A Closer Look at the Technology Transforming the Audit Landscape

Artificial intelligence and similar robots are friends, not a threat, thanks to disruptive technologies that have ushered in a new era that allows auditors to provide even more robust assurance to their clients. Human auditors will continue to be the backbone of auditing in the future, with machines serving as essential and diligent assistive hands.

Fremont, CA: The nature of the audit process will change when additional data input sources that are not limited to historical data become available. It will transition from being primarily a reactive exercise conducted in hindsight to becoming a more proactive and predictive endeavor.

The definition of an audit and its contents are being redefined by the technological advancements gaining traction in the auditing industry. For the foreseeable future, the provision of audit and assurance services will change due to the many innovative technologies currently in development, including machine learning, blockchain, artificial intelligence, and robotic process automation.

Adopting novel technologies yields discrete advantages, including heightened operational efficacy, superior and more perceptive auditing, expanded financial inclusivity, and more knowledgeable decision-making procedures. The audit process will change from being primarily a reactive exercise conducted in retrospect to becoming a more proactive and predictive endeavor as more sources of data input that are not limited to past data come into play. Likewise, the auditor's position will need to change. To provide their clients with greater value-added assurance, they will require technical abilities and familiarity with the many modern technologies.

How Technology is Transforming the Audit Landscape?

Technologies transform the audit landscape drastically. Some of the technologies that transform audit may include:

Data Analytics:

To support decision-making, data analytics aims to derive inferences from raw data. Data sets can be transformed into an easily understood pre-defined format, making it easier for clients and auditors to see trends and understand the information. Many businesses have moved away from traditional sampling techniques by integrating data analytics into their testing processes. The primary benefit of employing data analytics is that considering the entire population of transactions rather than just a sample may give auditors a more comprehensive picture. With the increased data coverage, auditors should be able to provide clearer insights into high-risk regions and be more precise and thorough when using "data-mining" technologies to identify irregularities. An increasing percentage of customers find that a snapshot of the merchandise is insufficient and no longer perfect.

Machine Learning:

An application of artificial intelligence is machine learning, which teaches a computer to learn without explicit and direct instruction using algorithms and data. It employs these models to create predictions and find patterns from data analysis, automating the process of constructing analytical models. Machine learning is an iterative process, just like human learning is. This implies that the more relevant data a machine is exposed to, the more adept it grows at internalizing patterns and recognizing them. The frequent exposure to pertinent facts continually improves its reactions.

Machine learning can be used in auditing by programming the system to identify potential outliers. If an auditor found that a company was not meeting industry benchmarks, they would need to determine whether the discrepancy was an outlier and the cause. The system would then apply the auditor's response to future instances of the same situation. The more cycles the continuous feedback loop completes, the more accessible it is for the machine to detect irregularities when it processes enormous volumes of data.

Robotic Process Automation:

Artificial intelligence mimics human intelligence, whereas robotic process automation (RPA) aims to substitute human labor for repetitive and regular jobs. It would be appropriate to compare it to highly sophisticated Excel spreadsheet macros. The "thinker" is the former, and the "doer" is the latter. RPA is typically used when information needs to be supplied into one system from another or when data scattered across various systems needs to be combined.

Robots can be programmed to open PDFs, interpret documents, find pertinent information, and alert users to problems or ambiguities with the correct configuration. Because it is protocol- and rule-based, RPA can relieve auditors of labor-intensive regular duties, including form-filling, calculations, reconciliations, substantive testing, internal control testing, and audit documentation preparation.