How audit data analytics is changing audit

Traditional audit methods involve obtaining a small sample of data and extrapolating the results to identify anomalies for investigation. In contrast, data analytics enables auditors to work with 100% of the transactions within a population of data. Using data analytics, auditors can quickly see the patterns and connections in vast amounts of data, present the findings graphically, and pinpoint high-risk areas for further audit testing.

While larger corporations have been using data analytics as part of their governance, risk and control monitoring systems for some time, it is a relatively new discipline for smaller company audits.

Data analytics enables auditors to improve the risk assessment process, substantive procedures and tests of controls. Building on internal financial data analysis, analytics tools can also draw on external data such as third-party pricing sources, commercial interest rates and foreign exchange movements. Used correctly, these results can provide further evidence to assist with audit judgements and provide greater insights for audit clients.

Data analytics involves the extraction of data using fields within the basic data structure, rather than the existing format of accounting records. Auditors then filter, sort, slice and highlight data, and present it visually in a variety of bubble, bar and pie charts.

By applying data analytics procedures, auditors can produce high-quality, statistical projections that help them understand and identify risks relating to the frequency and value of accounting transactions. Some of these procedures are simple, others involve complex models. Auditors using these models will exercise professional judgement to determine mathematical and statistical patterns, helping them identify exceptions for extended testing.

Auditors commonly use data analytics procedures to examine:

Analyses performed with audit data analytics are more granular, applied more widely and much faster. This allows auditors to spend more time on the things that matter.

It is important to remember that audit quality does not lie in the tools themselves, but in the quality of the analyses and judgements that the tools facilitate. The visualisations that data analytics produce are only as good as the data on which they are based. Also important is the way the data is extracted, analysed and linked, in order to create visual charts that facilitate reasonable analysis.

Ultimately, the value that data analytics brings to an audit comes from the analysis – the enquiries generated, the audit evidence extracted and the conversations prompted with management.

For management, boards and audit committees, audit data analytics can deliver a higher quality and more efficiently executed audit, as well as audit findings with enhanced transparency and granularity. It can also lead to more meaningful communication between auditors and management with insights around:

Data analytics provides the audit profession with the opportunity to rethink the way an audit is executed. Many of the traditional technical limitations are vanishing.

The challenge now is to ensure that auditing standards can accommodate the new tools, improve the assurance that auditors obtain, and enhance the value of an audit to investors and stakeholders. While operational challenges remain around ensuring good quality audit evidence for analyses, the opportunity to provide better insights and risk identification for clients is exciting.

Accru Felsers auditors use data analytics in selected audits. Please contact me or your Accru Felsers auditor if you would like to discuss how data analytics could add value to your audit.