The role of AI in transforming auditing practices: A global perspective review
Olubusola Odeyemi 1, Kehinde Feranmi Awonuga 2, Noluthando Zamanjomane Mhlongo 3, Ndubuisi Leonard
Ndubuisi 4, Funmilola Olatundun Olatoye 5 and Andrew Ifesinachi Daraojimba 6, *
1 Independent Researcher, Nashville, Tennessee, USA.
2 Independent Researcher, UK.
3 Department of Accounting, City Power, Johannesburg, South Africa.
4 Spacepointe Limited, Rivers State, Nigeria.
5 Independent Researcher, Houston, Texas, USA.
6 Department of Information Management, Ahmadu Bello University, Zaria, Nigeria.
World Journal of Advanced Research and Reviews, 2024, 21(02), 359–370
Publication history: Received on 29 December 2023; revised on 03 February 2024; accepted on 06 February 2024
Article DOI: https://doi.org/10.30574/wjarr.2024.21.2.0460
Abstract
This Review provides a glimpse into the comprehensive examination of the transformative impact of Artificial
Intelligence (AI) on auditing practices globally. The review delves into the multifaceted ways in which AI technologies
are reshaping traditional auditing methodologies, bringing about efficiency, accuracy, and adaptability in the face of an
evolving business landscape. The global perspective of this review encompasses diverse industries and jurisdictions,
offering insights into how AI is redefining the audit landscape on a universal scale. The analysis explores the integration
of AI-driven tools in auditing processes, emphasizing the enhanced capabilities for data analysis, anomaly detection,
and risk assessment. Key themes include the automation of routine audit tasks through AI, enabling auditors to focus
on complex analyses and strategic decision-making. The review also delves into the potential challenges and ethical
considerations associated with the adoption of AI in auditing, recognizing the need for a balance between technological
advancement and maintaining audit quality and integrity. Through a survey of case studies and real-world
implementations, the Review highlights successful instances of AI application in auditing across various sectors. It
elucidates how AI-driven algorithms contribute to real-time auditing, providing auditors with dynamic insights into
financial data, fraud detection, and compliance monitoring. The Review concludes by underlining the global significance
of AI in shaping the future of auditing practices. It calls attention to the imperative for industry stakeholders, regulators,
and auditors to embrace the transformative power of AI responsibly. As technology continues to evolve, this review
encourages a forward-looking approach, fostering a collaborative environment that harnesses the benefits of AI while
addressing the challenges to ensure the continued trustworthiness and effectiveness of auditing practices worldwide.
Keywords: Role; Transforming; Auditing Practice; Perspective; Global
1. Introduction
The landscape of auditing practices is undergoing a profound transformation, driven by the increasing integration of
Artificial Intelligence (AI). AI, with its advanced data analytics, machine learning algorithms, and automation
capabilities, is reshaping how audits are conducted across diverse industries (Fedyk et al., 2022). Traditional audit
methodologies are being augmented and, in some cases, replaced by intelligent technologies that enhance efficiency,
accuracy, and the ability to derive meaningful insights from vast datasets (Spring et al., 2022). In recent years, the
adoption of AI in auditing has grown exponentially, reflecting the industry's recognition of its potential to revolutionize
traditional audit approaches. From automating routine tasks to providing real-time insights and enhancing risk
assessment, AI is becoming an integral component of the auditor's toolkit (Falco et al., 2021). The utilization of AI-driven

Corresponding author: Andrew Ifesinachi Daraojimba
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0.
World Journal of Advanced Research and Reviews, 2024, 21(02), 359–370
tools allows auditors to focus on high-value tasks, fostering a more strategic and analytical approach to the audit
process.
Understanding the transformative role of AI in auditing requires a global perspective that transcends regional
boundaries (Badawy, 2023). Auditing practices vary across jurisdictions, influenced by unique regulatory
environments, industry structures, and cultural contexts. A global perspective ensures a comprehensive review that
captures the diversity of challenges and opportunities associated with AI adoption in auditing on a universal scale. The
significance of this global outlook lies in its ability to unveil patterns, trends, and best practices that emerge from
different corners of the world. It allows us to explore how AI is being harnessed across industries and regulatory
frameworks, offering a holistic understanding of its impact. This approach enables auditors, industry stakeholders, and
regulators to gain insights from successful implementations, learn from challenges faced in various regions, and
collectively shape the future of auditing practices in the age of AI.
As we embark on this global perspective review, the intention is to provide a nuanced exploration of AI's role in
transforming auditing practices. By examining case studies, best practices, and challenges from a diverse range of
contexts, this review seeks to contribute to a comprehensive understanding of how AI is reshaping the audit profession
worldwide.
2. Automation of Routine Audit Tasks
In the evolving landscape of auditing practices, Artificial Intelligence (AI) is emerging as a catalyst for transformative
change, particularly in the realm of automating routine audit tasks (Igou et al., 2023). This segment explores the
profound impact of AI in streamlining repetitive and time-consuming tasks, providing examples of specific routine
activities that can be automated, and highlighting the efficiencies gained through this automation, ultimately enabling
auditors to redirect their focus towards higher-value analyses.
Traditionally, auditors have grappled with the burden of labor-intensive and repetitive tasks, consuming valuable time
that could be better utilized for more strategic and analytical endeavors. AI, with its capacity for pattern recognition,
data processing, and machine learning, offers a paradigm shift in addressing this challenge. By automating routine audit
tasks, AI liberates auditors from the mundane, allowing them to channel their expertise towards complex analyses and
critical decision-making. AI-driven automation is particularly impactful in tasks that involve data extraction,
classification, and reconciliation. For instance, sorting through voluminous financial records, reconciling transactions,
and identifying inconsistencies in datasets are areas where AI excels. This not only expedites the auditing process but
also minimizes the likelihood of human errors associated with manual data handling.
AI algorithms can be trained to extract relevant information from diverse sources, such as financial statements, invoices,
and transaction records. This includes categorizing data based on predefined criteria, significantly reducing the time
required for auditors to manually sift through documents. Reconciling transactions across different ledgers or accounts
is a time-consuming yet crucial aspect of auditing. AI-powered tools can automate the reconciliation process by
matching transactions, identifying discrepancies, and generating detailed reports for further analysis (Tyagi et al.,
2020). Ensuring compliance with regulatory standards and internal policies involves meticulous scrutiny of vast
datasets. AI can automate compliance checks by continuously monitoring transactions and activities, flagging potential
deviations or violations for auditors to investigate. Reviewing contracts, agreements, and communication records is a
routine but essential part of audits. AI can streamline document review processes by quickly scanning and extracting
relevant information. Additionally, sentiment analysis algorithms can be employed to assess the tone and context of
communications, offering insights into potential risks or irregularities.
