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The IAASB has considered a number of important circumstances and factors that exist in the current business environment that impact the use of data analytics in a financial statement audit.
While considering the relevance and reliability of external data, the standards identify third party evidence as being a better source of evidence than an inquiry of management or an observation of the application of a control. On one level, this assumption might well be worth revisiting. Regardless of where the data originates from, data should require validation of completeness, accuracy and reliability.
It is important to prioritise the standard-setting challenges; the IAASB will not be able to find solutions to all the challenges in the short-term. For instance, for many firms the main priorities will be for the IAASB to provide guidance on how to deal with outliers or how to confirm that the population is in fact 100 percent. Equally, practitioners question the fundamentals of what kind of audit evidence data analysis provides and how underlying principles such as inherent and control risks are affected.
It is also expected that the issue will become more acute as the nature and extent of testing based on data analytics increases and becomes more complex because the amounts of data rapidly increases.
Accountancy Europe agrees that the journey is evolutionary and that audit data analytics is still a 'work-in-progress’. It is though being used in earnest right now. There are already major questions arising, some of these such as whether the risk-based approach is still valid, whether risk assessment is now so precise and comprehensive as to effectively giving some assurance, and how the substantive analytical procedures can be adapted to recognise an iterative approach. These questions are all so fundamental to the foundational premise of ISA that Accountancy Europe encourages early consideration.