Associate Professor Amy ZANG and Assistant Professor Wilbur CHEN of the Department of Accounting, HKUST Business School

This article is co-authored by Professor Amy Zang and Professor Wilbur Chen, Co-Directors of the Center for Securities Analysis with Financial Technology at HKUST’s Department of Accounting, and Roman Fan and Madelyn Gong of the AI Institute of Deloitte.

AI is poised to revolutionize accounting and auditing as it integrates into various aspects of the profession. While offering immense benefits, the adoption of this transformative technology also presents a host of challenges.

Impact on industry

As the capabilities of artificial intelligence (AI) continues to evolve, the auditing and accounting professions are among those most significantly impacted by these technologies.1 Many professionals in these fields have already found various applications of AI that are set to transform the auditing and accounting industries.

Recent research indicates that the adoption of AI in auditing and accounting can have substantial implications. For example, studies show that audit firms that adopt AI technologies experience fewer accounting errors and a reduced risk of audit failures. 2

At the same time, the integration of AI also presents risks for the auditing and accounting professions, especially in the area of talent management. Early evidence suggests that many junior auditors may be displaced due to the advanced capabilities of AI technologies.3

In this discussion, we will explore how AI is transforming the auditing and accounting industries, and examine strategies for professionals to navigate these changes effectively.

Use Cases of AI in Auditing and Accounting

Processing and Standardizing Information

In traditional accounting, accountants typically engage in extensive information retrieval, sifting through vast search results to extract relevant content for benchmarking competitors or confirming accounting standards and audit methodologies. This process is then enhanced by accountants’ professional expertise and experience, enabling them to make informed judgments and distill valuable insights.

AI has revolutionized how accountants access information and knowledge. By incorporating industry expertise, regulatory standards, policies, guidance and company-specific audit methodologies, auditors can create a proprietary knowledge graph for their enterprise. Accounting professionals can then interact with this knowledge graph, which serves as the professional’s “brain” through Large Language Models (LLMs). This natural language Q&A capability shortens the path from “question” to “answer,” minimizing retrieval time and allowing professionals to focus more on judgment and analysis.

Generative AI, in particular, has transformed how accountants interact with information. Traditionally, data is categorized into structured formats like online tables and bank statements, as well as unstructured content such as receipts and contracts. Processing these various types of data requires multiple tools and presents challenges due to the large volume of structured data with inconsistent quality and the complexities of dealing with unstructured data in bulk.

With generative AI, accountants can bridge the gap between different data types, extracting key information with unprecedented efficiency and uncovering hidden facts. For instance, AI can assist in data preprocessing by applying personalized methods for different data samples and automatically identifying and refining low-quality text through operations like string standardization and noise removal.4 Moreover, generative AI can enhance data reconciliation processes, such as matching loan portfolios with corresponding payments and related data. By ensuring that payments align with loan agreements, AI accelerates processes and improves the end-to-end accuracy of transactional records, linking back to the legal contract.5

Analyzing Information

A key function of the accounting and auditing profession is to detect and flag accounting issues, enhancing the quality of financial information used internally or presented to investors. However, AI is still not fully mature in performing quantitative financial analyses or calculations to identify accounting frauds.

In accounting academic research, scholars have traditionally relied on linear models to predict fraud and identify anomalous accounting entries among large samples of firm-year observations. However, these linear models often face significant challenges regarding prediction quality. The advent of AI offers the potential to enhance the accuracy of fraud detection in empirical research. For instance, studies in bookkeeping have demonstrated that advancements in graph machine learning can effectively identify anomalous entries, aiding accounting professionals in detecting errors within the accounting system.6

Additionally, research indicates that employing methods like random forests or gradient boosted regression trees can lead to significant improvements in fraud detection.7,8 Moreover, the use of automated machine learning also allows for real-time fraud detection, which would vastly improve the timeliness of fraud detection.9 These academic insights suggest a substantial opportunity for AI to improve error detection in auditing and accounting tasks.

Synthesizing Information

Finally, AI has the potential to revolutionize how information is synthesized in auditing and accounting. In these professions, presenting work often involves compiling scattered insights into a structured document while adhering to complex regulatory requirements. Generative AI can assist professionals in quickly establishing a starting point by decomposing tasks, systematically extracting information from various sources, and integrating it into a draft. By understanding context, AI aids in interpreting and compiling information, supporting professionals in documenting the process.

