Keywords

1 Introduction

An external audit of financial statements (hereafter “an audit”) is a significant contributor to the overall economy (Etim et al., 2020). It is performed with the sole purpose of reporting whether financial statements were prepared in accordance with the financial reporting framework (International Auditing and Assurance Standards Board, 2014). When these financial statements lack accuracy, contain errors or omissions, or are even misleading, the public that relies on these financial statements may make misinformed and/or incorrect economic decisions (Salih & Flayyih, 2020).

Despite its significance, the auditing profession has experienced several difficulties worldwide (Kueppers & Sullivan, 2010). These challenges include advancements in technological developments and, most importantly, the decline in audit quality, which has caused stakeholders to doubt the audit profession and the audit process as a whole (Aziza & Agus, 2019).

Numerous corporate failures have also been experienced in South Africa, partly attributed to audit quality failure. This is echoed by Harber (2016), who states that there have been several known corporate failures in South Africa in the past, with the collapse of the African Bank being one of them. According to (Marchant & Mosiana, 2020), corporate failures in South Africa, include VBS Bank, whose investors, largely stokvels and municipalities, suffered a great loss due to KPMG’s failure to raise red flags in the bank’s financial statements and KPMG issuing a falsified regulatory report.

Etim et al. (2020) describe how the effects of corporate failures are often felt by stakeholders, investors or shareholders, as billions are lost in the financial value chain when these companies collapse. This statement is echoed by Cole et al. (2021), when they state that financial instability or scandals cause company failures, which have severe negative effects on all parties involved, including the general public, workers, auditors, creditors, business partners, capital markets, investors, and regulators.

The corporate failures noted above clearly indicate that both internationally and in South Africa, there is evidence of poor audit quality, which severely impacts the stakeholders. A question of why audit quality is important may arise. An audit assures the credibility of financial statements and, as such, a quality audit contributes greatly to this credibility (EY, 2019; Haapamäki & Sihvonen, 2019; Kilgore et al., 2011; Soyemi et al., 2021).

In its Global Competitiveness Report, the World Economic Forum has previously ranked the IRBA as the best standard-setting and regulatory body in the world (Issirinarain, 2016). This statement is echoed by the Business Day (2020) in an article titled How a Mighty Profession Has Fallen, which states that the South African auditing standards were recognised as number one out of 141 globally in the World Economic Forum’s (hereafter WEF) 2016 Global Competitiveness Report, holding that position for seven preceding years in a row. However, South Africa’s auditing profession is no longer considered the best in the world (Independent Regulatory Board for Auditors, 2019). The WEF’s 2019 Global Competitiveness Report, which was the latest to list countries by rank, listed South Africa as number 49 out of 141 for the strength of its auditing and accounting standards (World Economic Forum, 2019). This shows an evidence for the deterioration in the audit quality in South Africa. Over and above the regulator’s inspection results and the drop in the IRBA’s ranking as the best standard-setting and regulatory body in the world, there are numerous instances of corporate failures which have been witnessed previously, which have been attributed to a lack of audit quality, as discussed above.

To respond to corporate failures, audit quality concerns and the rapidly changing IT environment in the financial reporting value chain and to improve audit quality, more auditors are utilising data analytics techniques (Earley, 2015). This statement is echoed by International Auditing and Assurance Standards Board (2018) and Appelbaum et al. (2017), who states that there has been a change in how audits are performed over the years due to a change in the technological landscape in which organisations are operating. This can be seen by organisations’ use of the cloud and the internet of things, among other things, as part of their accounting systems.

As organisations are changing the tools they use to account for transactions, external auditors have also been exploring innovative ways to effectively and efficiently audit these transactions over the years. The International Auditing and Assurance Standards Board (2018) supports this by stating that the change in how companies operate has necessitated the need for auditors to consider new or different ways of performing audits of financial statements. This is echoed by Association of Chartered Certified Accountants and Chartered Accountants Australia and New Zealand (2019), which states that nearly all businesses are on the front lines of disruptive innovations that impact their auditors. Auditors must equip themselves accordingly to keep up with the changes in technology. One way auditors stay relevant is by introducing data analytics (Chartered Professional Accountants of Canada, 2016).

