Using internal audit analytics can be a great way for an audit team to better identify risks and strengthen their overall audit methodology. And while internal audit analytics may seem like something reserved for only the most technical audit leaders, analytics software increasingly makes it easier for all types of auditors and assurance professionals to conduct deeper data analysis.
What is audit analytics?
Audit analytics, or audit data analytics, means the intelligence generated from reviewing audit-related information, often through the use of technology. Like other types of data analytics, audit analytics typically involve analyzing large sets of numbers (but could involve text) to find actionable audit insights.
What is the difference between data analytics and audit analytics?
Audit analytics is a subset of data analytics. All audit analytics are data analytics, but not all data analytics are audit analytics. Put another way, audit analytics are audit-related data analytics, but data analytics can also apply to other business functions.
As Experis Finance explains in a report published by The Institute of Internal Auditors (IIA), the broader category of data analytics means: “The process of inspecting, cleansing, transforming and modeling data with the objective of highlighting meaningful information, suggesting conclusions, and supporting decision making.”
Who uses audit analytics?
Internal audit teams at everywhere from large enterprises to public sector organizations can use audit analytics to improve their audit activities. In fact, 88% of audit teams either plan to or are already in the process of using audit analytics as part of every audit, according to our TeamMate Audit Benchmark study.
External auditors, such as those at tax firms and accounting organizations, can also use audit analytics to improve the efficiency and quality of their audits.
When can audit analytics be used?
Audit analytics can be used at essentially any stage of an audit methodology and across audit procedures like Benford’s testing, stratification, Monetary Unit Sampling, and gap and duplicate detection. For example, audit analytics can be used to spot anomalies across large quantities of transaction reports.
Especially when using software platforms to automate audit analytics processes, e.g., scanning thousands of invoices to detect suspicious activity, auditors can catch risks they may have otherwise missed.
Internal audit analytics can also be used by internal audit to compile more streamlined, actionable reports for management. Data analytics platforms can be used to not only uncover audit findings but also report insights through charts and other types of data visualizations. This reporting then makes it easier for management to digest audit reports.
What are the advantages of audit analytics?
Audit analytics can provide several benefits such as improving risk management, providing greater assurance and overall helping auditors gain more confidence in their audit findings. Plus, audit analytics can add clarity to audit reports to help both external and internal auditors present their findings to those outside the audit process.
How to implement audit analytics
While audit analytics can provide many powerful benefits, implementing them doesn’t have to be overly complex. Those with data science and related technical skills may be able to conduct their own analyses and share audit analytics, but audit teams don’t have to just rely on these specialists. Increasingly, software tools make it possible for any member of an audit team to analyze data and generate internal audit analytics.
In fact, our own data analytics show that audit teams who use TeamMate Analytics find it more feasible for more audit team members to generate some audit analytics. TeamMate Analytics runs on Microsoft Excel and can be used by both external audit firms and internal audit teams to easily generate audit analytics.
See how TeamMate Analytics can change the way you think about internal audit analytics. View a demo today.