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. Emerging technologies such as blockchain, cloud, and machine learning have the potential to transform the way an audit is conducted while enhancing audit quality. Technology will help drive an increasingly interconnected financial ecosystem where data is available ever faster and updates become more real time.
Blockchain has significant potential to boost the confidence and trust that a user has in data. The cloud could make auditing more centralized, whist quantum computing could one day be applied to auditing vast datasets. The power of robotic process automation 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.
Future of technology: New technologies powering audit
New and disruptive technologies will have implications for an audit in the future
Robotic process automation (RPA)
RPA is already widely in use — in business and in the audit. A simple example of RPA is a macro in Excel. Using technology to automate a process, whether that is collecting, identifying or checking information, brings clear benefits of speed, reliability and scalability. 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.
Digital labour is a term that is frequently used, and it is the umbrella term that captures three key forms of technology: RPA (or robotics/automation), machine learning (cognitive automation) and deep learning (AI). Digital labour therefore encompasses a spectrum of different levels of technology usage and is the application of software technology to automate business processes ranging from transactional ‘swivel-chair activities’ (in which information is copied over from one system or database to another) to more complex strategic undertakings.
Natural language processing (NLP)
One of the key challenge moving forward for auditors is to be able to analyze unstructured data. An important form of unstructured data is emails and other text documents. NLP is a form of machine learning in which the technology can ‘read’ natural language to find specific information.
Machine learning is a vital step beyond robotics because the technology can capture data and identify correlations and patterns. It is also more intelligent than robotics — for example, it can locate specific line items by currency symbol and/or other keywords even if the placement varies from invoice to invoice — something robotics alone is not able to achieve. The technology can therefore be used to scan huge volumes of information.
Audit in the cloud
One of the features of the digital age is the migration of huge amounts of data to the cloud. The cloud represents a new paradigm for computing, where data is held remotely in a secure digital environment rather than in a company’s physical data centers — reducing storage and running costs for companies, increasing flexibility, providing the capability of adjusting (also known as ‘spinning’) capacity up and down, and increasing functionality and processing power.
Blockchain could have major implications for an audit. Blockchain and other decentralized ledger technologies, if designed appropriately, could provide a permanent and immutable record of transactions. It has significant potential to boost the confidence and trust that a user has in the data.
Deep learning — full-fledged AI where a machine continuously integrates new information, draws conclusions and absorbs the learnings to enhance its cognitive abilities — is seen as one of the greatest potential prizes of emerging technology. It is fair to say that this is still some way off: We are in the early stages of the adoption curve.
Quantum computing may be better associated with solving complex physics questions or mapping the variables in space travel, but the power it offers could one day be applied to auditing vast datasets too.