The efficiencies gained through the automation of routine audit tasks are manifold and extend beyond time-saving
benefits. By leveraging AI, auditors can redirect their efforts towards more value-added activities, ultimately enhancing
the depth and quality of the audit process. Automation significantly accelerates the pace of routine tasks, allowing
auditors to process large volumes of data in a fraction of the time it would take manually (Tiron-Tudor and Deliu, 2022).
This time efficiency is particularly critical in meeting tight audit deadlines and ensuring timely reporting. AI-driven
automation ensures a high level of accuracy and consistency in tasks prone to human error. The algorithms adhere to
predefined rules, minimizing the risk of oversights or discrepancies associated with manual processes.
With routine tasks handled by AI, auditors are liberated to focus on higher-value analyses. This includes interpreting
complex financial data, identifying trends, assessing risk factors, and providing strategic insights that contribute to a
more comprehensive audit report. AI's adaptability to dynamic and diverse data sets is a distinct advantage. Whether
360
World Journal of Advanced Research and Reviews, 2024, 21(02), 359–370
auditing traditional financial statements or navigating intricate blockchain transactions, AI can seamlessly process and
analyze data, providing auditors with a versatile toolset (Arafa et al., 2023).
In conclusion, the automation of routine audit tasks through AI represents a transformative leap in the world of auditing
practices. The global perspective emphasizes the universality of the challenges faced by auditors in grappling with
repetitive tasks, and AI emerges as a global solution that transcends regional boundaries. The efficiencies gained
through this automation not only redefine the role of auditors but also contribute to elevating the overall quality,
accuracy, and timeliness of audit processes on a global scale (Hamdan and Al Habashneh, 2024). As the auditing
profession embraces the capabilities of AI, the potential for continued innovation and advancement in auditing practices
becomes increasingly evident.
3. Enhanced Data Analysis and Anomaly Detection
In the dynamic landscape of auditing, the integration of Artificial Intelligence (AI) is significantly enhancing data analysis
capabilities and revolutionizing anomaly detection processes (Peng et al., 2023). This segment delves into how AI
elevates data analysis in auditing, explores its pivotal role in identifying irregularities and potential fraud through
anomaly detection, and provides real-world examples illustrating the effectiveness of AI in improving both data analysis
and anomaly detection on a global scale. Data analysis forms the bedrock of auditing, and AI is amplifying this foundation
by introducing advanced tools that go beyond the capabilities of traditional methods. AI's ability to process large
datasets, identify patterns, and extract meaningful insights brings a transformative dimension to data analysis in
auditing.
AI employs sophisticated algorithms for pattern recognition, allowing auditors to identify complex relationships within
datasets that may be challenging to discern through conventional analysis. This capability enables a more nuanced
understanding of financial transactions, business operations, and potential risks. AI facilitates predictive analytics,
empowering auditors to forecast trends, assess future risks, and proactively address emerging issues. This forwardlooking approach enhances the auditor's ability to provide strategic insights and recommendations to businesses. AI's
incorporation of Natural Language Processing enables auditors to analyze unstructured data, including textual
information from financial reports, contracts, and communication records (Faccia and Petratos, 2022). This linguistic
analysis enhances the comprehensiveness of data analysis, capturing valuable information embedded in textual
formats. AI tools leverage data visualization techniques, translating complex datasets into interactive visual
representations. This not only simplifies the interpretation of data but also facilitates clearer communication of audit
findings to stakeholders.
Anomaly detection is a critical aspect of auditing, and AI's capabilities in this domain significantly augment the auditor's
ability to identify irregularities, discrepancies, and potential instances of fraud. AI employs machine learning algorithms
that learn from historical data patterns. By establishing baseline behavior, these algorithms can identify deviations that
may indicate anomalies. This proactive approach to anomaly detection enables auditors to stay ahead of potential
issues. AI facilitates continuous monitoring of financial transactions and activities. Through real-time analysis, auditors
can swiftly detect unusual patterns or transactions that may be indicative of fraudulent activities, enabling timely
intervention and investigation (Patel, 2023). AI conducts behavioral analysis by assessing patterns in user interactions
and transactional behaviors. This allows auditors to identify anomalies based on deviations from expected behavioral
norms, enhancing the detection of irregularities that may otherwise go unnoticed.
The Enron scandal serves as a landmark example where AI-powered anomaly detection could have played a crucial role.
The massive financial fraud that led to Enron's collapse involved intricate manipulations of financial data. AI's advanced
analytics and anomaly detection capabilities could have detected irregularities in the financial statements, potentially
preventing the catastrophic consequences of the scandal. In the Wells Fargo scandal, where unauthorized accounts were
created to meet sales targets, AI could have been employed to analyze transactional data and detect the abnormal surge
in account openings. Anomaly detection algorithms could have flagged the irregular patterns, prompting auditors to
investigate and uncover the unethical practices sooner (Brintrup et al., 2023).
In the era of blockchain technology, AI enhances data analysis by scrutinizing complex and decentralized transactions.
Anomaly detection algorithms can identify unusual patterns or discrepancies in blockchain transactions, offering
auditors a powerful tool to ensure the integrity and transparency of financial records. Beyond financial scandals, AI has
proven effective in detecting credit card fraud. Machine learning algorithms analyze transaction patterns, user behavior,
and other data points to identify anomalous activities that may indicate fraudulent transactions, preventing financial
losses for individuals and businesses (Shi et al., 2020).
361
World Journal of Advanced Research and Reviews, 2024, 21(02), 359–370
In conclusion, the integration of AI in auditing practices globally is ushering in a new era of enhanced data analysis and
anomaly detection. The ability of AI to process vast datasets, recognize patterns, and conduct real-time monitoring
contributes to a more robust and proactive audit process. Real-world examples underscore the potential of AI to identify
irregularities and prevent fraudulent activities, showcasing its effectiveness in improving data analysis and anomaly
detection on a global scale (Kunduru, 2023). As the auditing profession continues to embrace AI technologies, the
promise of a more resilient, efficient, and insightful audit process becomes increasingly evident.
