Using Data Analytics to Test Audit Engagements
Data analytics is improving the efficiency and effectiveness of audits, and audit firms are increasingly using audit data analytics to gather evidence for both risk assessment and substantive testing.
There is an increasing use of tests that use the full population of data instead of a sample. For example, when performing substantive tests for the occurrence of sales for clients with available and usable sales and cash receipts data, some auditors will use a technology-based tool to match individual sales transactions to subsequent cash receipts. The test can be run on the entire population of sales transactions and replaces the manual, sample-based, matching test.
Audit clients are also adapting to the use of technology-based analysis and digital data. More companies are moving their data into data warehouses. According to IBM, “a data warehouse, or enterprise data warehouse (EDW), is a system that aggregates data from different sources into a single, central, consistent data store to support data analysis, data mining, artificial intelligence (AI), and machine learning. A data warehouse system enables an organization to run powerful analytics on huge volumes (petabytes and petabytes) of historical data in ways that a standard database cannot.”
As clients adapt to the use of digital data and data analytics, they will update and change their business processes and internal controls. For example, as some clients increase the use of technology and analytics in the inventory process, they develop new controls and processes for inventory. The auditor must understand the changes to the client’s business processes and controls while planning the current year’s audit.
Still, most clients have not moved to data warehouses. For those clients the data resides in different systems, in different formats, and some may even be in flat, stagnant files. The auditor must understand the controls around each of these data files, understand the structure of each file, combine and clean the data before they can use the data for audit tests. This can be a very time consuming and expensive process.
In 2021, Public Company Accounting Oversight Board (PCAOB) established a research project on data and technology to assess the areas where data analytics is used and the extent of its use in financial statement audits. The goal of the research project is “to assess whether there is a need for guidance, changes to PCAOB standards, or other regulatory actions.”
The PCAOB periodically issues information on the research task force’s findings but has not issued any new guidance so far. However, in a 2021 report, it stated that the task force’s research so far indicates that there may soon be a need to update AS 1105 Audit Evidence, AS 2301 The Auditor’s Responses to the Risks of Material Misstatement, AS 2310 The Confirmation Process and AS 2510 Auditing Inventories. The task force found that there is an increase in the use of various sources of data both internal and external to the audit client and a significant increase in the use of technology-based tools to evaluate the data. The task force reports that “some audit firms believe that the use of technology-based tools, in certain instances, provides more persuasive evidence than traditional audit techniques.”
Advances in data storage, data capture, data analytics tools and accounting technology will have profound effects on the auditing profession and on client business processes. Clients will become even more connected to their upstream and downstream business partners as they enter into distributive ledger consortiums with their supply chain partners.
Distributive ledger technology is allowing the use of automated smart contracts that, after they are programmed into the distributed ledger, can be executed without the need of human intervention. AI is automating many business decisions. Auditors need to understand how AI is making the decisions and the controls around the programming and processes. Robotic process automation is allowing auditors to automate relatively simple, repetitive audit tasks, freeing up auditors to perform more high-level judgment and estimation audit decisions.
This is an exciting and dynamic time for auditing and accounting professionals. It is essential for today’s professionals to stay well-informed and to keep their skill set current to remain competitive and successful in the profession.
- AICPA. 2017. Guide to Data Analytics. New York. American Institute of Certified Public Accountants.
- PCAOB. 2021. Spotlight: Data and Technology Research Project Update, May 2021. Public Company Accounting Oversight Board.
- IBM Cloud Education. Accessed June 6, 2022. https://www.ibm.com/cloud/learn/data-warehouse
Nancy Uddin, Ph.D., is the chair and associate professor of accounting at Monmouth University. She can be reached at firstname.lastname@example.org.