How Analytics Deliver the Whole Audit Picture

By Mike Martin


Internal auditors have relied on statistical sampling to draw conclusions about large data sets for decades. Sampling has been widely adopted because it allows auditors to gather enough evidence to form an opinion and detect errors or potential fraud without spending the resources required to sift through massive amounts of transactions and information. 


But sampling can be flawed because it assumes the sample data set is representative of the entire data pool, which is not always the case. Errors caused by sample size, design or unexpected variables are a risk.


Today, internal auditors seeking to deliver higher quality audits don’t need to worry about taking inordinate amounts of time to examine huge numbers of financial transactions, or designing the perfect sample data set to detect trends or anomalies. 


Instead, they can use data analytics software to paint a complete and accurate picture of all their organization’s transactions and processes, allowing them to more accurately identify risks, trends and potential fraudulent activity. 


“Analytics and full populations really is the future of internal audit,” says Harold Silverman, Director of Executive Membership for The Institute of Internal Auditors (IIA), an international professional association that advocates, educates and provides standards, guidance and certifications for internal auditors. 


“It’s required for a truly mature internal audit function, because you get better audits, and the results of your audits resonate better with your key stakeholders.”


Internal auditors are increasingly recognizing the importance of incorporating analytics into their audits, Silverman notes. In an upcoming IIA survey titled, The North American Pulse of Internal Audit, the association asked respondents how they would spend any unexpected budget increase. The second-highest result, after additional staff, was technology. Of those who selected technology, 68 percent said they would spend the budget on data analytics software. 


“To me that’s indicative of two things,” Silverman says. “The first is a recognition, even by those who don’t have data analytics software, that it’s something they want to have. The second is there aren’t enough auditors out there with analytics software because if they already had it, they’re not going to spend additional budget on it.”


Benefits of Analytics Software


Analytics software delivers multiple benefits to internal auditors seeking deeper insights about their organization’s transactions and data. They include:


  • Testing complete data sets, allowing auditors to perform more thorough audits and more easily identify unusual data sets
  • Generating more granular results through the application of artificial intelligence and machine learning technology
  • Integrating data from multiple sources, enabling auditors to generate new insights
  • Creating data-based visualizations that make it simpler to spot patterns, trends and outliers
  • Building clear audit trails that can be analyzed at any time


While analytics software transforms some of the audit processes, it is a complement to, and not a replacement for, traditional auditing. It still requires a skilled and knowledgeable auditor to understand the context of the business processes, draw conclusions from the data and interpret the results for the executive team and board. 


Improving the Quality of Audit Evidence


How are financial statement auditors using analytics? According to a 2019 report titled An Inside Look at How Auditors in Canada Are Using Data Analytics, from the Chartered Professional Accountants of Canada (CPA Canada), auditors are using analytics with the primary goal of improving the quality of audit evidence obtained to support the auditor’s opinion.


The most common areas where participants applied analytics included:


  • Journal entry analysis to identify unusual attributes
  • Process mapping using transaction logs to identify, for example, missing steps or out-of-order processes
  • Two- and three-way matches of aspects of transaction streams such as payroll, or of aspects of one or more of purchases-payables-payments transaction streams
  • General ledger account balance analysis
  • Scanning data populations for various attributes (looking for large, unexpected items or duplicates)
  • Aging analysis (examining the length of time accounts receivable, accounts payable, loans receivable, etc. have been outstanding)
  • Churn analysis (e.g. changes in customers and amounts owed; changes in inventory items)


The report identified several benefits analytics delivered, including: improved identification and assessment of risks of material misstatement; providing management with useful insights, as the analytics allow auditors to look at entire data populations; and providing the audit committee with useful insights from the audit, while meeting stakeholder expectations. 


The report noted the use of analytics by auditors is growing and that many larger firms continue to make significant investments in both technology and the development of training and other forms of guidance. This indicates that analytics will be a key factor in enhancing the relevance and value of the financial statement audit and in continuing to improve audit quality. It concluded by encouraging auditors to continue learning about analytics, ensuring they have the necessary training to use analytics in their audits and to find new ways to leverage their capabilities. 


The IIA’s Silverman believes internal auditors need to begin working more with full data populations across all areas because the business leaders they report to are accustomed to working from complete data sets. 


“I think for internal auditors to be most relevant to their stakeholders – board members and senior executives – they need to be providing quick-turnaround, full-population data and assessments,” he said. “And that’s only really achieved through proper use of analytics and data.”

To discover how data analytics can improve your audit process, learn more about the capabilities of IDEA Data Analysis Software.