4. Risk Assessment and Management
In the realm of auditing, risk assessment is a cornerstone process that informs decision-making, safeguards financial
integrity, and ensures compliance. The integration of Artificial Intelligence (AI) is playing a pivotal role in
revolutionizing risk assessment methodologies globally. This segment explores how AI contributes to risk assessment
in auditing, its ability to identify potential risks, and the implementation of mitigating strategies through AI-driven tools
(Habbal et al., 2024). Real-world case studies further illustrate the successful application of AI in risk assessment and
management on a global scale.
AI enhances risk assessment by conducting both quantitative and qualitative analyses of vast datasets. Through machine
learning algorithms, AI processes historical data, identifies patterns, and assesses potential risks based on predefined
criteria. This analytical capability provides auditors with a comprehensive understanding of risk factors associated with
financial transactions, compliance issues, and operational processes. AI facilitates predictive analytics in risk
assessment, enabling auditors to forecast potential risks and anticipate challenges. By analyzing patterns in historical
data, AI-driven tools can identify trends that may indicate future risks, allowing auditors to implement preemptive
measures and proactive risk management strategies.
AI contributes to risk assessment by enabling auditors to conduct scenario modeling. Auditors can simulate various
scenarios and assess the potential impact of different risk factors on financial performance (Levytska et al., 2022). This
forward-looking approach helps auditors develop risk mitigation strategies tailored to specific scenarios. Through realtime data analysis, AI supports continuous monitoring of financial transactions, operational activities, and compliance
metrics. This constant vigilance allows auditors to promptly identify emerging risks, deviations from established norms,
and potential areas of concern, fostering a dynamic and adaptive risk management approach.
AI excels in identifying potential risks related to fraud through advanced anomaly detection algorithms. By analyzing
transactional patterns and user behaviors, AI-driven tools can flag irregularities indicative of fraudulent activities.
Mitigating strategies include real-time alerts, automated fraud prevention measures, and immediate intervention to halt
suspicious transactions. AI contributes to compliance risk assessment by continuously monitoring transactions and
activities for adherence to regulatory standards. Non-compliance risks are identified through AI's ability to compare
ongoing operations against established compliance frameworks (Taylor et al., 2021). Mitigating strategies involve
automated compliance checks, timely reporting, and corrective actions to address deviations.
AI analyzes market trends, economic indicators, and geopolitical factors to assess risks associated with external
environments. By identifying potential economic downturns, market volatility, or geopolitical instability, auditors can
develop strategies to mitigate these risks. AI-driven tools provide timely insights that inform risk management decisions
in a rapidly changing global landscape. AI assists in identifying operational risks by analyzing data related to internal
processes, supply chain management, and organizational efficiency (Shah et al., 2023). Mitigating strategies involve
process optimization, automated monitoring of operational activities, and the implementation of contingency plans to
address potential disruptions.
HSBC implemented AI-driven tools to enhance risk assessment in its anti-money laundering (AML) efforts. AI algorithms
analyze transactions, detect suspicious patterns, and flag potential money laundering activities. This proactive approach
has strengthened HSBC's risk management capabilities and regulatory compliance. JPMorgan Chase utilizes AI for credit
risk assessment. AI algorithms analyze customer credit profiles, transaction histories, and market trends to assess
creditworthiness (Odinet, 2021). This has resulted in more accurate risk evaluations, reduced credit losses, and
enhanced decision-making in extending credit facilities. Walmart employs AI for supply chain risk management. AIdriven tools analyze factors such as supplier performance, market conditions, and logistics data to identify potential
risks in the supply chain. Walmart's use of AI has improved inventory management, reduced supply chain disruptions,
and enhanced overall operational resilience. Maersk, a global shipping company, leverages AI for cybersecurity risk
assessment. AI tools analyze network activities, identify potential cyber threats, and implement preventive measures to
protect against cyber-attacks. Maersk's proactive approach to cybersecurity risk management has fortified its resilience
against digital threats.
362
World Journal of Advanced Research and Reviews, 2024, 21(02), 359–370
In conclusion, the integration of AI in auditing practices globally is reshaping risk assessment and management
methodologies. AI's analytical prowess, predictive capabilities, and continuous monitoring contribute to a more
dynamic and proactive approach to risk identification and mitigation. Real-world case studies exemplify how
organizations across different industries have successfully harnessed AI to strengthen their risk assessment processes,
leading to more robust risk management strategies (Nassar and Kamal, 2021). As the auditing profession continues to
embrace AI, the collaboration between human expertise and AI-driven tools promises a future where risk assessment
becomes not only more efficient but also more anticipatory and strategic in a rapidly evolving global landscape.
5. Real-Time Auditing and Dynamic Insights
In the fast-paced and ever-evolving business landscape, the integration of Artificial Intelligence (AI) is revolutionizing
auditing practices by enabling real-time auditing processes (Gautam, 2023). This segment explores the overview of AI's
role in facilitating real-time auditing, the dynamic insights it provides for enhanced decision-making during audits, and
provides illustrative examples of organizations globally benefiting from the adoption of real-time auditing through AI.
AI facilitates real-time auditing by continuously monitoring vast datasets, financial transactions, and operational
activities. Unlike traditional auditing methods that rely on periodic reviews, AI-driven tools provide instant access to
up-to-the-minute data, ensuring auditors have a comprehensive and current understanding of an organization's
financial health and compliance status.
AI's automation capabilities streamline data processing tasks, allowing for real-time analysis of financial records,
transactions, and other critical information. This automated approach significantly reduces the time required for data
compilation and processing, enabling auditors to focus on high-value analytical tasks rather than manual data entry.
AI's advanced anomaly detection algorithms operate in real-time, flagging irregularities, discrepancies, or potential
fraudulent activities as they occur (Faccia, 2023). This immediate identification of anomalies enhances auditors' ability
to respond swiftly to emerging risks, reducing the window of exposure to potential financial irregularities. AI seamlessly
integrates with emerging technologies such as blockchain, IoT (Internet of Things), and cloud computing, enabling
auditors to perform real-time audits on decentralized and interconnected systems. This adaptability ensures that
auditing practices stay relevant and effective in the face of technological advancements (Ohenhen et al., 2024).