Auditors are using data analytics tools to, among other things, obtain an extensive understanding of their clients’ businesses (Earley, 2015). Similarly, Krieger et al. (2021, p. 1) note that “audit firms are increasingly engaging with advanced data analytics to improve the efficiency and effectiveness of external audits through the automation of audit work and obtaining a better understanding of the client’s business risk and thus their own audit risk”. Additionally, as a result, auditors broaden the scope of the items they audit. Botez (2018) supports this by stating that using traditional sampling methods to obtain audit evidence as required by auditing standards changes due to application of data analytics. This is because using data analytics for audits raises the quality of such audits (Alsahli & Kandeh, 2020; Gao et al., 2020).

It is imperative to understand how IT landscape developments impact audit quality. The Centre for Audit Quality (2018) explains this by stating that it is less likely for auditors to design traditional substantive procedures (like tests of details or substantive analytical procedures) that, if executed exclusively, would provide sufficient appropriate audit evidence to address identified assertion-level risks as the use of emerging technologies in the financial reporting process increases.

It is evident that auditors are introducing more data analytics into their audits, as there is a growing need for auditors to enhance audit quality. However, the result of using data analytics by auditors is a topic that needs to be explored further (Wang & Cuthbertson, 2015). This is supported by Earley (2015), who states that even though academic research on data analytics has gained momentum, research on this topic is still lacking due to auditing and accounting firms not providing researchers with feedback on their experiences in using data analytics.

Over the years, the world has witnessed instances where external auditors have issued unqualified or “clean” audit opinions for entities which collapse afterwards due to irregularities and/or fraud which is subsequently revealed in these entities, a term Etim et al. (2020) defines as “audit failure”. This definition of the term is elaborated on by Smith and Marx (2021), who state that audit failure is frequently linked to company failures and dishonest financial reporting. In many cases, it is thought that the auditors violated their obligation to serve as the “watchdog” for those who utilise financial statements by allowing fraudulent acts to go unnoticed (Smith & Marx, 2021). Audit regulators worldwide, including the Independent Regulatory Board for Auditors (hereafter IRBA), South Africa’s regulator, have blamed these failures on the poor quality of audits (Huang et al., 2019).

The fundamental research question of the study is to understand what role data analytics plays in improving the quality of external audits in South Africa. Furthermore, the link between data analytics, audit quality, and external audit is explored as well as the impact of the latest technological advancements, commonly known as the Fourth Industrial Revolution, on the way firms perform audits. The study also considers the benefits and challenges of using data analytics by audit firms in conducting audits.

2 Literature Review

The audit function has become incredibly important over the years. This can be attributed to what the mandate of an audit is, which is to give the financial statements that are being audited credibility (Harber, 2016). The responsibility of providing credibility to these financial statements does not solely rest with the auditor, but is also dependent on other factors including the quality of the financial statements, the oversight role played by those charged with the governance of the entity, and regulatory requirements (Accounting and Corporate Regulatory Authority & CPA Australia, 2015).

Despite its significance, the auditing profession has experienced several difficulties worldwide (Kueppers & Sullivan, 2010). These challenges include advancements in technological developments and, most importantly, the decline in audit quality, which has caused stakeholders to doubt the audit profession and the audit process as a whole (Aziza & Agus, 2019).

