Application of Machine Learning in Auditing Teaching: A Case Study of Predicting the Audit Report Type of China ST Listed Companies

<p>Artificial intelligence is an important emerging technology in the accounting industry. Fear and hype associated with artificial intelligence and its impact on accounting and auditing jobs have pervaded the professional fields of accounting and auditing. It is important to develop AI competency in accountants and auditors. This paper presents a teaching case for a professor or lecturer to use for teaching machine learning to accounting students. The case is based on openly available data from the China Stock Market & Accounting Research database and aims to teach students how to predict the future audit report type of a China ST listed company. Through case teaching, students can learn skills related to computer-assisted auditing tools and machine learning (such as ACL) develop the confidence to apply artificial intelligence in their education and future work.</p> <p>&nbsp;</p>

The Development of Accounting Data Analytics Curriculum

<p>Accounting programs are now being encouraged to incorporate data analytics into their programs. This article will provide suggestions on how to set up a data analytics program in a school of accountancy, answer why it is important to incorporate data analytics within accounting programs, and nally how to create a well-integrated curriculum.</p> <p>&nbsp;</p>

Teaching Case – Predicting the Probability of Company Bankruptcy with CAATs

<p>The paper provides a machine-learning experimental process for a real-world corporate financial bankruptcy case: Chunghwa Picture Tubes, Ltd., in Taiwan in 2019. The teaching case addresses major topics in financial bankruptcy analytics to enable business students to learn how to analyze leveraged finance and distressed debt and to predict bankruptcy. It is a science, technology, engineering, and mathematics (STEM) teaching case with a project-based learning method. The learning goal of the teaching case is to inspire and encourage students through planned teaching activities. Students start by thinking through problems or situations and establishing a machine-learning project using computer-assisted audit technique (CAAT) software. After students conduct a self-directed project, the student can use the new knowledge to develop a new bankruptcy-case analysis.</p> <p>&nbsp;</p>

Big Data: Ideology vs. Enlightenment

<p> &ldquo;Big Data&rdquo; is a technological term with a seemingly cognitive connotation that masks an ideological orientation of those attempting to be benevolently, criminally of even &ldquo;innocently&rdquo; in control of our knowledge and subsequent actions. Without an epistemological foundation &ldquo;small&rdquo; and especially &ldquo;big&rdquo; data are a myth. When &ldquo;the truth&rdquo; becomes &ldquo;what&rsquo;s on a digital screen&rdquo; under the control of those in charge of &ldquo;the cloud&rdquo; we are clouding our cultural heritage voluntarily to an extent that exposes us to the whims of those screening and displaying our data even in so-called &ldquo;post-truth&rdquo; fashion. Subsequent information and knowledge cannot be critically and rationally assessed for lack of evidence. All lessons learned during the last four centuries of enlightening efforts seem to be forgotten or ignored by us. Our preference for &ldquo;cognitive ease&rdquo; can be easily abused by those in control of modern information technology. We remain in &ldquo;self-imposed immaturity&rdquo; (Kant) while they can act primarily for their own economic, political, and social benefits and may even feel &ldquo;justified&rdquo; by the big-data-ideology. Knowledge must remain relevant to, testable and rationally believable by the legitimate recipients of any public data and information. An enlightened framework for data governance is overdue in the &ldquo;digital big data age!&rdquo;</p>

Computer Auditing: The way forward

<p>We broadly define computer auditing as any audit practices that may rely on information technology (IT). Such skill has long been argued and considered to be an important capability for both external and internal auditors for more than two decades though its applications were relatively limited in the past. In recent years, with the advance of information technology, what auditors can achieve with IT has dramatically changed. For example, auditors are now be able to perform both descriptive and predictive analyses, process both numeric and textual data, and apply such capability from assertion testing to compliance and risk assessments. This evolving capability has also brought the new term &ldquo;audit analytics&rdquo; to practices. Specifically, analytics focuses more on the business decisions and processes while the traditional computer auditing is mainly about audit. This improved capability and expanded scope have attracted a lot of attention with a wide range of applications. For instance, the PCAOB&rsquo;s new strategic plan (PCAOB 2018 ) has highlighted that &ldquo;[i]nnovations in data analytics and technology have great potential to improve the efficiency and effectiveness of financial reporting and the audit process&rdquo; (p.9). Audit firms and internal audit functions have also engaged in the development and the use of analytics in external and internal audit processes (e.g., Forbes 2018; Deloitte 2016; KPMG 2016), which have potentially changed the role of internal auditors to internal consultants.</p> <p>&nbsp;</p>

General Supply Warehouse: A Case Study in Internal Control Assessment and IT Auditing Software

<p>Technology is changing the auditing paradigm. As such, future accounting graduates are expected to have substantial technological skills. The proposed case provides students with an authentic scenario in which to apply such skills. Revenue and expenditure processing procedures are described for General Supply Warehouse, and several pertinent data files are also included. Students are required to conduct a thorough analysis of internal controls, identify associated weaknesses and utilize either ACL or IDEA to perform substantive testing. Data provided with the case have been engineered to produce measurable results from the pre-specified audit tests. The instructor&rsquo;s section of the case contains comprehensive solutions and a suggested implementation plan.</p> <p>&nbsp;</p>

Auditors’ Perceptions of and Competencies in Big Data and Data Analytics: An Empirical Investigation

<p>This study presents evidence on practicing auditors&rsquo; perceptions of and competencies in applying big data and data analytics to audit engagements. An electronic questionnaire distributed to accountants shows that auditors have good information technology skills and are well-acquainted with big data and data analytics. However, they lack relevant technical skills and are unfamiliar with related data analysis tools, excluding Excel. The results reveal 64.71% of accountants have not attended any training on big data and data analytics, while 31.37% plan to enhance related knowledge. Auditors need to obtain training on substantive audit risk assessments using big data and data analytics.</p> <p>&nbsp;</p>