Protiviti Says, Data Analytics Is a Game Changer, but Internal Audit Groups Are Lagging

Survey Reveals Internal Audit Functions Need to Embrace Data Analytics Quickly to Keep up With Businesses That Are Increasingly Data-Driven

According to findings from global consulting firm Protiviti‘s 2018 Internal Audit Capabilities and Needs Survey, the use of analytics in auditing remains in the early stages, and many audit functions are likely using analytics tools as point solutions, as opposed to part of a broader initiative to leverage analytics throughout the audit process.

The global survey polled over 1,500 Chief Audit Executives (CAE) and internal audit leaders and professionals throughout North America, Europe, Asia-Pacific, Latin America, Africa, India and the Middle East to determine how internal audit groups are leveraging analytics in the audit process and where improvements are needed.

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Brian Christensen, Executive Vice President, Global Internal Audit, Protiviti, said, “Growing demands from boards and executive management for deeper insights into strategic risks that organizations face has made the ability to leverage analytics and robotics a top priority for CAEs and audit committees. Even so, many internal audit departments are still struggling to develop a formal methodology for integrating data analytics. It is critical for these departments to formalize a data analytics program that specifies how data is to be identified, acquired and analyzed.”

Increasing Data Analytics Capabilities

The survey found that organizations in Europe and the Asia-Pacific region appear to be utilizing data analytics in the audit process more frequently, with 76 percent of respondents from both regions confirming they currently use data analytics, compared to only 63 percent from North America. Two-thirds of internal audit groups plan to implement data analytics within the next one or two years (66 percent), yet one in three organizations (34 percent) have no plans to do so.

“Internal audit groups continue to face a lack of skills in understanding and using analytics technologies. Chief audit executives need to focus on increasing the levels of education in their internal audit functions, and more specifically, to move from general plans and discussions about using analytics to actually advancing and integrating analytics, robotic process automation and other digital initiatives into the audit plan. Those who fail to integrate these initiatives risk becoming obsolete as their organizations continue to undergo a digital transformation at an increasingly rapid pace,” added Christensen.

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The survey also reveals a clear correlation between the audit committee’s level of interest in the use of analytics to support the auditing lifecycle and the amount of information shared with the committee about the use of auditing analytics. Fifty-nine percent of respondents agreed that when a high level of information is shared with the audit committee, there is also a high level of interest from the audit committee in the use of audit analytics.

“The use of analytics and the benefits they deliver should be communicated to the audit committee regularly. By better informing and educating the audit committee about analytics, CAEs will be far more likely to gain the committee’s support for further investments in these capabilities,” said Andrew Struthers-Kennedy, Managing Director, Protiviti Internal Audit Practice.

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Data Analytics Action Items

The survey report, entitled “Analytics in Auditing Is a Game Changer,” outlines action items for CAEs and audit committees to improve their analytics capabilities:

• Recognize that the demand for data analytics in internal auditing is growing across all organizations and industries.
• Seek out opportunities to expand internal audit’s knowledge of sophisticated data analytics capabilities.
• Consider the use of champions to lead the analytics effort and, when appropriate, create a dedicated analytics function.
• Explore avenues to expand internal audit’s access to quality data and implement protocols that govern the extraction of data used during the audit process.
• Implement steps to measure the success of your data analytics efforts and also consider the most effective ways to report success and value to management and other key stakeholders.

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