Audit in the Modern Era : The Impact and use of Technology

Covid 19 affected both the projections of every business and the way they operated, notably in increasing adoptions of use of digital technologies. Business related services, such as audit, are no exception. The pandemic accelerated the digital journey of the audit profession which had already begun with more firms investing in digital technologies, albeit more slowly until then. The transition to remote work has brought about significant change in auditing methodology.

The full value from auditing can only be derived through the blend of emerging technologies and human skillsets and judgment. At its core, audit remains a very human endeavor. Technology, for all its explosion, remains an enhancement tool , if an increasingly more important one! The specifics differ. As of now, data analytics is the most popular and the most mature technology used in audits while something like machine learning is yet to be more widely adopted.

In this article we discuss the emerging technologies in Audit and their adoption.

Data Analytics

Analytical tools are applied to the data derived from accounting and operational systems. Data analytics is used as part of transactions testing as a departure from traditional sampling techniques. This tool allows an auditor to use the complete set of a population’s transactions when performing their tests. Rather than sampling transactions data to test a snapshot of activities, analysis can now be made for all transactions processed which helps in better identifying of anomalies and thus the risks, in addition to monitoring ‘business as usual’ activities.

Machine Learning (ML)

Rapid growth in the volume of financial transactions, if not properly managed, could significantly affect the work of accountants. For auditors, this relates to the samples they need and their representativeness.  In fact, a technology like machine learning could go beyond that with the possibility of reviewing entire populations to assist the auditor in testing items that are outside the norm. ML ‘predictions’ has clear applications in risk management and the detection of fraud and inaccuracy by comparing historical data sets with current data.

Deep Learning (DL)

Deep learning is subset of Machine Learning which uses neural networks to perform more complex tasks. DL systems are commercially available and have already been deployed and can be used to ‘read’ thousands of complex documents, such as contracts, leases and invoices, extracting and structuring textual information such as key words or phrases. DL has the ability to analyze a range of internal and external sources. Thus, this Big Data can potentially supply complementary audit evidence.

Drone  and Related Technologies

Unmanned drones are used in a variety of commercial projects. It has applications in the audit industry as well. Drones can be used for various physical verification and stock count exercises where physical scale or distribution is an issue.

Distributed Ledger Technology (DLT)

For the auditor, distributed ledgers become a sort of universal bookkeeping service, removing the need to reconcile multiple databases and providing a perfect audit trail. A key principle of DLT is immutability: historical entries cannot be changed, only corrected with a balancing entry. This helps auditors to test audit assertions such as occurrence and cut-off.

Cloud Technologies

Cloud technologies allows employees to work from anywhere in the world, enabling geographically dispersed teams to work on the same project in real time. Cloud technologies lower costs as cloud storage can provide huge capacity with the business only paying for the space that it uses. For cloud to be useful it must contain critical data, and a key benefit for audit is that organizations will increasingly be referring to a single data source  which updates for everyone everywhere with no time lags or inconsistencies.

Audit is thus moving from what has been perceived to be a reactive, backward-looking practice to a proactive, predictive, forward-looking one working in real time. As such, it provides further opportunity to help businesses through timely insights. At the same time, audit quality is enhanced by the closeness to an audited entity that is acquired through repeated involvement in the engagement. The  highlights of technology in the audit profession and current trajectory can be summed up as follows:

• Among the available technologies, data analytics is currently preferred and most used by the firms.
• Technology cannot replace the client-auditor human relationship which remains important.
• Auditors need to continue to adopt newer technologies and be adaptive to change going forward.