What is an Audit?
The term ‘audit’ is occasionally used in everyday language to refer to a check that a representation of financial information is correct. In an accounting context, the term audit is used to refer to the process an auditor undertakes to examine supporting information and evaluate whether financial statements represent past economic events.
What is Data Analytics in the context of an Audit?
While traditional audit methods involve obtaining a small sample of data and extrapolating the results to identify anomalies for investigation, data analytics enables auditors to work with 100% of the transactions within a population of data. Through data analytic insights, 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.
Larger corporations are frequently using data analytics as part of their governance, risk and control monitoring systems. Increasingly, smaller company’s are finding the insights provided by data analytics a valuable source of information.
One of the benefits of data analytics is the way it enables auditors to improve the risk assessment process, effectiveness of 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.
How Audit Data Analytics works:
Data analytics allows auditors to examine information, develop insights and draw conclusions. The term ‘data analytics’ predominantly refers to an assortment of applications from basic business intelligence to various forms of advanced analytics.
Generally, it 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 identify and determine risks relating to the frequency and value of accounting transactions. Some of these procedures are simple while others involve complex models. Auditors using these models will exercise professional judgement to define mathematical and statistical patterns, helping identify exceptions for extended testing.
Auditors commonly use data analytics procedures to examine:
- Receivables and payables ageing, and the reduction in overdue debt over time;
- Analyses of gross margins and sales, highlighting items with negative margins;
- Matches of orders to cash, and purchases to payments;
- Testing to see whether segregation of duties are appropriate, and whether any inappropriate combinations of users have been involved in processing transactions
- Analyses of capital expenditure versus repairs and maintenance.
Data analytics initiatives can also assist businesses increase revenues, improve operational efficiency and respond more quickly to emerging market trends while gaining a competitive edge over rivals. We focused above on historical records, whereas the same principles can be applied to real-time data from a mix of internal and external data sources.
How Data Analytics contributes to Audit Quality:
Analytics as an aid has been around for quite a few years with practical tools improving with several iterations. Currently, 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. An affinity with data, the ability to interpret results and apply the right phases of an engagement are ideally the skills applied to deliver valuable insights.
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.
How Businesses Benefit:
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:
- Control gaps: if control deficiencies have been remediated, are the outcomes as expected or hoped for?
- Measuring 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; and
- Internal benchmarking.
What does Data Analytics mean for the future of Audit?
Data analytics provides the audit profession with the opportunity to rethink the way an audit is performed. 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.