AI's predictive analytics provide auditors with dynamic insights into potential risks. By analyzing historical data and
identifying patterns, AI can predict future risks, allowing auditors to implement preemptive measures and proactive
risk mitigation strategies. This forward-looking approach enhances decision-making during audits. AI provides dynamic
insights into an organization's financial performance in real-time. Auditors can monitor key financial indicators,
revenue streams, and expense patterns as they unfold. This immediate access to financial data empowers auditors to
make informed decisions and recommendations based on the most current information. AI enables auditors to conduct
dynamic scenario modeling and sensitivity analysis in real-time. This capability allows auditors to simulate various
scenarios and assess the potential impact of different factors on financial outcomes (Babatunde et al., 2021). Dynamic
insights derived from scenario modeling contribute to more informed decision-making during audits. Compliance
requirements are constantly evolving, and AI's adaptive compliance monitoring provides dynamic insights into an
organization's adherence to changing regulations. Auditors can assess compliance in real-time, identify potential gaps,
and recommend adjustments to ensure ongoing conformity with regulatory standards.
IBM utilizes AI-powered real-time auditing to monitor its global financial transactions and identify potential anomalies
instantly. By leveraging AI algorithms, IBM ensures compliance with financial regulations, mitigates fraud risks, and
receives dynamic insights into its financial performance on a global scale. Deloitte, a global audit firm, employs AI for
real-time auditing processes to enhance the efficiency and effectiveness of its audits. By integrating AI-driven tools into
its audit procedures, Deloitte gains dynamic insights into client financial data, allowing auditors to provide more timely
and actionable recommendations (Lukong et al., 2022).
Amazon utilizes real-time auditing with AI to monitor its vast e-commerce transactions. AI algorithms continuously
analyze transactional data, identify potential fraud, and provide dynamic insights into sales performance. This real-time
auditing approach contributes to Amazon's ability to adapt quickly to changing market conditions (Kunene et al., 2022).
Bank of America incorporates AI for real-time auditing to ensure the integrity and security of its financial transactions.
AI-driven tools provide dynamic insights into transactional patterns, enabling the bank to detect and respond to
potential risks promptly (Sheth et al., 2022). This real-time approach enhances the bank's overall risk management
strategies.
In conclusion, the adoption of real-time auditing processes enabled by AI is transforming how audits are conducted
globally. The ability to access dynamic insights, analyze data in real-time, and make informed decisions during audits
363
World Journal of Advanced Research and Reviews, 2024, 21(02), 359–370
contributes to a more agile and responsive auditing practice. Illustrative examples from organizations across various
industries underscore the tangible benefits of real-time auditing through AI. As the auditing profession continues to
embrace the capabilities of AI, the trend towards real-time, data-driven audits is poised to redefine the standards of
accuracy, efficiency, and adaptability in auditing practices on a global scale (Hasan, 2021).
6. Challenges and Ethical Considerations
The integration of Artificial Intelligence (AI) in transforming auditing practices globally brings about unprecedented
opportunities, but it also introduces a set of challenges and ethical considerations. This segment explores the challenges
associated with the adoption of AI in auditing, engages in a discussion on ethical considerations, and emphasizes the
need for responsible AI use. Striking a balance between technological advancements and maintaining audit quality and
integrity is crucial for the ethical evolution of auditing practices on a global scale. The success of AI in auditing relies
heavily on the quality and integrity of the data it processes. Inaccurate, incomplete, or biased data can lead to flawed
analyses and misinformed audit conclusions. Ensuring the accuracy and reliability of data inputs poses a significant
challenge for auditors adopting AI (Almufadda and Almezeini, 2022).
Many organizations operate with legacy systems that may not seamlessly integrate with AI technologies. Ensuring the
interoperability of AI tools with existing infrastructure presents a technical challenge. The transition to AI-powered
auditing may require substantial investments in system upgrades and integration efforts. The absence of standardized
practices and guidelines for AI in auditing creates challenges in terms of consistency and comparability. Auditors face
the task of navigating diverse AI tools and methodologies, making it challenging to establish uniform standards across
the auditing profession.
The successful adoption of AI in auditing necessitates a workforce equipped with the skills to operate and interpret AIdriven tools. A shortage of skilled professionals and the need for ongoing training programs pose challenges for
organizations seeking to leverage AI effectively in their auditing practices. AI-driven auditing processes often involve
the handling of sensitive financial and operational data. Ensuring the security and confidentiality of this data becomes
a paramount concern. Protecting against cyber threats, data breaches, and unauthorized access requires robust
cybersecurity measures.
AI algorithms can inherit biases present in the data used for training. In auditing, this raises concerns about the fairness
of AI-driven analyses, especially if historical biases in financial data are perpetuated (Chen et al., 2023). Auditors must
be vigilant in addressing and mitigating biases to ensure fair and equitable audit outcomes. AI models, particularly
complex machine learning algorithms, are often considered "black boxes" that lack transparency in their decisionmaking processes. In auditing, the need for transparency and explainability is critical to justify audit conclusions. Ethical
considerations call for auditors to ensure that AI-driven insights are understandable and interpretable (Zhang et al.,
2022).
The use of AI in auditing involves the processing of large volumes of financial and operational data. Privacy concerns
arise when sensitive information is analyzed without proper safeguards. Ethical considerations dictate the need for
auditors to uphold privacy standards, adhere to regulations, and obtain consent when handling personal data. The
ethical integration of AI in auditing involves establishing a harmonious collaboration between AI tools and human
auditors. Striking the right balance between AI-driven automation and human judgment is crucial to ensure that ethical
considerations, contextual understanding, and professional skepticism are not compromised (Odeleye and Adeigbe,
2018). Determining accountability and liability in cases of errors or misjudgments made by AI-driven tools poses ethical
challenges. Auditors must address questions of responsibility, especially when AI systems contribute to audit decisions.
Clarity on accountability and liability frameworks is essential for responsible AI use in auditing.
Maintaining audit quality and integrity requires implementing robust quality control measures for AI-driven processes.
Auditors must validate the accuracy and reliability of AI outputs, conduct thorough reviews, and establish validation
protocols to ensure the integrity of audit findings. Professional auditing bodies and organizations must develop and
adhere to comprehensive ethical guidelines and codes of conduct for the use of AI (Olushola et al., 2017). Establishing
clear ethical frameworks helps auditors navigate the challenges associated with responsible AI adoption and ensures
alignment with ethical standards.
To balance technological advancements, auditors must implement continuous monitoring and auditing of AI processes.
Regular assessments of AI tools, ongoing training for auditors, and adapting to evolving ethical standards are essential
for maintaining the ethical integrity of AI-driven auditing practices. Ethical considerations involve engaging with
stakeholders transparently. Auditors must communicate with clients, regulators, and the public about the use of AI in
364
World Journal of Advanced Research and Reviews, 2024, 21(02), 359–370
auditing, addressing concerns, and ensuring that stakeholders are informed about the ethical principles guiding AI
adoption (Jauhiainen and Lehner, 2022).