2.1 The Audit Function

The IRBA, the national standard-setter and audit regulator in South Africa, defines an external audit as:

“‘audit’ means the examination of, in accordance with prescribed or applicable auditing standards

  • financial statements with the objective of expressing an opinion as to their fairness or compliance with an identified financial reporting framework and any applicable statutory requirements; or

  • financial and other information, prepared in accordance with suitable criteria, to express an opinion on the financial and other information;” (Republic of South Africa, 2005, p. 8)

In the past, auditing’s primary responsibility was to verify that there was no fraud occurring in government organisations and that the state’s income and expenses were accurately recorded (Teck-Heang & Ali, 2008). According to Kueppers and Sullivan (2010), this remains relevant, as some stakeholders recognise the true importance of an audit and believe that an audit was only successfully executed if no fraud and/or an error was discovered throughout the audit process.

Apart from audit being used as a fraud detection tool, Kumar and Mohan (2015) argue that the main objective of an audit is to reassure the stakeholders who use the financial statements of the accuracy of those financial statements. This is supported by the International Auditing and Assurance Standards Board (2021) in International Standards on Auditing (ISA) 200, Overall Objectives of the Independent Auditor and the Conduct of an Audit in Accordance with International Standards on Auditing, which states that the goal of an audit is to increase intended users’ level of trust in the financial statements. This is done by the auditor judging whether the financial statements were produced in compliance with the appropriate financial reporting framework in all material aspects (International Auditing and Assurance Standards Board, 2021).

2.2 Audit Quality

Salih and Flayyih (2020) state that defining audit quality remains challenging for both professional organisations and academics. This statement is supported by (Hosseinniakani et al., 2014), who states that the numerous different aspects that might impact quality make it difficult to define audit quality.

This statement is confirmed by the International Auditing and Assurance Standards Board (2014), which argues that there is no universally recognised definition or measurement of audit quality due to its complexity. According to Knechel et al. (2012), academics have performed various studies to define audit quality with no success. Similarly, Rainsbury (2019) mentions that regulators and standards-setters have also dedicated time and agendas to develop a universal definition of audit quality. However, these attempts have also been unsuccessful. According to Christensen et al. (2016), audit firms have also been involved in fruitless talks to develop a definition of audit quality. In addition to being a challenging term to define, audit quality is also challenging to measure (Christensen et al., 2016; Kilgore et al., 2011). Knechel et al. (2012) support this statement when they state that there is still no agreement over how to define, let alone quantify, audit quality despite more than two decades of studies.

Based on a detailed review of the available definitions of audit quality, the most common definition is that by DeAngelo, as cited by most academics in their recent studies on audit quality (Harber, 2016). According to DeAngelo (1981), audit quality is the likelihood that an auditor would discover and disclose misstatements in the auditee’s financial statements to the appropriate stakeholder. This concept is echoed by Xiao et al. (2020), who explains that the probability that an auditor will uncover and report an existing major misstatement in line with the audit objective is known as audit quality. The International Auditing and Assurance Standards Board (2014), in its Framework for Audit Quality (hereafter the Framework), defines audit quality as an audit performed by a team that illustrates appropriate values, ethics, and behaviours by teams that were adequately qualified, competent, and experienced, and had sufficient time allocated to perform the audit work.

The International Auditing and Assurance Standards Board published the Framework in 2014 to promote what constitutes audit quality, putting the notion of audit quality on the agendas of important stakeholders and prompting key stakeholders to consider how audit quality might be enhanced. The Framework lists the following as elements of audit quality (International Auditing and Assurance Standards Board, 2014):

  • Inputs – This includes the auditor’s ethical behaviour, skills, knowledge and experience required to perform an audit;

  • Process – this entails how detailed both the audit process and quality controls are that will be utilised in the audit;

  • Outputs – outputs are items, such as the auditor’s report, distributed to the appropriate parties following an audit.

  • Key interactions within the financial reporting supply chain refer to the type of relationship and communication that an auditor has with management and the business owners.

  • Contextual factors include, for example, laws and regulations and corporate governance frameworks that need to be adhered to by the auditor as part of the audit.

