Introduction
The crucial role that audit plays in lending credibility and confidence to the capital market cannot be over-emphasised. From the Fortune 500 to the least capitalized companies on the world stock exchanges, the demand and wish of the investors has remained the same; the yearning and craving for a reasonable financial assurance which can only be provided through an audit conducted by an independent auditor.
Despite this huge public expectation, there has been some inherent limitations and challenges particularly in the audit of large corporations. This is due to the extremely high volume of transactions and the impracticality of vouching all these transactions to relevant supports and evidences. This consequently reduces the scope of the assurance provided by an audit.
However, the good news is that the recent technological advancement is IT data analytics, big data and artificial intelligence has come to address these limitations to a reasonable extent.
What is Data Analytics?
Data analytics in the context of this write-up could be seen as the act of sourcing, processing, analysing, interpreting and visualizing data with the primary objective of extracting actionable insights from the results of the analysis.
The International Auditing and Assurance Standards Board (IAASB) defines data analytics as the science and art of discovering and analysing patterns, deviations and inconsistencies, and extracting other useful information in the data underlying or related to the subject matter of an audit through analysis, modelling and visualisation for the purpose of planning and performing the audit. {1}
There are various data analytic tools available to auditors. In fact, audit firms are now integrating data analytic capability into their audit workflow and this allows them to run all their analytics within a single system.
Common data analytic tools include excel data analysis tool, python, SQL query, IDEA analytical software, R Programming, tableau etc.
Impact of Data Analytics on Audit
Data analytics has positively impacted audit in several ways some of which are;
1) Fraud and Error Detection: Data analytic tools can help to discover unusual pattern in large data set which sometimes may be suggestive of fraud or error.
2) Analytic tools usually contain features that assist to discover invalid, missing or erroneous data and could sometimes assist in confirming the completeness of a population to be tested. Likewise, through the application of computer assisted audit techniques (“CAATs”), audit analytic tools assist in verifying the completeness and accuracy of system generated reports.
3) CAAT enables auditors to easily take care of the highly automated ledger balances thereby allowing an increased focus on high risk areas that require deep professional judgement.
4) It enables effective managerial decision making through superior business intelligence provided by data analytic tools such as Microsoft Power BI
5) Increases efficiency thereby reducing the time spent mundane task on an audit;
6) The traditional auditing methodology is based on sampling technique; selecting a set of data for sampling out of a large population. However, with data analytics, it is possible for auditors to test the entire population and provide audit evidences in more detailed granular form.
7) Also, advanced machine learning and artificial intelligence has led to the invention of warehouse drones which can be used to carry out inventory count. Inventory count involves taking a record count of the stock being held by a company at a particular point in time. Inventory counts are usually done manually and may be very strenuous and time-consuming.
Conclusion
There is the need for internal audit departments and external audit firms to continually leverage on and develop capacity in data analytics considering its numerous benefits (even though it has its own limitations).
However, in spite of the afore-mentioned benefits, the insight provided by data analytics tools cannot replace the auditor’s professional judgement. It remains the responsibility of the auditor to maintain adequate professional skepticism and judgement throughout the audit.
Reference
{1} This definition of data analytics is based largely on a definition used in an American Institute of Certified Public Accountants (AICPA) publication titled Audit Analytics and Continuous Audit, Looking Toward the Future