7. Case Studies and Implementations
The global integration of Artificial Intelligence (AI) in auditing practices has witnessed successful implementations
across diverse industries and jurisdictions. This segment provides a survey of real-world case studies, showcasing the
impactful applications of AI in auditing. It offers diverse examples from different industries and global jurisdictions,
highlighting lessons learned and best practices derived from successful AI implementations in auditing. KPMG
implemented AI-driven cognitive automation to enhance tax compliance auditing processes. The AI tool analyzed large
volumes of financial data, identified tax compliance risks, and provided real-time insights to tax auditors (Strauss et al.,
2020). The implementation resulted in increased efficiency, improved accuracy, and a reduction in the time required
for tax compliance audits.
PricewaterhouseCoopers (PwC) utilized machine learning algorithms for fraud detection in financial audits. The AI tool
analyzed transactional patterns, identified anomalies indicative of fraudulent activities, and provided auditors with
actionable insights. PwC reported enhanced fraud detection capabilities, contributing to a more robust auditing process.
Ernst & Young (EY) incorporated Natural Language Processing (NLP) in financial statement analysis. The AI tool
processed textual information from financial reports, contracts, and regulatory filings, extracting relevant data for
auditors (Leitner-Hanetseder and Lehner, 2022). This application of NLP improved the efficiency of financial statement
analysis, allowing auditors to focus on more complex interpretations. Deloitte leveraged blockchain technology for
supply chain auditing. The transparent and decentralized nature of blockchain facilitated real-time tracking of supply
chain transactions, ensuring the integrity of data throughout the supply chain. This implementation provided auditors
with a tamper-resistant ledger, reducing the risk of fraudulent activities in the supply chain.
Citibank implemented AI in internal audits to enhance risk assessment and compliance monitoring. The AI system
analyzed transactional data, identified patterns indicative of risks, and provided dynamic insights for auditors. This
application spanned multiple jurisdictions where Citibank operates, showcasing the adaptability of AI in addressing
global audit challenges. Mayo Clinic employed AI for billing audits, analyzing medical billing records for accuracy and
compliance. The AI tool identified billing errors, flagged potential compliance issues, and provided real-time feedback
to billing auditors (Munoko et al., 2020). This implementation showcased the applicability of AI in the healthcare sector,
ensuring financial integrity and compliance with billing regulations.
Toyota integrated AI in quality audits across its global manufacturing facilities. AI algorithms analyzed production data,
identified deviations in quality metrics, and facilitated proactive quality control measures. This implementation
demonstrated the use of AI in ensuring product quality and compliance with manufacturing standards on a global scale.
Microsoft utilized AI for software license audits, analyzing usage patterns and license agreements. The AI system
identified instances of non-compliance, provided detailed usage reports, and facilitated license optimization. This
implementation showcased the effectiveness of AI in auditing complex software licensing agreements in the technology
industry.
Successful implementations underscore the importance of aligning AI applications with specific audit objectives.
Tailoring AI tools to address the unique requirements of each audit, whether in tax compliance, fraud detection, or
supply chain auditing, enhances the effectiveness of the technology. Lessons learned emphasize the need for continuous
training and skill development for auditors to effectively leverage AI. Ensuring that auditors possess the necessary skills
to operate, interpret, and validate AI-driven tools is crucial for successful implementation. Best practices emphasize
transparent communication with stakeholders about the use of AI in auditing. Clear communication fosters trust and
helps stakeholders understand the benefits, limitations, and ethical considerations associated with AI-driven audit
processes (Birkstedt et al., 2023).
Implementing regular audits of AI processes is a best practice derived from successful cases. Auditing the AI itself
ensures that the technology remains accurate, unbiased, and aligned with ethical standards. Regular assessments
contribute to ongoing improvement and adaptability. Lessons learned highlight the significance of fostering
collaboration between human auditors and AI-driven tools. Combining human judgment, ethical considerations, and
professional skepticism with the analytical capabilities of AI results in a symbiotic relationship that enhances audit
quality. Successful implementations stress the need for customizing AI applications to address industry-specific
challenges (Jan et al., 2023). Recognizing the unique characteristics and regulatory requirements of each industry
ensures that AI is tailored to deliver maximum value in diverse audit contexts.
365
World Journal of Advanced Research and Reviews, 2024, 21(02), 359–370
In conclusion, the global implementation of AI in auditing practices has yielded successful outcomes across diverse
industries and jurisdictions. Real-world case studies showcase the versatility of AI applications in addressing specific
audit challenges, from tax compliance and fraud detection to supply chain auditing. Lessons learned emphasize the
importance of aligning AI with audit objectives, continuous training for auditors, transparent communication with
stakeholders, regular audits of AI processes, and collaboration between humans and AI (Seethamraju and Hecimovic,
2023). These insights contribute to the evolving best practices for responsible and effective AI adoption in auditing
practices on a global scale.
8. Global Significance and Future Implications
The integration of Artificial Intelligence (AI) in auditing practices has profound global significance, reshaping traditional
audit methodologies and paving the way for a more efficient, accurate, and adaptive audit landscape. This segment
reflects on the global impact of AI on auditing practices, considers the evolving role of auditors in the context of AI
adoption, and calls for a collaborative approach among industry stakeholders, auditors, and regulators for responsible
AI integration in auditing.
AI's global impact on auditing practices is characterized by enhanced efficiency and accuracy. AI-driven tools automate
routine tasks, allowing auditors to focus on complex analyses. The speed and precision with which AI processes vast
datasets contribute to quicker audit cycles and more accurate insights, transcending geographical boundaries. The
adoption of AI in auditing fosters global standardization and consistency. As AI tools adhere to predefined algorithms
and standards, audit practices become more uniform across jurisdictions. This standardization is particularly beneficial
for multinational corporations, ensuring a consistent approach to audits irrespective of the geographical diversity of
operations (Downey and Westermann, 2021).
The dynamic nature of AI enables auditors to adapt swiftly to evolving regulatory changes globally. AI-driven tools can
be updated to incorporate new regulations, ensuring compliance and reducing the lag time traditionally associated with
manual adjustments. This adaptability contributes to a more responsive and proactive approach to regulatory
compliance. AI's data analysis capabilities enable auditors to identify and mitigate risks more effectively on a global
scale. The real-time monitoring of financial transactions, anomaly detection, and predictive analytics contribute to a
proactive risk management approach (Aljohani, 2023). This is especially significant in industries with intricate global
supply chains and financial networks. AI facilitates cross-border collaboration among auditors and organizations.
