Audit data analytics are particularly useful at the risk assessment stage of an audit, but should not be used to identify exceptions for further examination during substantive testing.

Question

BlackTom AI · Accepted Answer

The question presents a statement about audit data analytics and asks whether it is true or false. 
Option True: The claim that analytics are only useful in risk assessment and should not be used to identify exceptions during substantive testing is misleading. While analytics are indeed valuable in risk assessment to identify areas of potential risk, they are also commonly used during substantive testing to detect anomalies, outliers, or patterns that merit further examination. This makes the blanket claim in option True incorrect. 
Option False: This option correctly challenges the idea that analytics have utility solely at risk assessment. In practice, audit data analytics serve multiple purposes across the engagement life cycle: they help with risk assessment, planning, and specifically identifying exceptions or anomalies during substantive testing that require follow-up, investigation, or additional evidence. Therefore, saying analytics should not be used to identify exceptions during substantive testing is inaccurate. 
In summary, the statement conflates the full utility of audit data analytics and overlooks their role in substantive testing, so the appropriate choice is False.

Audit data analytics are particularly useful at the risk assessment stage of an audit, but should not be used to identify exceptions for further examination during substantive testing.True/False

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