In its IRBA Public Inspections Report on Audit Quality 2021, the IRBA (Independent Regulatory Board for Auditors, 2021) highlighted that audit quality might be achieved if audit firms and individual auditors can work on improving on deficiencies noted by regulators during firm and individual file inspections. On the other hand, audit quality may be attributed to having individuals with the required skills, knowledge, competence and ethical values as part of the audit team (Accounting and Corporate Regulatory Authority & CPA Australia, 2015; Alsughayer, 2021; Centre for Audit Quality, 2018). In addition, Garcia-Blandon and Argiles-Bosch (2018) point out that firms and individual auditors possessing the required knowledge or specialisation in the industry in which the auditee operates may result in higher audit quality. EY (2019) further stated that an essential component of audit quality is the use of technology and the digitisation of the audit process, as technology and digitisation allow auditors to perform audits more efficiently. This allows auditors to channel their resources towards focusing on the more important sections that require the auditor’s judgement.

2.3 Data Analytics

Over the past 10 years, auditors have increasingly used data analytics tools in audits (EY, 2015). Murphy and Tysiac (2015) echoes this when highlighting that over the past 20–30 years, auditors have been using more technology in an audit. (Earley, 2015) stated that this debunks the myth that audit and accounting firms do not employ data analytics throughout the external auditing process.

Data analytics is an instrument that an auditor can use to gather audit evidence through the identification and analysis of relationships between data, formulating expectations, combining data from different sources, and also using graphics or visualisations to reach conclusions (American Institute of Certified Public Accountants, 2015; Botez, 2018). This can be done at different stages in the audit, from pre-engagement activities to planning and executing the engagement and reporting (Botez, 2018).

Data analytics is classified into four types: descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics (Harvard Business School, n.d.). According to Tschakert et al. (2016) and Al-Dalabih (2018), descriptive analytics use past information to provide insights into what is happening. An example of descriptive analytics is an analysis of percentage changes in data and year-on-year financial statement analysis (Al-Dalabih, 2018; Tschakert et al., 2016). On the other hand, diagnostic analytics answers why something happened (International Federation of Accountants, 2018). Tschakert et al. (2016) mention that diagnostic analytics provide answers as to why the results came out the way they did. Variance analysis, explaining historical outcomes, is one example of diagnostic analytics (Tschakert et al., 2016). Harvard Business School highlights that diagnostic analytics are more detailed than descriptive analytics in explaining why something happened. Another type of data analytics, predictive analytics, uses patterns identified in historical data to predict what the future will or should look like, when something will happen and why it will happen (Al-Dalabih, 2018). Harvard Business School mentions that predictive analytics uses results of past trends and assumptions to make predictions possible. Tschakert et al. (2016) mention that an example of predictive analytics would be predicting how much of a trade receivable balance will be collected in the future using that specific trade receivable’s payment history. Lastly, prescriptive analytics answers the question of what should happen (International Federation of Accountants, 2018).

(Bekker, 2019) argues that the more complex the type of data analytics is, the more beneficial it is and, as such, adds more value to the audit process, as can be seen in Fig. 1 below. In simple terms, prescriptive analytics, when utilised, provide more value than predictive, diagnostic and descriptive analytics due to the complexity thereof.

Fig. 1
An illustration of added value contribution versus complexity plots descriptive analysis, diagnostic analysis, predictive analysis, and prescriptive analytics from the bottom to the top.

Complexity of data analytics versus value-add. Source: (Bekker, 2019)

Full size image

2.3.1 Benefits of Using Data Analytics

An auditor has several benefits when using data analytics at different phases of an audit. Data analytics during the audit planning stage provides the auditor with a comprehensive understanding of the business, allowing them to understand better the organisation and its operations (Eilifsen et al., 2020). Using anomalies, trends analyses, correlations, and fluctuations may help inform decisions about potential risks of material misstatement. This will ensure that auditors focus on significant items that matter and in ensuring that auditors obtain new in-depth insights about their clients (Earley, 2015; International Auditing and Assurance Standards Board, 2018; Murphy & Tysiac, 2015). According to (Zhu, 2021), data analytics also makes it possible to visually present the results of procedures performed to understand the entity, making it easy to spot the anomalies and where potential risks may arise.

