Audit Analytics

Audit Analytics is the science of analyzing an extensive data set to solve problems, identify risks in the company’s structure, and then build controls to mitigate those risks in the organization. It is a subset of data analytics with different objectives, scopes of applications, and intelligence.

Most audit analytics use raw information from multiple sources to evaluate different operations, structures, employee behavior, and performance. Technology, software, and online tools mainly do it, helping make sense of the data and offering insights.

• Audit analytics is the science of assessing companies and organizations to forecast potential risks and strategize to mitigate them.
• It uses technology, tools, and automated systems to check internal controls and structure to improve audit practices and quality.
• Many audit analytics courses are available in taxation, corporate governance, law, accounting, and tax management.
• Audit analytics allows the evaluation of an entire dataset rather than a sample, improving auditing quality.

Audit analytics is a branch of data analytics focused on inspection and accounting. It studies vast data sets to comprehend the results in meaningful information indicating potential risks, loopholes, and errors. At the same time, it helps build strategies, control systems, and structures to mitigate them. Therefore, it is convenient to say that all audit analytics are data analytics, but vice versa needs to be corrected.

As per the Audit Benchmark study, more than 88% of audit teams already use or plan to implement audit analytics in their procedures for better results and valuable insights. Thus, typical procedures like monetary unit sampling, Benford’s testing, gap and duplicate detection, and stratification are simple examples of audit analytics.

In addition, many online platforms and software use audit analytics databases to compile reports and offer uncovered audit findings. They are simple, and people with the background and technical skills for data science can quickly implement them to conduct audits and share results. Since many platforms and software are already available, analysts do not have to rely on manual work with automated and less time-consuming processes.

Most organizations prefer conducting internal auditing to ensure they don't involve any third parties or external audit teams. For this reason, in most cases, it becomes challenging to stop teams from reviewing confidential data related to company financials. As a result, over time, audits and data analytics have become an integral part of management structures and processes, as most decisions regarding operations and businesses are based on the results of these analyses. Therefore, many organizations often use automated programs and applications to reduce cost and time, which is essential for every business.

Here are some examples to better understand the concept:

Example #1

Suppose an accounting firm and the management have an internal team to perform an audit to spot anomalies in the transactions the firm managed for the last year, and the process starts. They use different audit analytics tools to evaluate each department, as well as financial statements, reports, and official documents.

The auditors might use technology, software, systems, and automation, but human effort still plays a vital role in devising the policies and making final decisions. Thus, upon completion, the auditor and crew submit a final report that helps the management with details on potential risks, errors, and performance of the company.

Audit analytics lies at the core of assessing large data sets and offers insights through which the company compares, makes decisions, formulates policies, and runs operations. Finally, the result presented different anomalies in front of the firm that may put the firm at risk or involve it in fraud and malice practices. Therefore, the whole data set proved beneficial and offered valuable insights to the firm.

Example #2

As per a report from Gartner on July 11, 2023, the primary focus of chief audit executives (CAEs) has been more on advanced applications of analytics like AI, automation, continuous risk assessment, etc. as it enables firms adopt advanced cyber security measures, robust software applications, critical transformation projects, and evolving IT practices to mitigate new IT risks, etc.

The report also added how challenging it has been for CAEs to focus on enhancing the use of data analytics and ensuring technology and digitalization for over a decade. The expectations from audit executives have led them face increased pressure to tackle challenges as organizations increase risk appetites and expand digital investments in the next two years.

The various benefits of using audit analytics are as follows:

• It focuses on performing audits to identify risks and build control systems.
• It helps in the swift assessment of large data sets and raw information
• With the help of audit analytics tools, management gets valuable insights, data charts, sheets, and understandable reports to study and make better business decisions.
• Audit analytics involves fraud, errors, mistakes, and inspection of systems and internal structures.
• With it, a company can assess its internal teams and mitigate risks.
• It provides better value for money, mainly when the timeframes are similar to those of traditional methods.

This article has been a guide to what is Audit Analytics. Here, we explain the concept in detail along with its examples and benefits. You may also find some useful articles here -

• Continuous Audit
• Brand Audit
• Best Auditing Books