Driving Audit Quality Through Technology in Audit

In today's dynamic business landscape, the field of external auditing is experiencing a transformation driven by technology. While data analytics is a relatively new concept for auditors, its potential to enhance audit quality is undeniable. By harnessing specialised software and analytical techniques, auditors can examine vast datasets, draw valuable insights, and make informed decisions.

On this page:

What is data analytics?

Why should data analytics be used?

How can data analytics improve audit quality?

What are the challenges of data analytics?

What is data analytics?

Although data analytics is a relatively new concept for external auditors, it has been around in various forms for a long time. Data analytics is essentially examining the data available and drawing conclusions. This is enabled by specialised software and can require a substantial investment in skills and quality control.

The data analytics tools are applied to ‘full data sets’ i.e. complete populations, to support judgements or provide direction for further investigation. Data visualisation can be used, such as bar or pie charts to help users understand the data and hence it can be a powerful tool in the risk assessment process.

In the early days of auditing, the auditor would have followed all transactions and balances through the system, giving an exhaustive examination of every transaction and balance. In the 1950s this approach was questioned and the use of risk analysis, controls testing, sampling and flowcharts became common practice.

Data analytics challenges the way an audit is commonly performed, and enables an auditor to step back and see ‘the bigger picture’ more easily than use of controls testing and substantive testing in isolation.

Why should data analytics be used?

Data analytics allows an increased access to and manipulation of data. The tools can be used in risk analysis, transaction and controls testing, analytical procedures, and in support of judgements.

Examples of the use of data analytics to perform audit procedures include:

• The use of heat maps to identify outliers in populations;
• Matching purchase orders to invoices and payments;
• Extraction of journals presenting a higher risk, such as those posted on a weekend;
• Matching of purchase orders to invoices and payments.

There are many benefits of the use of data analytics, including;

• An increased business understanding as a result of the ability to visualise’ client data, rather than relying on text or numerical information alone;
• Identification of appropriate risks which enables testing to be directed to those audit areas;
• Data can be more easily manipulated by the auditor as part of audit testing;
• Increased fraud detection through the ability to interrogate all data and to test segregation of duties; and
• Added value to clients as information obtained can be shared.

How can data analytics improve audit quality?

Data analytics can have a positive impact on audit quality, although this is not direct from the tools. The increase in audit quality is a result of the conversations and enquiries that the data analytics prompts.

There are several unique features of data analytics which can enhance audit quality significantly:

• Data visualisation allows graphical visualisation of data previously presented numerically;
• Data analytics offers a breadth of interrogation options;
• Use by the auditor is easy and often independent of management; and
• The scale and speed of interrogation can drive audit efficiency.

What are the challenges of data analytics?

Although a powerful tool, data analytics does present challenges. There is currently no regulation or guidance which covers all the uses of data analytics within an audit, as the auditing standards are written on the assumption that 100% testing is rarely achievable.

Other challenges include:

• The copying and storage of client data increases the risk of breaches of confidentiality and data protection laws as the data stored is more detailed. There is also a practical implication as the sheer volume of data to be retained to support each audit could easily go beyond the capacity of standard servers;
• Businesses may be reluctant to give the auditor access to data, either due to data security or due to a perceived disruption to the business;
• Completeness and integrity of the extracted data may not be guaranteed, especially where multiple data systems are used by the client;
• Audit staff may not be competent to understand the output of the data analytics tools, and hence there will be a need to invest in training.

Final Thought

Data analytics tools may allow the auditor to test more transactions and balances, which may increase the chances of detection of certain frauds.

It is unable to predict the future, however, and hence there is still a need for the auditor to assess judgements and the future of the client. It is therefore a useful tool, however computers alone are not able to fully replace an auditor.