Additionally, AI can function as a compliance checker, ensuring that documents align with industry regulations, company policies, and methodologies. It flags errors and omissions, providing references to guidelines that enable professionals to make necessary corrections and revisions.

The advent of generative AI thus holds the potential to radically restructure workflows in the accounting and auditing professions, shifting from a copilot to an agent model and achieving a high level of human-machine integration. This transformation is primarily reflected in the reallocation of professionals’ time, reducing the effort spent on fact-gathering and documentation and allowing them to focus on framework design, review, and judgment, ultimately delivering better insights.

Challenges in AI Adoption

While there are many beneficial applications of AI in auditing and accounting, the adoption of this technology has not been without challenges. Notably, many accounting and auditing professionals still lack the expertise to determine which tasks are suitable for AI or to recognize the technology’s limitations. Additionally, it remains challenging for professionals to understand and identify the potential risks associated with AI, as well as to evaluate and compare AI’s performance with that of humans. Consequently, as auditing firms embrace AI, they risk the valuable insights of experienced workers and may become overly reliant on AI without proper critical assessment.

Another significant barrier is employee skepticism toward AI. Experimental studies indicate that audit professionals tend to exhibit “algorithmic aversion”, underestimating the value of AI-generated advice.10 The aversion is substantial, and these studies reveal that auditors discount AI-driven recommendations by 23% compared to human benchmarks.

Another critical issue is data privacy concerns. Recent surveys on the adoption of data analytics in the audit industry show that clients are apprehensive about data leakage when proprietary information is used in analytics models. 11

Regulatory skepticism also poses a challenge to AI adoption. As a relatively new technology, guidance on the use of AI is limited, which discourages audit firms from embracing these innovations. Research indicates that regulators are more likely to second-guess estimates derived from data analytics, suggesting a perception that automated outputs are of lower quality than those produced by humans. 12

To fully unlock the potential of AI in the auditing and accounting industries, significant efforts must be made to alleviate skepticism among employees, clients, and regulators regarding AI adoption.

The Future of Accounting Work

AI is enhancing the efficiency and output of professionals in auditing and accounting, but it also presents significant challenges in human capital management within the industry. A survey by Goldman Sachs in March 2023 indicates that accountants and bookkeepers are among the professions most likely to be affected by the rise of AI, highlighting its substantial impact on talent development.

Recent research suggests that popular ChatGPT models can replicate many skills associated with auditors and accountants. For instance, when researchers tasked ChatGPT with accounting licensure exams (such as CPA, CMA, CIA and EA exams), the models achieved scores of 85% across all tests.13 This staggering statistic underscores the potential disruptive impact that AI technologies may have on the auditing workforce.

However, does this remarkable AI performance imply that companies no longer need professional auditors and accounting talent? We believe the answer is no. In fact, research from Deloitte shows that 93% of CFOs consider it crucial to incorporate talent with generative AI skills into their finance teams over the next two years, making this a top concern.14 Thus, a critical question for the auditing and accounting profession at this juncture is twofold: (1) what are the essential skills needed in the era of AI? and (2) How can auditors reskill themselves to effectively integrate AI into their workflows?

What kinds of skills do accountants and auditors need?

In the era of Gen AI, a new workforce design principle has emerged, known as Workforce Intelligence (Wi). This concept refers to the seamless integration of Human Intelligence (Hi) and Artificial Intelligence (Ai). This workforce design is primarily focused on reallocating professionals’ time, resulting in a shift in individual value. Tasks such as information gathering and initial idea formulation are now managed by generative AI, allowing professionals to concentrate on enhancing their skills in insight generation and professional judgment.

This trend is undoubtedly the future for accountants and auditors. According to the World Economic Forum’s Report, a survey on the skills expected to increase or decrease in importance over the next five years highlights evolving business expectations regarding worker skills.15 Cognitive skills are reported to be rising most rapidly, emphasizing the growing need for complex problem-solving in the workplace. Creative thinking is anticipated to gain importance slightly faster than analytical thinking, with technology literacy emerging as the third-fastest growing core skill.

