Guide to Data Analytics in Internal Audit

Speaker A: Welcome back to the Risk Advisory Podcast. I'm, um, Ann Blair Jamison, a Senior manager in the Risk Advisory Practice at Cherry Becker, and I'm joined by my colleague Yani Diaz, a Risk Advisory Director. Today we are excited to discuss Data analytics in Internal Audit. This is the third episode in our series celebrating IIA's internal audit awareness Month. Make sure to check out the first two episodes in this series at uh cbh.com podcast in today's rapidly evolving business landscape, data analytics has emerged as a powerful tool that can transform the way internal audit teams work and conduct testing within their company. Both public and private companies stand to benefit immensely from incorporating data analytics into their internal audit processes. Today we'll focus on some of the key benefits of implementing data analytics into your internal audit processes and some ways that Cherry Becker can assist with this implementation. Yanni, thanks for joining me for Part three.

Speaker B: Happy to be here to kick us off.

Speaker A: Can you give us a brief overview of data analytics for those who might not know?

Speaker B: Sure. Data analytics is the process of sorting through and examining all the pieces of data or information provided to find patterns, trends, or important details to help us understand the bigger picture. It's the process of turning raw numbers and information into something useful and understandable which will help people make better decisions.

Speaker A: That's a really helpful explanation, Yoni. We've seen that organizations are increasingly recognizing the importance of data analytics and enhancing their operations. In your opinion, what are some key benefits of using data analytics in internal audit business process testing?

Speaker B: Sure, there are a few of them. So first of all, data analytics allows internal auditors to analyze vast amounts of data various very quickly and also very accurately. This leads to more effective risk assessments. By identifying patterns and anomalies, auditors can then pinpoint areas of potential risk and inefficiencies within business processes, which then helps guide where they want to focus their attention. Secondly, the use of data analytics streamlines the internal audit process, reducing the time and resources required for manual data analysis. This efficiency gain reduces the risk of human error and it allows the business to spend more time focusing on higher value activities such as strategic planning and even decision making.

Speaker A: Wow, that's great. I could see how that would be really helpful with leadership in their business making decisions. Any other benefits in using data analytics for internal audit business process testing?

Speaker B: Absolutely. As a final thought, predictive analytics can also forecast future trends and identify potential issues before they become problematic. This proactive approach enables companies to take preemptive measures and mitigate risks, enhancing their overall business performance.

Speaker A: Those are all great points. Especially with the shift of internal auditors to advisors, the ability to use data for predictive analysis and giving suggestions to improve business operations is going to be extremely beneficial. Now, let's shift gears a bit. Are there areas on the ITGC side where data analytics can play a role in internal audit testing as well?

Speaker B: Absolutely. Data analytics can really make a difference in improving ITGC Internal audit Testing Awesome.

Speaker A: What do you see as some of the key benefits to using data analytics within an organization's internal audit IPGC testing?

Speaker B: Well, there are a few. Quite a few actually. Data analytics helps in monitoring and analyzing IT systems for any unusual activities and for potential security breaches. By identifying vulnerabilities in real time, companies can then respond swiftly to prevent data loss and ensure compliance with regulatory requirements. Internal auditors can also leverage data analytics to automate their control testing, ensuring that IT controls are functioning as intended throughout the year. And if you add automation that enhances the accuracy and reliability of the control assessments, leading to better compliance and governance overall. Most analytics software allows for automatic generation of reports and for notifications to key stakeholders as well, so that prompt mitigating actions can be taken as opposed to discovering unintended vulnerabilities much later down the line. And lastly, data analytics provides comprehensive insights into IT operations, helping companies to improve governance and oversight. By analyzing data from various IT systems, auditors can identify inefficiencies and recommend improvements, ensuring that IT resources are best utilized and allowing leadership to better focus resources and attention on the higher risk areas.

Speaker A: Wow, that was a lot to process. Do you maybe have an example of one of those benefits you could share with us?

Speaker B: Yes, absolutely. So I once helped a client who wanted to automate some of their testing around credit card transactions. They basically had concur and they wanted to check that uh, people were following the policy, not skirting the rules by splitting the same expense into smaller pieces to avoid the expense limit. So I did help them with the tool that used audit command language to implement an automated script that would look for scan all transactions and then look for the specific rules that we that we told the system to look for with the specific limits, matching the dates and the amounts, summing them together to see if there were any potential violations of the policy, and then that script would automatically send a report for review by one of the auditors, which saved them tons of time with data collection and analysis and then allowed them to only look at the relevant potential infractions.

Speaker A: That's great Yoni, thanks for sharing. For organizations who are excited about the benefits of using data analytics but don't quite know where to start. What are some important considerations or first steps?

Speaker B: Uh, absolutely. If you're thinking about setting up, say, a brand new analytics program and you don't know where to start, it's important to kind of consider tailoring solutions to meet the unique needs of your organization as a first step. So looking beyond even the internal audit landscape, aim to have a well defined strategy and then take your time to select the right tool before spending any money. By understanding the specific requirements and challenges faced by your company and by speaking to leadership, you can understand what are the needs of the organization as a whole and develop a customized strategy to improve the efficiency and productivity of the analytics program for your organization, thereby adding value beyond the internal audit function and selecting a tool that can serve various functions across the organization from pooled data source. Other departments can then easily piggyback off of Internal Audit's data analytics program as a first launch. That then leads to additional value for other functional leaders. This makes it very scale, very scalable. It's also critical to provide comprehensive training and ongoing support so not only to think about which tool to select and, and um, which departments it can serve, but then also once the tool is selected, really plan for training and supporting those that need to adopt the tool to avoid common roadblocks to implementation. And then lastly, I'd say to be sure to work with your IT and change management teams during the implementation process to ensure seamless integration between the selected tool and your existing systems, applications and infrastructure components. It can be quite overwhelming to coordinate a new data analytics program, so if you have questions during the process, feel free to reach out. Jerry Beckert has experienced professionals who assist clients with integrating analytics tools or selecting tools when they're first launching their programs, and we can help every step of the process from picking the tool to implementing and then training the folks who are going to use it. Post Go Live. This helps our clients gain easier access without disrupting their operations or straining their existing resources.

Speaker A: Thank you Yani for sharing those valuable insights and knowledge on how data analytics can be utilized to empower companies to more efficiently and effectively conduct their internal audit testing. Looking Ahead we're happy to announce the final episode in our series where we will explore how to audit artificial intelligence. Stay tuned for this insightful discussion that will help you stay ahead of the field of risk advisory and internal audit. As always, please like share and subscribe to the Risk Advisory podcast. Thanks again for listening and we look forward to having you join us on our next episode.