Cloud-based AI applications enable auditors to access and analyze data from various geographical locations (Ageed et
al., 2021). This collaborative approach ensures that auditors can work seamlessly across borders, sharing insights and
collectively addressing global audit challenges.
The adoption of AI transforms auditors into strategic decision-makers. Rather than focusing solely on manual data entry
and verification, auditors can leverage AI-driven insights to make informed decisions. The evolving role positions
auditors as critical contributors to strategic planning, risk mitigation, and overall business success. Auditors in the age
of AI become proficient data analysts and interpretation experts. The ability to interpret AI-generated insights,
understand the nuances of complex algorithms, and apply human judgment to contextualize findings becomes integral
to the auditor's role (Fu and Stasko, 2023). This evolution aligns auditors with the demands of a data-driven business
environment.
The evolving role of auditors requires a commitment to continuous learning and adaptation. As AI technologies advance,
auditors must stay abreast of the latest developments, refining their skills to effectively operate and interpret AI-driven
tools. This commitment to ongoing learning ensures auditors remain at the forefront of technological advancements.
Auditors become ethical guardians of AI integration in the auditing process (Light and Panai, 2022). With the power of
AI comes ethical considerations, including bias mitigation, transparency, and responsible AI use. Auditors play a pivotal
role in ensuring that AI-driven tools align with ethical standards, promoting fairness, accountability, and integrity in
audits.
A collaborative approach among industry stakeholders is essential for responsible AI integration in auditing (Rakova et
al., 2021). Collaboration fosters the sharing of best practices, insights, and lessons learned. Industry forums,
consortiums, and collaborative initiatives enable stakeholders to collectively address challenges, ensuring a harmonized
approach to AI adoption. Partnerships between auditors and regulators are crucial for setting guidelines and standards
for AI integration. Regulators can work collaboratively with auditors to establish ethical frameworks, compliance
requirements, and regulatory standards specific to AI-driven auditing (de Laat, 2021). This partnership ensures that AI
adoption aligns with regulatory expectations. A collaborative approach involves knowledge sharing and the
development of training programs. Industry associations, academic institutions, and professional bodies can facilitate
366
World Journal of Advanced Research and Reviews, 2024, 21(02), 359–370
knowledge exchange platforms and training initiatives. These programs equip auditors with the skills and knowledge
needed to navigate the complexities of AI integration responsibly.
Transparency and open dialogue between auditors, industry stakeholders, and regulators are paramount. Open
discussions about the challenges, risks, and ethical considerations associated with AI adoption foster trust and
understanding. Transparent communication ensures that all stakeholders are informed and actively engaged in shaping
responsible AI practices. Global standardization initiatives play a pivotal role in ensuring a consistent and responsible
approach to AI integration in auditing. Collaborative efforts to establish international standards for AI-driven auditing
practices contribute to a cohesive global framework (Badawy, 2023). These standards can address ethical
considerations, data privacy, and overall audit quality.
In conclusion, the global significance of AI in transforming auditing practices is profound, offering enhanced efficiency,
standardization, and adaptability on a global scale. The evolving role of auditors positions them as strategic decisionmakers and ethical guardians of AI integration. A collaborative approach among industry stakeholders, auditors, and
regulators is crucial for ensuring responsible AI adoption in auditing practices. As AI continues to reshape the auditing
landscape, fostering collaboration and embracing ethical considerations will be instrumental in harnessing the full
potential of AI for the benefit of the auditing profession and the global business community (Losbichler and Lehner,
2022).
9. Conclusion
The transformative impact of Artificial Intelligence (AI) on auditing practices has been examined through a global lens,
revealing key insights that underscore the profound changes and opportunities reshaping the audit landscape
worldwide. This conclusion recaps the key insights from the global perspective review, emphasizes the transformative
potential of AI in shaping the future of auditing practices globally, and issues a call to action for embracing AI responsibly
to ensure the continued trustworthiness and effectiveness of auditing practices on a global scale. The integration of AI
in auditing practices has consistently demonstrated a marked increase in efficiency and accuracy. AI-driven tools
automate routine tasks, allowing auditors to focus on more complex analyses, resulting in quicker audit cycles and more
precise insights. The adoption of AI contributes to global standardization and consistency in auditing practices.
Standardized algorithms and methodologies ensure a uniform approach across jurisdictions, benefiting multinational
corporations and streamlining audit processes on a global scale. Auditors are evolving into strategic decision-makers,
data analysts, and ethical guardians of AI integration. The new role emphasizes continuous learning, adaptation to
technological advancements, and a commitment to upholding ethical standards in the era of AI-driven auditing.
Collaboration among industry stakeholders, auditors, and regulators emerges as a critical factor for responsible AI
integration. Open dialogue, knowledge sharing, partnerships, and global standardization initiatives are essential
components of a collaborative approach to harnessing the benefits of AI in auditing.
The transformative potential of AI in shaping the future of auditing practices worldwide cannot be overstated. As AI
continues to advance, auditors will play an increasingly strategic role, leveraging AI-driven insights for more informed
decision-making. The global standardization facilitated by AI will lead to a harmonized approach to audits, ensuring
consistency and reliability in financial reporting across diverse industries and jurisdictions. AI's ability to enhance risk
management, provide real-time auditing, and adapt to regulatory changes positions it as a catalyst for innovation in the
auditing profession. The efficiency gains, coupled with the evolving role of auditors, promise a future where audits are
not only more accurate but also contribute to strategic business objectives, fostering growth and sustainability on a
global scale.
Embracing AI responsibly is imperative to ensure the continued trustworthiness and effectiveness of auditing practices
globally. As AI becomes an integral part of the auditing toolkit, auditors, industry stakeholders, and regulators must
collaborate to establish and adhere to ethical frameworks, transparent communication standards, and responsible AI
practices. Auditors must prioritize ethical considerations, addressing issues such as bias mitigation, transparency, and
privacy. Responsible AI adoption requires a commitment to upholding ethical standards and ensuring that AI-driven
tools align with professional and societal values. To navigate the evolving landscape of AI-driven auditing, auditors need
to engage in continuous learning and skill development. Training programs and knowledge-sharing initiatives will
empower auditors to leverage AI effectively while maintaining their roles as critical thinkers and interpreters of AIgenerated insights.
