The application of data analytics in internal audit encompasses the use of software to identify significant trends and exceptions in large amounts of data. Such software can be used for basic data analysis, through to complex data interrogation across billions of transactions, as well as assessing control performance and exception reporting among other applications.
Many internal audit teams still rely primarily on spreadsheet-based tools and applications rather than more sophisticated analytics and data mining tools. A recent Deloitte survey showed that only around one-third of HIAs use data analytics at an intermediate or advanced level. The remaining two-thirds of HIAs use basic, ad hoc analytics or no analytics at all.
It is important to compare the different sets of analytic tools to determine what works best for the internal audit function. The range of tools includes the following: Desktop tools, e.g. Most organizations have this tool and its use for data analytics within internal audit is widespread; Specialized tools, e.g. These enable a wider range of tasks such as infographics and are compatible with usage in other parts of the organization; Audit specific tools, e.g. These tools can be used by audit functions, but users need some data science skills and knowledge e.g., scripting.
Internal auditors use different types of data analytics to undertake a variety of functions. In internal audit functions where there is currently no, or limited use of data analytics internal auditors will test samples of transactions. Only very rarely would they examine every transaction in the period audited, i.e., if fraud or other financial irregularity was suspected then internal audit will test 100% of transactions. Instead, internal auditors often turned to statistical sampling to extrapolate the number of errors in the total population and to determine the accuracy, or otherwise, of transactions.
Credit: Data analytics – is it time to take the first step? by Chartered Institute of Internal Auditors (April 2017)