Data analytics is a great way for auditing that lets any audit team identify risks better and strengthen the overall audit methodology that they use. It usually seems that the most technologically leading auditors can apply data analytics for auditing. But analytics software and their data analytics tools for internal audit make it easier for all types of auditors and assurance professionals to conduct deeper AI data analysis.

What Does It Mean by Data Analytics in Auditing?

Data analytics when used in auditing is called audit analytics or data audit analytics. Generally, it means the intelligence that is generated from reviewing audit-related information, often through technological use. And just like all the other kinds of data analytics, data audit analytics also typically involves analyzing large sets of numbers or texts (depending on the study) to identify actionable audit insights.

Who Can Use Audit Analytics?

Internal audit teams of every kind, from large enterprises to public sector organizations can use audit analytics and improve their audit analytics. And external auditors such as the ones that work at tax firms and accounting organizations can also use audit analytics to improve the efficiency and quality of their audits.

Did you know that the Touchstone Insights for Internal Audit study by Wolters Kluwer state that, 88% of audit teams either plan to or are already in the process to use audit analytics as part of each audit? 

When Can Audit Analytics Be Used?

Audit analytics can essentially be used at any stage of the audit methodology and across all auditing procedures such as Benford’s testing, gap and duplicate detection, Monetary Unit Sampling, and stratification. A useful scenario to understand this is using software platforms to automate the audit analytics process. For eg. by scanning thousands of invoices to detect suspicious activity, auditors can now catch risks that they may have missed otherwise.

Using audit analytics also enables internal audits to perform more streamlined, actionable reports for management. The data analytics platform is useful not only just uncovering audit findings but also for reporting insights through charts and other various data visualizations using the data analytics tools for internal audits. Thus, it becomes easier to manage audit reports.

What is The Advantage of Using Audit Analytics?

Audit analytics enables several benefits such as improving risk management, providing greater assurance, and overall helping auditors gain more confidence in their audit findings. Additionally, audit analytics also adds clarity to audit reports and is helpful for both internal and external auditors in presenting their findings to those outside the process of auditing itself.


5 Key Advantages of Using Audit Analytics For Internal Audits


Better Risk Management

One of the main benefits of using audit analytics for internal audits is the improvement in risk management throughout the organization. Trying to review all data manually is not feasible and when auditors use limited data sampling methods to compensate for information overload, risk management gaps are evident. Even with a full set of data, the same can repeat. Audit analytics for internal audits can spot and understand these risks by reviewing large quantities of data. iCCM helps you achieve this by enabling you to monitor your critical processes at all times regardless of where you are. It also allows you to manage and internally audit your organizational processes, ensuring that they meet their targets for performance and effectiveness.

Greater Assurance

Another benefit of audit analytics for internal audits is the ability to provide greater assurance when it comes to risk management. Audit analytics can provide a more systematic, complete review of business processes between different departments of an organization. Audit analytics software makes it easier for organizations to visualize and compare results. On the contrary, sampling and other more manual, limited processes may restrict the transparency of mistakes when committed and provide wrong results.

Enhanced Efficiency

Audit analytics also saves a lot of time for organizations when performing audits. Audit analytics software like iCCM can review thousands of rows of data instantly. Altogether, an internal auditor can use audit analytics and data analytics tools for internal audits to improve the efficiency of conducting, planning, and presenting audits.

Clearer Reporting

Using audit analytics software helps create data visualizations like charts and graphs for audit functions that are better for communicating audit findings. In contrast to that, traditional methods would require internal auditors to communicate via lengthy tables and wordy explanations that require more explanation. But with data analytics tools for internal audits are great for creating clearer reports for management, audit committee, or other stakeholders, to help them get the most out of audit presentations.

Improved Audit Quality

Overall, audit analytics improves the quality of auditing at each stage of the audit process, and leads to an improved quality of final auditing. To be very specific, data audit analytics can be used to conduct audit procedures in a much more systematic and efficient manner.


Why Choose IntoneSwift?


Data analytics tools for internal audits can directly interact with systems and extract data. They are also able to allow every transaction and balance to be analyzed and reported. The growth of computerization and the increased volumes of transactions have moved audits away from traditional methods to effective ones. 

Intone has stepped up to meet this rising demand and is proud to present Intone Data Integrator. Intone Data Integrator is a state-of-the-art data integration solution that is trusted by industry pioneers and leaders. We offer,


  • Generates knowledge graph for all data integrations done
  • 600+ Data, Application, and device connectors
  • A graphical no-code low-code platform.
  • Distributed In-memory operations that give 10X speed in data operations
  • Attribute level lineage capturing at every data integration map
  • Data encryption at every stage
  • Centralized password and connection management
  • Real-time, streaming & batch processing of data
  • Supports unlimited heterogeneous data source combinations
  • Eye-catching monitoring module that gives real-time updates