Transparent communication about the integration of AI in auditing practices is essential. Stakeholders, including clients,
regulators, and the public, should be informed about the benefits, limitations, and ethical considerations associated with
AI adoption. Transparency fosters trust and understanding. The global community of auditors, industry leaders, and
367
World Journal of Advanced Research and Reviews, 2024, 21(02), 359–370
regulators should engage in collaborative efforts to establish international standards for AI-driven auditing practices.
Sharing best practices, addressing challenges, and developing standardized approaches will contribute to a cohesive
global framework for responsible AI adoption. In conclusion, the global perspective review of AI's role in transforming
auditing practices underscores the potential for positive change and innovation. Embracing AI responsibly is not just a
necessity but an opportunity to redefine the future of auditing practices, ensuring they remain trustworthy, adaptive,
and aligned with the evolving needs of the global business environment. As the auditing profession embraces the
transformative potential of AI, it stands poised to lead the way in shaping a more efficient, accurate, and resilient
financial reporting landscape worldwide.
Compliance with ethical standards
Disclosure of conflict of interest
No conflict of interest to be disclosed.
References
[1]
Ageed, Z.S., Zeebaree, S.R., Sadeeq, M.M., Kak, S.F., Yahia, H.S., Mahmood, M.R. and Ibrahim, I.M., 2021.
Comprehensive survey of big data mining approaches in cloud systems. Qubahan Academic Journal, 1(2), pp.2938.
[2]
Aljohani, A., 2023. Predictive analytics and machine learning for real-time supply chain risk mitigation and agility.
Sustainability, 15(20), p.15088.
[3]
Almufadda, G. and Almezeini, N.A., 2022. Artificial Intelligence Applications in the Auditing Profession: A
Literature Review. Journal of Emerging Technologies in Accounting, 19(2), pp.29-42.
[4]
Arafa, A., Sheerah, H.A. and Alsalamah, S., 2023. Emerging Digital Technologies in Healthcare with a Spotlight on
Cybersecurity: A Narrative Review. Information, 14(12), p.640.
[5]
Babatunde, F.O., Omotayo, A.B., Oluwole, O.I. and Ukoba, K., 2021, April. A Review on Waste-wood Reinforced
Polymer Matrix Composites for Sustainable Development. In IOP Conference Series: Materials Science and
Engineering (Vol. 1107, No. 1, p. 012057). IOP Publishing.
[6]
Badawy, W., 2023. Data-driven framework for evaluating digitization and artificial intelligence risk: a
comprehensive analysis. AI and Ethics, pp.1-26.
[7]
Birkstedt, T., Minkkinen, M., Tandon, A. and Mäntymäki, M., 2023. AI governance: themes, knowledge gaps and
future agendas. Internet Research, 33(7), pp.133-167.
[8]
Brintrup, A., Kosasih, E., Schaffer, P., Zheng, G., Demirel, G. and MacCarthy, B.L., 2023. Digital supply chain
surveillance using artificial intelligence: definitions, opportunities and risks. International Journal of Production
Research, pp.1-22.
[9]
Chen, P., Wu, L. and Wang, L., 2023. AI Fairness in Data Management and Analytics: A Review on Challenges,
Methodologies and Applications. Applied Sciences, 13(18), p.10258.
[10]
de Laat, P.B., 2021. Companies committed to responsible AI: From principles towards implementation and
regulation?. Philosophy & technology, 34, pp.1135-1193.
[11]
Downey, D.H. and Westermann, K.D., 2021. Challenging global group audits: The perspective of US group audit
leads. Contemporary Accounting Research, 38(2), pp.1395-1433.
[12]
Faccia, A. and Petratos, P., 2022, December. NLP And IR Applications For Financial Reporting And Non-Financial
Disclosure. Framework Implementation And Roadmap For Feasible Integration With The Accounting Process. In
Proceedings of the 2022 6th International Conference on Natural Language Processing and Information Retrieval
(pp. 117-124).
[13]
Faccia, A., 2023. National Payment Switches and the Power of Cognitive Computing against Fintech Fraud. Big
Data and Cognitive Computing, 7(2), p.76.
[14]
Falco, G., Shneiderman, B., Badger, J., Carrier, R., Dahbura, A., Danks, D., Eling, M., Goodloe, A., Gupta, J., Hart, C.
and Jirotka, M., 2021. Governing AI safety through independent audits. Nature Machine Intelligence, 3(7), pp.566571.
368
World Journal of Advanced Research and Reviews, 2024, 21(02), 359–370
[15]
Fedyk, A., Hodson, J., Khimich, N. and Fedyk, T., 2022. Is artificial intelligence improving the audit process?. Review
of Accounting Studies, 27(3), pp.938-985.
[16]
Fu, Y. and Stasko, J., 2023. More Than Data Stories: Broadening the Role of Visualization in Contemporary
Journalism. IEEE Transactions on Visualization and Computer Graphics.
[17]
Gautam, A., 2023. The evaluating the impact of artificial intelligence on risk management and fraud detection in
the banking sector. AI, IoT and the Fourth Industrial Revolution Review, 13(11), pp.9-18.
[18]
Habbal, A., Ali, M.K. and Abuzaraida, M.A., 2024. Artificial Intelligence Trust, Risk and Security Management (AI
TRiSM): Frameworks, applications, challenges and future research directions. Expert Systems with Applications,
240, p.122442.
[19]
Hamdan, S.A.R. and Al Habashneh, A.K., 2024. The Advantages and Difficulties of Using AI and BT in the Auditing
Procedures: A Literature Review. Artificial Intelligence-Augmented Digital Twins: Transforming Industrial
Operations for Innovation and Sustainability, pp.111-126.
[20]
Hasan, A.R., 2021. Artificial Intelligence (AI) in accounting & auditing: A Literature review. Open Journal of
Business and Management, 10(1), pp.440-465.
[21]
Igou, A., Power, D.J., Brosnan, S. and Heavin, C., 2023. Digital Futures for Accountants. Journal of Emerging
Technologies in Accounting, 20(1), pp.39-57.
[22]
Jan, Z., Ahamed, F., Mayer, W., Patel, N., Grossmann, G., Stumptner, M. and Kuusk, A., 2023. Artificial intelligence
for industry 4.0: Systematic review of applications, challenges, and opportunities. Expert Systems with
Applications, 216, p.119456.
[23]
Jauhiainen, T. and Lehner, O.M., 2022. Good Governance of AI and Big Data Processes in Accounting and Auditing.
In Artificial Intelligence in Accounting (pp. 119-181). Routledge.
