Question

You are a member of the auditing staff of a medium-size CPA firm, Reid, Daniels, & Luke. One of the audit partners, Alan Daniels, has asked you to prepare a report addressing the topic of the use of data analytics technology both in external financial statement auditing and in internal auditing. He has heard that the use of such technology is revolutionizing how larger CPA firms conduct audits and does not want RD&L to fall behind. He is particularly interested in more fully understanding exactly what data analytics technology is and how RD&L can use it to provide better service to its clients. Mr. Daniels plans to circulate your report to all partners and managers in the firm. Write a report for Mr. Daniels addressing his concerns.

Answer 1

While the profession has long recognized the impact of data analysis on enhancing the quality and relevance of the audit, mainstream use of this technique has been hampered due to a lack of efficient technology solutions, problems with data capture and concerns about privacy. However, recent technology advancements in big data and analytics are providing an opportunity to rethink the way in which an audit is executed.

The transformed audit will expand beyond sample-based testing to include analysis of entire populations of audit-relevant data (transaction activity and master data from key business processes), using intelligent analytics to deliver a higher quality of audit evidence and more relevant business insights. Big data and analytics are enabling auditors to better identify financial reporting, fraud and operational business risks and tailor their approach to deliver a more relevant audit.

Auditor data analytics is about enhancing audit quality. There are different angles on what this means in practice but audit quality is a common objective of auditors, regulators and standard-setters alike. A high-quality, focused and effective audit is aligned with the way the audited entity manages its data and operations. Data analytics offers a practical way for auditors to manage some important aspects of IT systems in larger audits.

Following unique features of data analytics have the capacity, if used appropriately, to enhance audit quality significantly:

• the ability to graphically visualise results: data visualisation.

• sophistication, and the breadth of interrogation options; • ease of use by non-specialists

• scale and speed. Interviewees emphasise different elements of the list above when asked about how data analytics contributes to audit quality. For some, the sophistication of enquiries generated by high-quality visualisations has resulted in better quality explanations.

Data analytics has shrunk the haystacks and in future it’ll be about what you do with those needles when you’ve found them.’ Auditors can navigate much bigger external data sets much faster than before because the biggest recent advances have been in the interfaces between client and auditor systems, software and data ie, the interfaces that facilitate data extraction.
These interfaces enable auditors to run the routines not just as substantive procedures, as in the past, but earlier during the audit at the risk assessment stage in understanding processes, and in work on controls. Many analyses performed are not fundamentally different to those performed in the past but they are now more granular and applied more widely at the same time. For example, the risks highlighted by the journals dashboard noted above might enable auditors to drill down into further detail as part of the substantive testing, by performing routines that analyse large journals and unexpected users, for example, which in turn facilitate further investigation.s of

Benefits of Data Analytics in both external and internal audit canbe as follows:

• views on control gaps: if there are control deficiencies that are subsequently remediated are the outcomes as expected or hoped for? Have the blocks been removed? Is the right data released?
• quantification or measurement of the impact of manual interventions, control failures, the extent to which process is being applied and the consistency of controls application
• the root causes of exceptions
• internal benchmarking
• visualisation, often through dashboards.