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.
How audit data analytics works
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:
- Receivables and payables ageing, and the reduction in overdue debt over time by customers
- 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.
How data analytics contributes to audit quality
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.
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
- 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 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.