[24]
Kommunuri, J., 2022. Artificial intelligence and the changing landscape of accounting: a viewpoint. Pacific
Accounting Review, 34(4), pp.585-594.
[25]
Kunduru, A.R., 2023. Artificial intelligence advantages in cloud Fintech application security. Central Asian Journal
of Mathematical Theory and Computer Sciences, 4(8), pp.48-53.
[26]
Kunene, T.J., Tartibu, L.K., Karimzadeh, S., Oviroh, P.O., Ukoba, K. and Jen, T.C., 2022. Molecular Dynamics of
Atomic Layer Deposition: Sticking Coefficient Investigation. Applied sciences, 12(4), p.2188.
[27]
Leitner-Hanetseder, S. and Lehner, O.M., 2022. AI-powered information and Big Data: current regulations and
ways forward in IFRS reporting. Journal of Applied Accounting Research, 24(2), pp.282-298.
[28]
Levytska, S., Pershko, L., Akimova, L., Akimov, O., Havrilenko, K. and Kucherovskii, O., 2022. A risk-oriented
approach in the system of internal auditing of the subjects of financial monitoring. International Journal of Applied
Economics, Finance and Accounting, 14(2), pp.194-206.
[29]
Light, R. and Panai, E., 2022. The Self-Synchronisation of AI Ethical Principles. Digital Society, 1(3), p.24.
[30]
Losbichler, H. and Lehner, O.M., 2022. Cybernetic Limits of Artificial Intelligence in Accounting and a Future
Research Agenda. Artificial Intelligence in Accounting: Organisational and Ethical Implications.
[31]
Lukong, V.T., Ukoba, K., Yoro, K.O. and Jen, T.C., 2022. Annealing temperature variation and its influence on the
self-cleaning properties of TiO2 thin films. Heliyon, 8(5).
[32]
Munoko, I., Brown-Liburd, H.L. and Vasarhelyi, M., 2020. The ethical implications of using artificial intelligence in
auditing. Journal of Business Ethics, 167, pp.209-234.
[33]
Nassar, A. and Kamal, M., 2021. Machine Learning and Big Data analytics for Cybersecurity Threat Detection: A
Holistic review of techniques and case studies. Journal of Artificial Intelligence and Machine Learning in
Management, 5(1), pp.51-63.
[34]
Odeleye, D.A. and Adeigbe, Y.K. eds., 2018. Girl-child Education and Women Empowerment for Sustainable
Development: A Book of Readings: in Honour of Dr Mrs Oyebola Ayeni. College Press & Publishers, Lead City
University.
[35]
Odinet, C.K., 2021. Fintech Credit and the Financial Risk of AI. Cambridge Handbook of AI and Law (Kristin
Johnson & Carla Reyes eds., forthcoming 2022)., U Iowa Legal Studies Research Paper, (2021-39).
369
World Journal of Advanced Research and Reviews, 2024, 21(02), 359–370
[36]
Ohenhen, P.E., Chidolue, O., Umoh, A.A., Ngozichukwu, B., Fafure, A.V., Ilojianya, V.I. and Ibekwe, K.I., 2024.
Sustainable cooling solutions for electronics: A comprehensive review: Investigating the latest techniques and
materials, their effectiveness in mechanical applications, and associated environmental benefits.
[37]
Olushola, A.O., 2017. Sexting in educational sector: gender perspective in some selected secondary schools in
ekiti and osun states. IFE PsychologIA: An International Journal, 25(2), pp.245-261.
[38]
Patel, K., 2023. Credit Card Analytics: A Review of Fraud Detection and Risk Assessment Techniques.
International Journal of Computer Trends and Technology, 71(10), pp.69-79.
[39]
Peng, Y., Ahmad, S.F., Ahmad, A.Y.B., Al Shaikh, M.S., Daoud, M.K. and Alhamdi, F.M.H., 2023. Riding the waves of
artificial intelligence in advancing accounting and its implications for sustainable development goals.
Sustainability, 15(19), p.14165.
[40]
Rakova, B., Yang, J., Cramer, H. and Chowdhury, R., 2021. Where responsible AI meets reality: Practitioner
perspectives on enablers for shifting organizational practices. Proceedings of the ACM on Human-Computer
Interaction, 5(CSCW1), pp.1-23.
[41]
Seethamraju, R. and Hecimovic, A., 2023. Adoption of artificial intelligence in auditing: An exploratory study.
Australian Journal of Management, 48(4), pp.780-800.
[42]
Shah, H.M., Gardas, B.B., Narwane, V.S. and Mehta, H.S., 2023. The contemporary state of big data analytics and
artificial intelligence towards intelligent supply chain risk management: a comprehensive review. Kybernetes,
52(5), pp.1643-1697.
[43]
Sheth, J.N., Jain, V., Roy, G. and Chakraborty, A., 2022. AI-driven banking services: the next frontier for a
personalised experience in the emerging market. International Journal of Bank Marketing, 40(6), pp.1248-1271.
[44]
Shi, Y., Liu, Y., Tong, H., He, J., Yan, G. and Cao, N., 2020. Visual analytics of anomalous user behaviors: A survey.
IEEE Transactions on Big Data, 8(2), pp.377-396.
[45]
Spring, M., Faulconbridge, J. and Sarwar, A., 2022. How information technology automates and augments
processes: Insights from Artificial‐Intelligence‐based systems in professional service operations. Journal of
Operations Management, 68(6-7), pp.592-618.
[46]
Strauss, H., Fawcett, T. and Schutte, D., 2020. Tax risk assessment and assurance reform in response to the
digitalised economy. Journal of Telecommunications and the Digital Economy, 8(4), pp.96-126.
[47]
Taylor, S., Surridge, M. and Pickering, B., 2021, May. Regulatory compliance modelling using risk management
techniques. In 2021 IEEE World AI IoT Congress (AIIoT) (pp. 0474-0481). IEEE.
[48]
Tiron-Tudor, A. and Deliu, D., 2022. Reflections on the human-algorithm complex duality perspectives in the
auditing process. Qualitative Research in Accounting & Management, 19(3), pp.255-285.
[49]
Tyagi, A.K., Aswathy, S.U. and Abraham, A., 2020. Integrating blockchain technology and artificial intelligence:
Synergies perspectives challenges and research directions. Journal of Information Assurance and Security, 15(5),
p.1554.
[50]
Zhang, C.A., Cho, S. and Vasarhelyi, M., 2022. Explainable artificial intelligence (xai) in auditing. International
Journal of Accounting Information Systems, 46, p.100572
370