Just like how digital transformation ushered in a new era for industries like retail, banking and healthcare, the field of auditing has also been disrupted by technological advancements — especially with the recent wave of breakthroughs in data science. Thanks to advances in data analytics, auditors now can gain deeper insights into their clients’ organisations, from testing large data sets to refining risk assessment methodologies for better accuracy.
According to a 2020 article by data skills platform Quanthub, there were three times as many job postings for data scientists versus job searches, suggesting a shortage of talent in the field of data science. There is also a perceived skills gap among auditors to transition from conventional auditing procedures to data analytics techniques to achieve more continuous assurance and relevant reporting. Anecdotal evidence suggests that data analytics streamlines the audit process and yields valuable insights that improve audit quality, but empirical evidence on the effect of data analytics on audit quality is scarce.
“There are lots of discussions of big data, AI, machine learning and industry revamp towards smart technology,” says SMU School of Accountancy Associate Professor of Accounting, Sterling Huang. “But there is little academic evidence to gauge the effectiveness of these technologies in the audit market.”
As such, Associate Professor Huang and fellow SMU School of Accountancy Associate Professor of Accounting, Rencheng Wang co-authored the paper “Data Analytics and Audit Quality” in 2021 to fill this gap by examining whether and how the use of data analytics can affect audit quality.
The power of data
The authors studied the data analytics capability of audit offices by assessing the number of employees who have listed relevant data analytics skills on their LinkedIn profiles. As compared to clients of audit offices with fewer data analytics expertise, the study found that offices with greater data science expertise are associated with higher audit quality, evident by fewer accounting restatements among their audit clients. One of the explanations for the improved audit quality is that frontline auditors can leverage data analytics to ask relevant questions and thus better understand the clients' business and associated audit risk in a more timely manner.
The study is among the first to suggest that an audit office's analytics capability is a vital attribute affecting audit quality. As the study states, data analytics empower auditors with the ability to interpret large amounts of granular data for a more holistic view of their client's businesses, identifying risk areas requiring more audit effort. Consequently, auditors can achieve the same level of assurance more efficiently at a lower cost or a higher level of confidence at a similar cost. While there may be concerns that the use of technological resources may increase an over-reliance on data for decision purposes, raise issues of compliance, there is nevertheless a pressing need for human capital investment in data analytics; as well as for policies and procedures to be in place to ensure the appropriate and ethical use of technological resources.
“The main challenge is to measure the data capabilities of audit firms, which is unobservable to academic researchers,” adds Associate Professor Huang. “One novelty of our paper is to measure human capital investment in data analytics. Due to the open-source nature of the software used in data analytics such as Python and R, human capital investment comprises a large share of the investment in data analytics and is likely to be positively correlated with overall data analytics capability.” For example, auditing professionals can leverage computerised data and file interrogation software to perform transaction testing on 100 percent of a population rather than tap on just a handful of samples. The more frequent or continuous monitoring of transactions by external auditors enables them to spread audit work throughout the year, identify potential issues earlier, and modify audit plans in response on a timely basis.
But the rise of big data and its impact on auditing also has a downside: irrelevant, poorly controlled, and unreliable data could negatively affect audit quality. As auditing standards have yet to be updated to reflect the usage of data analytics methodologies, there lacks clarity on whether evidence obtained from data analytics is permissible and compliant with the requirements of these standards.
The data analytics ROI
In order to justify the value of investing in data analytics human capital and aligning auditing standards with technological advances, the study provides evidence on how and under what conditions investments in new technologies can improve audit quality and complements existing literature that examines how auditor incentives and competencies affect audit quality by incorporating data analytics into auditor skill sets.
Notably, the results for the audit of complex business operations using data analytics appear incrementally stronger for clients with more business and geographic segments. In cases of complex accounting estimates, auditors also often face substantial uncertainty due to subjective and unverifiable assumptions. The study discovered that data analytics could at least partially alleviate this estimation uncertainty. For example, data analytics can automate Internet search methods over extended periods to collect information for evaluating otherwise hard-to-value assets. Auditors then use this information to confirm the reasonableness of established fair values.
As businesses become increasingly digitalised, the importance of data analytics in auditing becomes more pronounced. Such clients with digitalised businesses typically have readily available and easily accessible data that can be interpreted, thereby producing even more reliable audit quality.
By demonstrating the critical role of data analytics in improving the quality of audits, the paper should be of interest to policymakers and standard setters. It directly answers recent calls by the Public Company Accounting Oversight Board (2017) for more research to understand how changes in technology impact the skill sets required for auditors and how technology affects the business model of audit firms. Looking forward, Associate Professor Huang is conducting research to better understand the role of the judicial system in shaping supply chain resilience and flexibility. “Our pandemic experience highlights the importance of maintaining supply contract flexibility in an effort to navigate unexpected uncertainties,” he adds.
“The challenge lies in the fact that many supply contracts are negotiated long before any disruptions and neither contracting party has perfect foresight of what the future holds at the time of contracting. Understanding the factors that influence the rigidity and flexibility of supply contracts is thus important in improving the economic efficiency of partnerships, and reducing the friction and litigation costs associated with unanticipated, disruptive events, especially in the supply chain sector.”
The full draft of the paper is available here.