Using data analytics during the execution stage of the audit results in efficiencies Liew et al. (2022) and Murphy and Tysiac (2015) supports this statement by stating that data analytics guarantees that 100% of the population could be tested rather than testing account balances, classes of transactions, and disclosures on a sample basis. This eliminates the limitations of concluding the entire population based on audit evidence, or a snapshot obtained from a sample selected and tested (Huang et al., 2022). Another advantage of data analytics includes enabling quick and simple testing of the whole population by the auditor O’Donnell (2015) and may also help the auditor identify fraud risk indicators to develop appropriate responses to this risk (EY, 2015). Earley (2015) and International Auditing and Assurance Standards Board (2018) also highlight that data analytics give auditors more persuasive, relevant, and sufficient audit evidence, which serves as the foundation for the auditor’s opinion, and that instead of performing the audit towards or at year-end, the utilisation of data analytics allows auditors to perform an audit throughout the year.

Murphy and Tysiac (2015) further mentions that achieving a higher degree of assurance at a similar cost, resulting in better audit quality for customers and investors and less audit risk and liability for auditors, would be the best benefit of utilising data analytics. This is supported by Raphael (2017), who states that using data analytics reduces auditors’ time to gather the necessary information for the audit. Another benefit of using data analytics is that it provides useful insights that an auditor can share with the audit committee. In contrast, data analytics can be seen as the key differentiating factor between audit firms when it comes to audit tendering (Financial Reporting Council, 2017).

2.3.2 Challenges of Using Data Analytics

The use of data analytics in an audit has been gaining momentum. However, it presents unique challenges that auditors must address before it becomes a norm (Earley, 2015). According to a survey performed by KPMG in 2014, 85% of the respondents mentioned that the biggest challenge with using data analytics was the inability of the audit teams to analyse the data collected successfully. Al-Ateeq et al. (2022) mention that one challenge to adopting data analytics is that auditors have not received enough training on how to use data analytics. Wüsthoff (2017) states that both audit firms and universities are behind in terms of teaching trainee accountants and students, respectively, about data analytics.

Another challenge auditors’ face in using data analytics is the quality of data received from their clients. According to Liew et al. (2022) and Earley (2015), auditors often struggle with obtaining data to analyse from clients, and when they do, they sometimes obtain incorrect or irrelevant data. This is echoed by Zhu (2021), who states that auditors face another challenge: clients often provide incomplete data to be analysed.

Literature also highlights the following as challenges faced by auditors in utilising data analytics in the audit process:

  • Audit teams struggle with obtaining sufficient and appropriate audit evidence when using data analytics, or they fail to document work performed using data analytics per the ISAs (Financial Reporting Council, 2017; Huang et al., 2022).

  • As audit teams obtain client data to analyse, they do not focus on ensuring that the client data remains secure from threats. This exposes the audit firms to a possible risk of reputational damage (Financial Reporting Council, 2017; Zhu, 2021).

  • The initial cost of setting up and utilising data analytics is usually high. To save the audit budget, auditors resort to not utilising any data analytics in their audits (Huang et al., 2022; Wang & Cuthbertson, 2015).

  • Some data analytics tests show “false positives”, where the results are inaccurate based on the data analysed. Auditors struggle with properly evaluating and concluding in these circumstances (Brazel et al., 2022; Wang & Cuthbertson, 2015).

  • There is an expectation gap between regulators and users of financial statements as they believe that utilising data analytics results in auditors providing absolute assurance on the financial statements, as opposed to reasonable assurance as required by the ISAs (Earley, 2015; Zhu, 2021).

2.4 The Relationship Between External Audit, Audit Quality and Data Analytics

As stated above, auditors have been using some form of analytics for some time. In the past, the environment in which companies operated was not as complicated as it is currently, and therefore there was no need to use automated processes in the audit (International Auditing and Assurance Standards Board, 2018). Since then, the world has experienced a change in the Information Technolofy (IT) landscape (Al-Ateeq et al., 2022), and auditors have used information technology tools in an audit since the companies being audited started using computerised systems to record their transactions (Financial Reporting Council, 2017).

