Traditional Internal Audit practices relied on outdated sample testing, where manual tests were applied to a small subset of data from a large population. While this method was deemed acceptable in the past, evolving organizational risks have rendered it inadequate and in need of an upgrade.
Despite the exponential growth in data to be audited, many companies persist in using outdated tools and techniques that have remained unchanged for decades. Moreover, auditors often face risks in businesses that were unknown just a few years ago.
At Ingrity, our data analytics team specializes in enhancing Internal Audit capabilities by incorporating modern data analytics techniques into our clients’ audit processes. This empowers auditors to test the entire population of data against key risks, rather than relying on small samples. Furthermore, this approach facilitates quantifying the magnitude of exceptions identified.
For our client, we conducted an audit of over 2 million payments to verify if they were authorized by individuals with appropriate authority. Through this comprehensive testing, we identified 114 exceptions resulting from four distinct gaps in the payment process.
By implementing our data analytics support, the client’s Internal Audit team was able to test the entire population of data, providing greater assurance compared to the outdated practice of sample testing. This shift in approach enabled the identification of specific gaps and exceptions that would have gone undetected using less sophisticated methods.
The use of advanced technology and modern data analytics techniques in Internal Audit significantly enhanced the effectiveness of the client’s audit processes. By embracing a data-driven approach, the client experienced improved risk identification, stronger control measures, and enhanced overall governance. This, in turn, led to increased confidence in the audit results and a more robust risk management framework for the organization.
