Robotic process automation has enormous implications for the audit
You may hear a lot of different terms flying around these days about various forms of 'digital labor'. Digital labor covers a spectrum of different technologies that run from RPA (or robotics/automation), to machine learning (cognitive automation) and on to deep learning (artificial intelligence).
RPA is the simplest form of digital labor. Its significance is that it enables data to be collected, analyzed or calculated at a speed and scale far greater than a human or team of humans could manage.
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 analyze 100% 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 analyzed a complete set of about 250 million transactions, isolating 50 to 60 that were identified as outliers and brought these forward to the organization for an in-depth discussion.1
Areas such as audit confirmations, reconciliations, generation of emails, automated emails, both internally and with the organization's data, can all be facilitated with RPA. For example, KPMG in Canada has already used RPA techniques such as automating confirmations with banks and depositories directly with our clients records. In addition we have created routines matching of cash receipts to our clients ledgers to eliminate the need for certain confirmations, resulting in a more efficient and higher quality audit. In We have also piloted reviewing an organization's performance of a four-way match in their sales-to-cash process, analyzing 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 organizations' 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 analyzed. 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 analyze 100% 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 and its flexible deployment in the cloud, 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.
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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