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Any financial audit is, at heart, the independent testing of a series of related financial assertions.  Underlying these tests is the need to gather evidence that is increasingly digital, growing in terms of volumes, velocities and variety and located in multiple and diverse systems. Increasingly, attention is being directed towards the value of data analytic technologies for improving audit decision making leading to increased audit performance and business improvement.

Broadly, audit analytics is about using technology and automation for data collection and simple and advanced analytics tools for monitoring and testing internal controls, transaction details and account balances against benchmarks. [1] The purpose of this article is to discuss applications of audit analytics and the implications for auditors as these tools have the potential to further enhance the delivery of audit services.

For further insight, be sure to download our guide on Audit Analytics for Finance, Audit & Risk Professionals.

Analytic enabled audits

Technology has put a range of tools and techniques in the hands of modern auditors, which previously would have been costly and complex.  For example, the cost of analytic procedures has been reported as USD 0.01 compared to a cost of USD 4.00 for a “standard audit of the same evidence.”[2] Audit analytics in the broader context of a continuous auditing paradigm is changing the nature, timing and extent of ‘traditional’ audit testing..[3] In a traditional audit, internal control and substantive testing are periodically performed to evaluate management assertions. This approach of controls reliance, supplemented by substantive testing, arose (broadly speaking) from risk and cost considerations over the past few decades. Prior to automation, external auditing involved audit clerks performing complete substantive testing.

The labour and time intensiveness of this approach was influential in the adoption of audit sampling and systems of controls reliance; each with inherent risks and limitations. The application of data analytic techniques to transaction details and account balances in a continuous audit context enables consideration of the whole population in monitoring and testing. Hence we are no longer limited by the technology but by our ability to interpret, manage and understand the data before us.


Risk Assessment of what to Analyse

Auditors should select the data analytics target areas based on a risk assessment.  The process of assessing risk for a financial audit is well known.  What is suggested is that audit analytics should assume a higher profile in the selection of audit procedures.  However, this strategy has its own challenges as additional planning would be required to properly implement such a change in audit strategy.  This is required due to issues such as data availability, completeness, and data management independence.  If these issues can be managed then data analytics can deliver reductions in audit risk.

A range of potential tests.

These tests include those designed to identify fraud indicators and statutory non-compliance.  Some of these tests can be used for “value added” service work for clients.   However, many of these tests, unless well written can produce “telephone book” outcomes (with many false positives) that are unusable for practical purposes.  Thus is important to understand not just the data but the output of the tests performed.

It is often suggested, why test the data when “there are controls in place to prevent exceptions”.  The issue here is that controls tend to reduce the probability of adverse events, however, controls do not guarantee that they will not occur.

“The truth lies in the transaction, not just in the control”.

Part 2 of the article focuses on examples and Practical applications and implications for auditors. Read part 2 here.


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[1] Rutgers Accounting Web

[2] ibid

[3] Vasarhelyi, M.A., Alles, M. and Williams, K.T. “Continuous Assurance for the Now Economy, The Institute of Chartered Accountants in Australia. Retrieved from




Author Bio

Gavin Steinberg is the CEO of Satori Group and industry expert in Data Analytics, Budgeting, Forecasting and Financial Consolidation, and Continuous Control Monitoring. Gavin’s passion is helping companies to see the value that can be achieved through automation, understanding their data and bringing this to life through visual communication and assurance.