Abstract
The emergence of artificial intelligence (AI) has opened new challenges to traditional audit practices within the e-commerce industry. This chapter investigates the current state of audit professionals, who leverage audit data analysis technology to analyze client big data systems. It explores the future of audit methods by introducing intervention by the latest Do-Calculus concepts and proposing a potential connection between BDA and ADA\. The authors employ qualitative analysis, combining in-depth interviews and content analysis, to explore how external auditors use skepticism to make necessary professional judgments. The authors' analysis reveals that auditors view AI technology as a tool and still rely on a combination of manual and computer-based audit methods to ensure the quality of their professional judgments and maintain the status quo. However, the challenge remains as audit practitioners must continually improve their AI capabilities in the new AI era.
Harnessing Technology for Knowledge Transfer in Accountancy, Auditing, and Finance
Top1. Introduction
As an example, let's consider a scene that auditors often come across:
An auditor was examining a hotel client's financial records. During the audit, the hotel president expressed satisfaction with the hotel's business performance, despite the unfavorable economic environment. The president attributed the success to the years of dedication and hard work put in by the management team in raising the hotel's brand awareness to a new level. The hotel president was particularly pleased with the hotel's loyal clients, whose patronage had brought in unexpected revenue. It was evident that the hotel's commitment to quality service had earned them a reputation that was paying off even in trying times.
When conducting a substantive audit test, for example, for the hotel’s revenue, the auditor needs to ensure that the information provided by the management is accurate and reliable. However, it is equally important for the auditor to adopt a suspicious mindset (Brydon, 2019) throughout the audit process. This will help the auditor obtain sufficient and appropriate audit evidence that is reliable and relevant. A suspicious mindset is crucial for determining the adequacy and reliability of audit testing. This aspect can become a problematic area in many professional judgments, especially if the management's fraudulent representation is backed by fabricated business digital data stored in the client's database. In such situations, the intelligence level of the audit data analytics becomes a significant concern.
Over the past few years, there has been a remarkable rise in the application of digital tools in the fields of business and auditing due to the continuous innovation of technology (Nair and Gupta, 2021; Lehner et al., 2022). As digital auditing work involves analyzing vast amounts of data and making professional judgments based on the analysis results, the use of audit methods and tools in auditing tasks has become gradually more significant.
In the current big data environment, there are three primary paths for digital audit professional judgment (see Figure 1). The first path, i.e., path (i), shows the assistance provided by Big Data Analytics (BDA) to Audit Data Analytics (ADA), which affects Audit Professional Judgment (APJ) (ACCA, 2019). This implies that BDA can help auditors in making informed decisions by providing them with valuable insights and perspectives. The second possible path, i.e., path (ii), demonstrates that BDA directly influences APJ (Belissent, 2017). In this scenario, auditors use BDA to analyze vast amounts of data and make judgments based on the analysis results. In other words, the analysis results directly influence the auditor's judgment. The final path, i.e., path (iii), shows that machine learning may play a role in APJ.
According to Mitchell (1997, p. 2), machine learning is “A computer program is said to learn from experience E concerning some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E”. Therefore, machine learning refers to the ability of computer systems to learn from data, identify patterns, and make decisions without human intervention. If machine learning is integrated into the auditing process, it has the potential to enhance the accuracy and efficiency of APJ.
Figure 1. Professional judgment path for digital auditing
As technology continues to advance, the world of digital auditing is ever-changing, presenting digital audit professionals with new opportunities to exercise their expertise. Hence, it is essential to remain up-to-date on the latest technological developments to improve the quality and efficiency of audits.
Key Terms in this Chapter
Intervention: An intentional involvement in processes or systems designed to influence events and/or consequences. This term can refer to a single activity but typically refers to a group of activities organized within a project, plan, or tool.
Counterfactuals: A belief that a cause may have an impact, and its impact must be different from what would have happened without it. If it were not for it, its impact - at least partially, usually entirely - would also disappear.
Suspicious mindset: The initial lack of trust where auditors believe financial statements involve criminal or dishonest activities until they are convinced or reassured.
Audit Data Analytics (ADA): Algorithm designed for audit software to implement audit testing procedures. ADA is used to discover and analyze patterns, identify anomalies, and obtain other useful information from data populations relevant to the audit.
Digital Audit: Digital audit refers to a comprehensive audit method that utilizes computer and information technology to inspect, analyze, and evaluate the financial accounting and internal control of enterprises. Digital auditing uses data analysis tools and techniques to collect, process, and analyze a large amount of data, automate various tasks in the auditing process, and improve auditing efficiency and accuracy.
Digitalization: Information about commercial activities is immediately digitally transformed by information technology and stored in a digital data store for further reading.
Substantive Analytical Review (SAR): The analysis program includes evaluating financial information by analyzing the reasonable relationship between financial and non-financial data. They also include conducting necessary investigations into identified fluctuations or relationships that are inconsistent with other relevant information or differ significantly from expected values (ISA 520). A basic premise of applying analytical programs is that, in the absence of opposite conditions, it is reasonable to expect the existence and continuation of seemingly reasonable relationships between data.
Big Data Analytics (BDA): Big data analysis is the process of collecting, examining, and analyzing large amounts of data to discover market trends, insights, and patterns, thereby helping companies make better business decisions. These pieces of information can be quickly and effectively obtained, enabling companies to flexibly develop plans to maintain a competitive advantage.
Audit Professional Judgment (APJ): A behaviour that requires auditors to make decisions, analyses, or evaluations based on their knowledge, skills, training, or experience as auditors, by relevant standards, laws, regulations, or principles of applicable financial reporting frameworks.
I.T. Tools: The applications and services that help people do their work more efficiently. A common tool in IT, for example, is cloud storage. Many companies run the risk of losing data in the event of power shortages, hardware problems or even unexpected software crashes.