Robotic process automation powering up the audit - KPMG Australia
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Robotic process automation powering up the audit

Robotic process automation powering up the audit

You may hear a lot of different terms flying around these days about various forms of ‘digital labour’. Digital labour covers a spectrum of different technologies that run from robotic process automation (RPA, or robotics/automation), to machine learning (cognitive automation) and on to deep learning (artificial intelligence). RPA is the simplest form of digital labour. Its significance is that it enables data to be collected, analysed or calculated at a speed and scale far greater than a human or team of humans could manage.

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While the common perception of ‘robotics’ may be a robot or piece of machinery that automates a packing, picking or processing process in a factory, robotics is equally applicable to business processes, such as in the finance function, human resources, internal audit or external audit. RPA means that data can be processed in vast quantities, far beyond what was possible before.

Robotics in the audit

RPA has enormous implications for the audit — and is already bringing huge benefits. In the analogue world where accounting was done with manual tools like physical ledgers, the auditor would validate processes and transactions using statistically valid sampling or similar techniques. In today’s digital world, where data is proliferating across digital networks and systems, we are bringing new capabilities to mine the mountain of data to identify audit risk, highlight anomalies and outliers, and perform further analysis.

Already, new technology is dramatically enhancing the analytical power of our audits. Using RPA, we can analyse 100 percent of certain datasets through various audit lenses. This means that we can quickly identify the outliers that need further examination. For example, an audit engagement team analysed a complete set of about 250 million transactions, isolating 50 to 60 that were identified as outliers and brought these forward to the organisation for an in-depth discussion.1

In a service centre such as KPMG’s Global Services (KGS), we regularly use RPA in our centralised service centres to validate calculation datasets, enabling our people to focus on collecting and analysing anomalies.

Areas such as audit confirmations, reconciliations, generation of emails, automated emails, both internally and with the organisation’s data, can all be facilitated with RPA. For example, KPMG in China is piloting the automation of matching the organisation’s own cash transactions to external bank statements to tax authority VAT records. KPMG in Germany is reviewing an organisation’s performance of a four-way match in their sales-to-cash process, analysing individual sales orders, invoices and shipping documents being reconciled to the cash received.

A key use of RPA is to gather audit evidence by collecting information where there is data in different organisations’ systems that are not integrated. This information can then be subjected to data analytics to inform the auditor to enhance risk assessment procedures or provide audit evidence. RPA is not in itself ‘intelligent’ but is a vital part of the process of gathering information that can then be intelligently analysed. RPA helps with collecting data, combining data from different sources and applying a basic order to the data.

What impact could RPA have on audit quality?

The ability to analyse 100 percent of datasets rather than sampling provides clear benefits whereby the technology will enable auditors to focus their efforts on the outliers and anomalies, devoting greater time to areas of higher risk.

The power of RPA — and other new, emerging technologies such as machine learning, natural language processing (NLP) and deep learning — will mean that an audit, based on increasingly granular and sophisticated analysis of data, may provide richer, more detailed audit evidence, enhanced transparency and depth of audit procedures, and deeper views into a company’s risks and its controls.

Combined with the integration of our applications into KPMG Clara, its flexible deployment in the cloud, and with such capabilities as Dynamic Risk Assessment, we are well placed to develop ever more powerful capabilities. These capabilities will also help drive consistency across the network, helping us to enhance service to cross-border businesses.

The fundamentals of an audit will not change as the need for human judgment and professional skepticism will always be necessary. The real-use case for new technologies is that they will enable us to obtain — more easily, quickly, accurately and extensively than ever before — the corroborating evidence that is needed in an audit.

 

Helping to bring emerging technology to the global network

KPMG is investing in enabling technology to improve audit quality, enhance confidence in financial statements and further strengthen capital markets. Within member firms and through global strategic investments, we are piloting new technologies to help raise the bar on audit quality, by exposing risks and bringing heightened focus to anomalies and outliers not seen before, while revealing new insights to our clients about their business.

As part of our commitment to bring consistency to our audits across the network, we are working with your audit technology leads to identify and assess capabilities that may be leveraged to the benefit of all member firms.

To help do this, we have a Global Intake Process that includes a rigorous review against a comprehensive scorecard. Using this process means we are able to support audit innovation globally while helping ensure consistency, achieve greater speed to market, and to continually improve audit quality. Some of the examples featured in this article may be considered for review through the Global Intake Process.

References

  1. Outliers being defined as exceptions based on our audit lenses in assessing the transactions that were not consistent with an industry expectation, an accounting principle or our expectation on how controls would have processed the information, among others. 

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