Using Data Analytics in Audits

Data Analytics is the process of inspecting, cleansing, transforming and modeling raw data with the purpose of discovering useful information, drawing conclusions and supporting decision making.

Traditional audit methods served auditors for decades but as technology advances and stakeholders’ expectations evolve, so does the need for auditors to innovate and transform their approaches in order to keep pace with demand. Advances in technology and software solutions like Computer Aided Audit Tools (CAATS) and Audit Data Analytics (ADA) make it possible for auditors to fundamentally change the way a financial statement audit is done.

What is new about Audit Data Analytics (ADA)?

Classical analytical procedures consist of absolute comparisons of balances with prior year balances or with budgets and forecasts, ratio comparisons and trend analyses. They may also consist of comparisons based on financial or operational data designed to predict the balance in a financial statement classification and form part of the audit judgment process by challenging financial information or the lack of such information.

Audit data analytics is much broader and deeper than traditional analytical procedures. It involves using powerful software tools and statistically complex procedures. These can include: cluster analysis; predictive models; data layering; visualizations; and “what if” scenarios that allow the exploration of new ways to analyse large sets of audit relevant data sourced from internal and external sources in order to produce audit evidence during risk assessment, analytical procedures, substantive procedures and control testing.

What benefits do ADA bring to auditors?

The advances in technologies and software solutions in ADA will enable auditors to improve audit quality in a number of ways, including:

  • deepening the auditor’s understanding of the entity;
  • facilitating the focus of audit testing on the areas of highest risk through stratification of large populations;
  • aiding the exercise of professional skepticism;
  • improving consistency and central oversight in group audits;
  • enabling the auditor to perform tests on large or complex datasets where a manual approach would not be feasible;
  • improving audit efficiency;
  • identifying instances of fraud; and
  • enhancing communications with audit committees.

How ADA can enhance audit quality?

ADA techniques and methods enable audit teams to start analysing client data early in the audit process and begin identifying areas that need further investigation. This enables problems to be identified as early as possible, and audit teams can tailor the audit approach to deliver a more relevant audit by adapting their audit plans accordingly.

ADA can be used to evaluate and assess large volumes of information quickly and can result in better understanding the entity and its systems. This provides opportunity for auditors to make better informed risk assessments so that further audit procedures responsive to those risks are more focused and effective. Since more time is spent focusing on the areas where greater risk is detected, a better and more sophisticated risk analysis, fraud identification and monitoring is possible, enabling increased auditors’ focus.

ADA techniques can also enable auditors to perform more frequent testing at shorter intervals, rather than concentrating audit work around year-end. Engaging in continuous testing and monitoring of data again leads to better risk identification, more accurate control assessments, and more timely and relevant audit reporting.

What is important to ensure that ADA can be used successfully?

It is important to first confirm the feasibility of ADA in an audit. The following would need to be considered:

  • Availability of data
  • Transportability of data
  • The client’s data media and format
  • Costs of using ADA

Once the feasibility of ADA is confirmed the next steps would need to be taken:

  • Decide which areas will be subjected to ADA (amend audit plan accordingly)
  • Define the objectives of each procedure (what outcome is required?)
  • Involve the IT specialists (where necessary)
  • Identify the data fields that would be needed to produce the required reports
  • Discuss the objectives and benefits with the client and identify a primary client contact person that will assist the audit engagement team
  • Request the data files in an acceptable format
  • Perform a test run (where ADA is being used for the first time) to ensure that data is correct
  • Develop and run the test on the data (as developed to address audit objectives)
  • Inspect reports with test results and follow up on any exceptions
  • Evaluate the results and conclude
  • Communicate findings to the client in an understandable manner (charts and graphs with simple notes can be used)