Artificial Intelligence (AI) and Machine Learning (ML) in Auditing

As technology changes over time, it has impacted the practice of auditing in many ways, and one of the critical changes that have occurred is the adoption of artificial intelligence (AI) and machine learning (ML). These technologies are transforming the face of audits and presenting new ideas on how to carry out the audits. According to Charles (2019), internal audit functions can increase their effectiveness by using these tools to improve operations and compliance's overall effectiveness and efficiency. This article will explain how AI and ML are being adopted in auditing and what changes these developments carry for the future of auditing.

AI and ML in Automating Audit Tasks

AI and ML have the potential to revolutionize auditing because numerous uncomplicated and repetitive processes can be handled through the technology. In the past, auditors have expended much of their efforts in data entry, document analysis, and checks on transactions. Charles (2019) also emphasizes that applying AI technologies and tools in auditing would help automate such tasks so that auditors can concentrate on using their professional judgment. For this reason, by leveraging AI in the documentation analysis, comparing different data pieces, and identifying possible risks, auditors' work will become more effective.

For example, AI systems incorporated with Natural Language Processing (NLP) can analyze the contracts, pinpoint specific clauses, and suggest further review of such clauses for possible problems. This automation reduces time in the audit process and eliminates the chances of making other human mistakes or missing some essential things (Hasan, 2021). In addition, such approaches imply constant monitoring, with opportunities for real-time detection of abnormalities and opportunities for auditors to address potential issues.

This automation also decreases the burden on auditors, who can now dedicate more time to strategic aspects of audits, including assessment of risk management models and offering advisory services (Akpan, 2024). By reducing manual and repetitive procedures, the auditing process can be made much more efficient, and at the same time, the results can be more accurate.

Enhancing Risk Assessment with AI and ML

Risk assessment is a vital process in auditing, and AI and ML can assist auditors in identifying more risks with enhanced precision and speed. Charles (2019) highlights the application of sophisticated algorithms capable of processing a significant amount of data to trace new risks. Risk assessments given by artificial intelligence afford organizations better ways to manage new threats and enhance general risk management paradigms. Through these technologies, auditors will realize that the information they use for their assessments is current.

For instance, AI algorithms can help identify suspicious patterns in people's spending that suggest fraud, money laundering, or other forms of embezzlement. Different risk assessment threats may include trend risks forecasted through previous data fed to machine learning models, wherein the subsequent dangers include exposure to particular market conditions or failure to meet regulatory requirements (Ganapathy, 2023). This predictive potential allows auditors to give forward-looking insights, thus making enterprises less reactive to issues.

Using AI and ML in risk assessment through progressive learning is also an added benefit. The more data they process, the better they get, so in the long run, they can detect risks at a faster rate (Akpan, 2024). Real-time analysis also enhances the auditor's ability to observe the risk setting within an organization and guarantee that emerging problems are dealt with before assuming major adversarial concerns.

Ethical Implications of AI and ML in Auditing

AI and ML are beneficial in auditing since they offer various benefits; nevertheless, using them has drawbacks, including ethical dilemmas. Ganapathy (2023) stresses the need to ensure that the right people refrain from using technology. Auditors must ensure that the AI tools' processes and output are understandable. This implies explaining the thought process behind the AI models used to make and defend these conclusions to the stakeholders.

Another significant issue is data privacy, as it becomes crucial when AI systems handle financial data. This means that auditors must check that the AI utilized respects data security laws and regulations and that any data used in the audit processes will do so. Also, the specific algorithm used in AI systems is another problem with risk, such as algorithmic bias. Should these systems not be managed efficiently, they tend to 'mirror' their training data set and produce skewed results (Akpan, 2024). The auditor must know that these tools are correctly set up and that the results are not skewed.

Despite technological advancements, the human factor is essential in AI-based audits. AI has demonstrated the ability to work with large volumes of data and execute repetitive tasks. Still, audits require judgment, organizational perspective, and the ability to appreciate meanings within audit evidence (Hasan, 2021). First, the role of AI has to be understood as one that complements the activity of auditors rather than replaces it.

The Evolving Role of Auditors

Whereas the use of AI and ML in auditing is still in its early stages, their importance is expected to increase in the future. Charles (2019) stated that future auditors must acquire new skills to use these technologies. This involves knowing what AI models' processes are, how their results are derived, and what areas AI models may not detect as problematic.

In addition, the AI and ML applications for auditing will not only continue to be employed in financial audits. These technologies suit operations, compliance, ESG, environmental, and social audits (Hasan, 2021). Using AI tools, auditors can deliver more frequent assessments based on real-time data across different organizational contexts, thus assisting organizations to operate effectively in increasingly complex regulatory environments.

Conclusion

AI and ML will revolutionize the work of auditors by helping to reduce the amount of manual work, improving risk assessment, and offering a more profound analysis of the financial and operating data. Nevertheless, these technologies must be included in the audit environment with a unique approach to make them accountable and ethically utilized. As mentioned by researchers, auditors should and have to adapt to those technologies and develop and adapt the skills and tasks of an auditor to today's advancing digital world. Auditing is not a science that AI will replace in the future. Still, it will be a syndicate of human auditors and the capabilities of AI to produce better, more efficient, and more accurate audits.

References

Akpan, D. M. (2024). Artificial Intelligence and Machine Learning. In Future-Proof Accounting: Data and Technology Strategies (pp. 49-64). Emerald Publishing Limited. https://doi.org/10.1108/978-1-83797-819-920241007

Charles, S. (2019). Charles Financial Strategies LLC. Charles Financial Strategies LLC. http://charlesfs.com

Ganapathy, V. (2023). AI in auditing: A comprehensive review of applications, benefits, and challenges. Shodh Sari-An International Multidisciplinary Journal, 2(4), 328-343. https://icertpublication.com/wp-content/uploads/2023/10/29.-AI-in-Auditing.pdf

Hasan, A. R. (2021). Artificial Intelligence (AI) in accounting & auditing: A Literature review. Open Journal of Business and Management, 10(1), 440-465. https://www.scirp.org/journal/paperinformation?paperid=115007