How should accountants and auditors reskill and upskill?

Firstly, embrace change and confront challenges by exploring the accounting and auditing fields with generative AI tools. As an accountant or auditor, it’s essential to learn how to use these tools correctly and effectively, including self-assessing your proficiency level in prompt engineering and optimizing your prompts based on best practices. This may pose a greater challenge for older workers compared to their younger counterparts.

Secondly, beyond mastering the tools, accountants and auditors should evaluate which tasks in their profession are suitable for generative AI assistance. This process involves recognizing the technology’s limitations, understanding potential risks, critically selecting appropriate use cases, and ensuring human review supplements AI outputs. Professionals should shift their focus from “how to do” expertise to “how to do it better and faster with Gen AI tools”, fostering better human-AI alignment.

Finally, the accounting and auditing professions must collectively address the issue of knowledge retention. As generative AI develops, some decision-making will inevitably be delegated to AI. However, it’s crucial to identify the knowledge that auditors and accountants must retain to avoid over-reliance on AI and to maintain sound judgment. Determining which expertise can be entrusted to AI with minimal human intervention is a thought-provoking exercise that leaders in the auditing and accounting fields should actively pursue.

References

1. Eloundou, T., S. Manning, P. Mishkin, and D. Rock. 2024. GPTs are GPTs: An early look at the labor market impact potential of large language models. Science 384 (6702), pp. 1306-1308.

2. Law, K. K. F., and M. Shen. 2024. How does artificial intelligence shape audit firms? Management Science, Forthcoming.

3. Fedyk, A., J. Hodson, N. Khimich, and T. Fedyk. 2022. Is artificial intelligence improving the audit process? Review of Accounting Studies 27 (3):938-985.

4. Fan Zhou, Zengzhi Wang, Qian Liu, Junlong Li, PengFei Liu. 2024. Programming Every Example: Lifting Pre- training Data Quality Like Experts at Scale, Working Paper, Available from https://arxiv.org/abs/2409.17115.

5. Bloomberg, “Citi used generative AI to read 1,089 pages of new capital rules”, Oct 2023.

6. Liang, P. J., A. Wang, L. Akoglu, and C. Faloutsos. 2021. Pattern recognition and anomaly detection in bookkeeping data. Working paper. Asian Bureau of Finance and Economic Research (ABFER). Available from https://www.abfer.org/component/edocman/main-annual-conference/pattern-recognition-and-anomaly- detection-in-bookkeeping-data.

7. Bao, Y., Bin. Ke, Bin. Li, Y. J. Yu, and Jie Zhang. 2020. Detecting accounting fraud in publicly traded U.S. Firms using a machine learning approach. Journal of Accounting Research 58 (1):199-235.

8. Bertomeu, J., E. Cheynel, E. Floyd, and W. Pan. 2021. Using machine learning to detect misstatements. Review of Accounting Studies 26 (2):468-519.

9. Jiang, L., M. Vasarhelyi, and C. A. Zhang. 2022. Towards real-time financial statement fraud detection using machine learning. Working paper. Rutgers University. Available from https://ssrn.com/abstract=4003621.

10. Commerford, B. P., S. A. Dennis, J. R. Joe, and J. W. Ulla. 2022. Man versus machine: Complex estimates and auditor reliance on artificial intelligence. Journal of Accounting Research 60 (1):171-201.

11. Austin, A. A., T. D. Carpenter, M. H. Christ, and C. S. Nielson. 2021. The data analytics journey: Interactions among auditors, managers, regulation, and technology. Contemporary Accounting Research 38 (3):1888-1924.

12. Cao, T., R.-R. Duh, H.-T. Tan, and T. Xu. 2022. Enhancing auditors' reliance on data analytics under inspection risk using fixed and growth mindsets. The Accounting Review 97 (3):131-153.

13. Eulerich, M., A. Sanatizadeh, H. Vakilzadeh, and D. A. Wood. 2024. Is it all hype? Chatgpt’s performance and disruptive potential in the accounting and auditing industries. Review of Accounting Studies 29 (3):2318-2349.

14. Deloitte, Research from Deloitte CFO Signals Q1 2024

15. World Economic Forum, Future of Jobs Report, May 2023