For internal audit teams, data analytics has been touted as a superpower: It speeds up calculations and computations, improves accuracy and enables organizations to tackle massive data sets and spot red flags in a single click.
Data analytics is becoming necessary for chief audit executives (CAEs) and their teams. As organizational risk profiles become more complex and challenging to manage and oversight expands beyond compliance and internal controls, internal auditors are pressed to use data analytics and other new tools to drive strategic decisions.
But the prospect of bringing on data analytics expertise can be daunting, especially given today’s focus on efficiency and cost-effectiveness. Training existing staff in this highly specialized and complex area takes time. Hiring in-demand data analytics professionals can be pricey or even impossible for audit departments without the budget for an increased headcount.
This is where another tool with superhero potential comes in: automation. Automation streamlines time-consuming and repetitive processes, like cross-referencing or copying and pasting data between applications. Moreover, these software robots are easy to configure without extensive IT knowledge. PwC estimates that nearly half (45%) of tasks in today’s workforce have the potential to be automated.
What is “Data Analytics Automation in Internal Audit”
Data Analytics Automation in Internal Audit refers to using automated tools and technologies to streamline and enhance the data analytics process within the internal audit function of an organization. It involves leveraging software robots or artificial intelligence to perform data gathering, data cleansing, data processing, controls testing, and generating insights, among other tasks, with minimal human intervention.
With people and processes as the foundation, automation provides internal audit with the acceleration it needs to be more innovative and effective.
Read on for four ways automation can help internal audit teams integrate data analytics into their operations efficiently and cost-effectively.
4 automated robot-ready data analytics activities for internal audit teams
Opportunities for automation exist throughout the entire analytics lifecycle. If one were to write a job description for a data analytics robot within an internal audit team, tasks might include:
1. Automatically gather and clean data for internal audits
Collecting and inputting data, validating the completeness of fields, and checking for duplications are the bread and butter of data analytics, comprising up to 80% of the work. Such tasks are slow and prone to error, not to mention tedious, when conducted via spreadsheets, emails and manual methods.
Robots transform this scenario. They can extract and process large volumes of data and high-volume transactions faster and more efficiently, freeing audit and analytics teams up for higher-value work. Furthermore, by removing the risk of human error, robots increase the quality and accuracy of data. This raises stakeholders’ confidence in an audit team’s findings and recommendations.
2. Streamlining and strengthening internal controls testing
Always a data-intensive activity, the vast and growing array of areas that internal auditors oversee and analyze, Sarbanes–Oxley Act (SOX), anti-money laundering (AML) ML, accounts payable, general ledger and beyond, has made controls testing a never-ending task.
Robots push this burdensome boulder firmly up the hill and keep it there, with their ability to process more, faster, than humanly possible. With pre-built scripts, analytics and testing ideas stored in a central library, automated testing workflows add efficiency to the sampling and controls testing process. Furthermore, robots can test entire populations of data. Manual control testing, by contrast, is typical to mere samples. More data analyzed means greater confidence in the results.
As an added benefit, automated testing enables continuous monitoring. Audit teams can move from periodic snapshots of their operations to round-the-clock oversight, enhancing their ability to keep on top of issues and serve as trusted advisors.
3. Accelerating internal audit reports and remediation with automated data analytics
At the end of the data analytics process, automation enables swifter sharing of results and more immediate action when needed and offers significant time savings for executive and board presentations. For example, reporting that used to take two days to complete using manual processes might now only take five minutes.
On the remediation side of things, automated notifications can alert CAEs and key stakeholders when critical thresholds have been reached, and automated remediation workflows can address issues like internal control failures in real-time.
4. Supporting internal audit’s shift to an advisory role involves automated data analytics
Looking ahead to the audit team’s expanding role, automation tools like robotics offer CAEs superpowers in two critical areas: predictive and prescriptive analytics.
Automation-powered risk assessments are one example. Here robots use predefined rules, data points and trends to classify risks. This enables internal audit teams to identify high-priority areas more quickly.
Then what? For the next step, internal audit teams can add machine learning for prescriptive analysis, diving into the data in an even more advanced fashion to unearth questions to ask and optimal courses of action.
Don’t be the Chief Audit Executive that doesn’t start a data analytics automation journey
CAEs can no longer wait to implement automated data analytics in their internal audit team. “Effective data analysis must lie at the heart of internal audits if they are to remain relevant to stakeholders,” the Institute of Chartered Accountants of England and Wales declares.
Those harnessing the power of robotics and other automation technologies are already reaping the benefits. “IA departments, large and small, have already begun their journey into the world of automation by expanding their use of traditional analytics to include predictive models, RPA, and cognitive intelligence (CI),” writes Deloitte. “This leads to quality enhancements, risk reductions and time savings — not to mention increased risk intelligence.”
Start your data analytics automation for your internal audit journey today. By automating time-consuming tasks, ACL Analytics from Diligent helps internal audit teams augment their capacity without increasing their headcount. Get in touch, and one of our specialists will provide a personalized demonstration.