Prior to advances in IT, companies used to record transactions manually, which unintentionally led to, among other things, monthly and/or yearly reporting being performed late, disorganised filing systems that were difficult to use and navigate, and several errors, omissions and misstatements being noted in the financial information. With advances in IT, there has been a shift in how companies record their transactions, which is now simplified and performed with the audit of IT systems. This resulted in monthly and/or yearly reporting being performed timeously and with fewer human errors, omissions and misstatements (Imene & Imhanzenobe, 2020).

Manita et al. (2020) note that digitalisation will improve the audit relevance and also improve the audit quality. Furthermore, they state that this will enable the culture of innovation within audit firms Specifically, traditional CAATs, which include ratio analyses, prior year versus current-year comparisons and trend analyses, according to SAB&T (2018) has been used by auditors for many different reasons during the audit process. This is further supported by Wüsthoff (2017), who mentions that the Big Four has invested in technology that helps them perform tasks that may be tedious or those that do not provide significant benefit for the auditor and the client in the shortest time possible.

Coupled with the changing IT landscape, data analytics is increasingly being used in the audit process, and there are more auditors employing data analytics now than ever before (Krieger et al., 2021). According to O’Donnell (2015), the introduction of data analytics in an audit does not change the basics of what auditors do, as auditors have always collected, analysed, and issued conclusions on the analysed data since the auditing profession started. Furthermore, Verver (2013) states that throughout the preceding 20 years, auditors have integrated the use of data analysis into audits.

To improve efficiencies and effectiveness of audit procedures, auditors have over the years used technology not limited to Microsoft Excel, Audit Command Language, Interactive Data Extraction and Analysis or the internet to perform tasks including financial statements analysis (both with prior years or industry norms), journal entry testing (including an analysis of abnormal or missing journals) (American Institute of Certified Public Accountants, 2015).

A question may arise regarding the relationship between external audit, audit quality and data analytics. Many authors have concluded that using data analytics in the audit process enhances audit quality (Brazel et al., 2022; Dagilienė & Klovienė, 2019; Jacky & Sulaiman, 2022). As stated above, by applying data analytics, auditors may better identify risks, such as the likelihood of fraud, by developing a comprehensive picture of the client. Furthermore, data analytics allows the auditor to test the whole population rather than just a sample. The role of data analytics is emphasised by Earley (2015), who states that not only does the use of data analytics provide auditors with greater coverage in terms of sample sizes, but it also provides auditors with greater insight into the client’s processes, resulting in enhanced audit quality. The role of data analytics in enhancing audit quality can be summarised by (De Santis & D’Onza, 2021), who state that data analytics may be a game changer in improving audit quality and revolutionising auditing practices.

3 Methodology

The study followed a qualitative approach in exploring the role of data analytics in enhancing external audit quality. This approach was used because it enables the researcher to delve into people’s and groups’ perspectives on an issue (Creswell, 2009). Criteria for selecting the study participants were developed, which included that the audit firm must use data analytics in performing its audits, and participants must thoroughly understand what data analytics, audit quality, and the audit function are. Purposive sampling was used to select the sample for the empirical study, allowing the researcher to have discretion in choosing audit firms that met the given criteria, South Africa’s Big Four audit firms. The empirical study used the survey method, consisting of a questionnaire sent to the heads of audit departments for each of the Big Four audit firms in South Africa. According to the (Independent Regulatory Board for Auditors, 2017), these firms also hold the largest market share of overall audit fees spent by JSE-listed companies. These participants are chosen because they have the required knowledge of data analytics, audit quality and the external audit function. Data received from the participants were analysed thematically to derive similarities from the participants to allow the researcher to reach conclusions on the responses.

