial Revolution and how AI is transforming standard accounting and auditing practices by automating labor-intensive tasks.
Research Questions: Details the theoretical and practical focus of the paper, specifically identifying the research gaps and the necessity for a structured framework to integrate AI into existing IAFs.
Literature Review: Offers a comprehensive synthesis of existing research, covering organizational competitive advantage, the internal audit function, and specific critical factors like AI governance, data architecture, and human factors.
Results: Presents the analysis of recent academic publications, noting the surge in research popularity between 2021 and 2022 and highlighting the need for more in-depth conceptual studies on AI in auditing.
Conclusions: Summarizes the study’s findings, arguing that while AI is in its early stages of widespread audit implementation, it is essential for future organizations; it also suggests directions for future research in developing nations.
Keywords
Artificial Intelligence, Internal Audit Function, IAF, Audit Efficiency, Data Analytics, Risk Management, Competitive Advantage, Technological Adoption, Digitalization, CACS Framework, Automation, Ethical AI, Governance, Predictive Auditing, Fraud Detection.
Frequently Asked Questions
What is the primary focus of this research paper?
The paper provides a systematic literature review on the implementation and impact of artificial intelligence technologies within the internal audit function of modern organizations.
What are the central themes discussed in this work?
Key themes include the evolution of audit methodologies through AI, the importance of data quality and governance, the shift in required auditor competencies, and the potential risks inherent in automated decision-making.
What is the primary goal of this systematic literature review?
The goal is to map the current state of research, bridge gaps in knowledge, and propose a conceptual framework (CACS) to assist organizations in deploying AI solutions in their internal audit departments.
Which scientific methods were employed to conduct this study?
The research design adheres to standard systematic literature review (SLR) protocols, analyzing publications from the Web of Science database between 2019 and 2023 to identify trends, research methods, and geographical focus areas.
What core elements does the paper address in the main body?
The main body examines technological theoretical models, the internal audit framework, the role of human-AI collaboration, and the practical implementation of AI-driven audit governance strategies.
Which specific keywords define this study?
Core keywords include Artificial Intelligence, Internal Audit Function, Audit Efficiency, Data Analytics, Ethical AI, and the CACS Framework.
How does the CACS framework proposed in the paper work?
The CACS framework (Commitment, Access, Capability, and Skilling) is suggested as a structured, four-pillar strategy for enterprises aiming to deploy AI tools effectively within their audit workflows.
What does the study conclude regarding the replacement of human auditors by AI?
The study argues that AI does not replace human judgment but rather acts as a sophisticated assistant, allowing auditors to pivot from routine compliance checks to more complex, value-added, and fraud-predicting audits.
- Quote paper
- Ph Zygoulis (Author), 2024, Artificial intelligence and the future of the internal audit function, Munich, GRIN Verlag, https://www.grin.com/document/1499142
