What Is a Data-Driven Audit?
Data is the foundation of a data-driven audit. Auditors pull in client data, starting with the trial balance and general ledger, and leverage these to perform a robust data-driven risk assessment. That data, along with subledger and other sources, is used throughout the audit, tailoring the audit approach and audit programs using a methodology that enables auditors to choose the right procedures to address the identified and assessed risks.
It can be tough to determine where to focus time and resources in any audit. Auditors should already use a risk assessment to drive the procedures they perform, but this approach can have its limitations with traditional auditing methods.
First, auditors may not be able to identify or adequately assess all the risks that are present.
Second, they can struggle with designing or tailoring audit procedures to address those risks.
Third, audit procedures are still largely sample-based. Testing the bulk of transactions is cost- and time-prohibitive; auditors can’t manually review every transaction, receipt, or invoice.
As a result, auditors tend to:
Over-audit by performing substantive tests of detail on low-risk areas
Perform the same procedures that they did last year
Overlook misstatements that aren’t picked up by traditional sampling
In a data-driven audit, the data helps auditors to make decisions about which areas present the most significant risks by delving deeper into current year data and incorporating data from prior years and external benchmarks. For example, data analytics tools can help identify unusual spikes in activity, adverse longer-term trends, new transaction flows, etc. All this analysis can be completed in a fraction of the time that it would take the audit team to perform manually and presented in a way that makes them easy to interpret.