During the research, ethical issues were considered to ascertain that participants did not suffer any physical or emotional harm. Ethical clearance for the study was also granted. All information was kept private and only used to generate aggregate results.

4 Findings

4.1 The Fourth Industrial Revolution’s Impact on the Audit Process

4.1.1 Question

In your opinion, how considerably have the latest technological advancements, commonly known as the Fourth Industrial Revolution, affected how your audit firm performs audits of financial statements? Please justify your answer.

4.1.2 The Objective of the Question

This question aimed to establish the participants’ perspectives of whether and/or how much information technology advances, commonly known as the Fourth Industrial Revolution, has affected the way audit firms perform audits of financial statements.

4.1.3 Presentation of Findings

The participants revealed the following in response to this question (Table 1).

Table 1 The Fourth Industrial Revolution’s impact on the audit process
Full size table

4.1.4 Interpretation of Findings

From the review of the data, it is evident that the participants undoubtedly agree that the Fourth Industrial Revolution has affected the way that audit firms perform audits of financial statements. The participants noted that the Fourth Industrial Revolution allowed auditors to be more efficient in their audits. They are able to perform a robust risk assessment based on an in-depth understanding of their client’s businesses and perform detailed, tailored procedures in response to these identified risks, which include testing 100% of the population instead of sampling. The above findings are consistent with the literature, where it was stated that the changes in the IT landscape affect how audits are performed (ICAEW Chartered Accountants, 2018). The literature pointed out that introducing IT and digitisation allows auditors to perform audits more efficiently (American Institute of Certified Public Accountants, 2015; EY, 2019).

4.2 Benefits of Using Data Analytics

4.2.1 Question

In your opinion, what benefits does your firm enjoy when using data analytics as part of the audit process?

4.2.2 The Objective of the Question

This sought to understand the benefits the participants enjoy from utilising data analytics in the audit process.

4.2.3 Presentation of Findings

The following was revealed by the participants in response to this question (Table 2).

Table 2 Benefits of utilising data analytics
Full size table

4.2.4 Interpretation of Findings

According to the literature presented, incorporating data analytics into an audit provides several benefits for firms. Some benefits included testing 100% of the population instead of sampling (Murphy & Tysiac, 2015), performing detailed risk assessments (Earley, 2015; International Auditing and Assurance Standards Board, 2018; Murphy & Tysiac, 2015), and obtaining more appropriate and sufficient audit evidence (Earley, 2015; International Auditing and Assurance Standards Board, 2018), among others. The findings above show that the firms are also enjoying the benefits of using data analytics in their audits. Firms C and D went as far as stating that they believe using data analytics results in a better-quality audit, which echoes the study’s goal of investigating the impact of data analytics in improving external audit quality.

4.3 Challenges When Using Data Analytics

4.3.1 Question

In your opinion, which challenges does your firm experience when using data analytics as part of the audit process?

4.3.2 The Objective of the Question

The objective of this question was to learn about the challenges organisations’ face when applying data analytics in the audit process.

4.3.3 Presentation of Findings

The participants revealed the following in response to this question (Table 3).

Table 3 Challenges when using data analytics
Full size table

4.3.4 Interpretation of Findings

The findings reveal that one of the biggest challenges with using data analytics in the audit process is obtaining good quality and well-structured data in the right format to be analysed by the auditors. The reason why auditors experience the above challenge is either due to the clients’ IT systems being outdated, clients having multiple IT systems that interface which may produce different reports of the same data, clients not understanding their own data, or clients providing auditors with inaccurate and incomplete data. This is consistent with the literature which revealed that auditors often struggle with obtaining data to analyse from clients or obtain data that is incorrect or irrelevant (Earley, 2015; Liew et al., 2022).

Interestingly, the findings also revealed that auditors face another challenge when using data analytics, where both the audit firms and the clients are still resistant to the change brought by data analytics in the financial value chain. Firm C and Firm D believe that changing the mindset around data analytics will result in much more utilisation of data analytics in an audit.

4.4 Improvement Required in the Utilisation of Data Analytics

4.4.1 Question

Are you of the opinion that there is room for improvement in how audit teams utilise data analytics in the audit process? If you answered yes, please provide suggestions.

4.4.2 The Objective of the Question

This question aimed to determine whether participants felt there was an opportunity for improvement in how audit teams use data analytics.

4.4.3 Presentation of Findings

The participants revealed the following in response to this question (Table 4).

Table 4 Improvement required in the utilisation of data analytics
Full size table

4.4.4 Interpretation of Findings

Figure 2 indicates that 100% of the participants provided a definitive answer to the question, stating that they, without a doubt, believe there is room for improvement in how audit firms utilise data analytics in their audits. In the literature it was noted that auditors default to not using any data analytics tools when they are presented with a wide range of data analytics tools to choose from (Brazel et al., 2022; Huang et al., 2022; Krieger et al., 2021). The participants indicated that one way in which audit firms can improve on how they adopt data analytics is by ensuring that tailored data analytics are created to suit the different types of engagements and that when these data analytics tools are made available by the firms, audit teams do utilise them in their respective audits.

Fig. 2
A piechart depicts the following result. Yes, 100%. No, 0%.

Definitive response to question: Is there improvement required in the utilisation of data analytics? Source: Author’s own compilation

Full size image

As the literature and findings suggest, one of the difficulties firms encounter when using data analytics is struggling to obtain good quality data to analyse from clients or obtaining data that is incorrect or irrelevant (Earley, 2015; Liew et al., 2022). One of the suggestions on improving how firms utilise data analytics, Firm B revealed that “clients need to understand their data and be able to provide it in an appropriate format.”

4.5 Impact of Data Analytics on Audit Quality

4.5.1 Question

Do you think that using data analytics to audit financial statements enhances audit quality? Please justify your answer.

4.5.2 The Objective of the Question

This was important, as it sought the participants’ views about whether they believe utilising data analytics in the audit process enhances audit quality.

4.5.3 Presentation of Findings

The participants revealed the following in response to this question (Table 5).

Table 5 Impact of data analytics on audit quality
Full size table

4.5.4 Interpretation of Findings

The findings, similar to what was found in the literature reviewed, revealed that 100% of the participants (Fig. 3) unanimously believe that using data analytics in auditing financial statements enhances audit quality. Firm A and Firm C elaborated on their responses and stated that data analytics enable auditors to detect and analyse misstatement risks, leading to a better-quality audit. Firms C and D also stated that because data analytics make testing 100% of the population possible, auditors perform a better-quality audit than traditional sampling methods. This result agrees with literature presented, where it was reported that using data analytics in an audit increases audit quality (De Santis & D’Onza, 2021).

Fig. 3
A piechart depicts the following result. Yes, 100%. No, 0%.

Definitive response to question: Does the use of data analytics enhance audit quality? Source: Author’s own compilation

Full size image

5 Conclusion

The basis on which auditors perform their audits has not fundamentally changed, even with the introduction of data analytics and the Fourth Industrial Revolution. The literature review and empirical data found that there is a noticeably growing trend of auditors utilising data analytics in audits and that all phases of the audit process can incorporate data analytics. Data analytics provide various benefits to the auditor and that even though the use of data analytics provides benefits, it also provides unique challenges. Furthermore, the study highlights that The Fourth Industrial Revolution, a term used to describe recent technology breakthroughs, has impacted how audit firms conduct financial statement audits.

It was determined that data analytics provide various benefits to the auditor and that even though the use of data analytics provides benefits, it also provides unique challenges that auditors need to overcome. Similarly, the study found a positive link between the use of data analytics and the outcomes of regulatory inspections. The empirical study revealed strong evidence that there are notable audit quality concerns in South Africa, which have been attributed to a failure in audit quality, and that auditors are using data analytics to overcome these concerns. The study concluded that using data analytics in an audit enhances external audit quality.