**2.3 Emerging technologies and audit**

Many emerging technologies have revolutionized industries. As this study is focused on the impact of digitalization on auditing, the study was limited to key technologies that have impacted the general automation of processes and digitalization of processes. The most notable emerging technologies considered are big data, AI and blockchain technology. Due to massive advances in storage technology, costs have tumbled, and in line with increased computing power, the amount of data generated has seen an exponential rise [3]. The term big data refers to datasets that are too large to manage and process with standard tools. In many cases, the data is from multiple

sources, in differing formats and fulfills different objectives; therefore, it is not generally stored in a structured/controlled way [41].

The four-V paradigm is used to describe big data, and these are volume, velocity variety and value [41]. Of the four metrics, the most pertinent is value, and the biggest concern, finding value in these large datasets and not wasting resources and time is important [3, 41]. Big data analytics is the utilization of techniques and technologies to gain insight into these large, unstructured data sets. Audit traditionally will focus on financial data, which is well-formed and more easily understood and interrogated; this can make analytics on big data potentially counter-intuitive for the audit industry [42]. The techniques and technologies, however, can be applied to the more structured financial data, and testing of all data is possible rather than the more routine sample data approach [3, 42]. Links between financial data and external non-financial sources from a big data source can be found; potential sentiment analysis might be an option [42]. Linking what is said on social media with an increase in transactions or trying to determine a value of reputational risk following a negative article being published are options that did not exist before [43]. The risk lies in not understanding these large unstructured datasets. An auditor must make sure that they understand the content and value of the dataset [3]. Another risk lies in trying to link disparate datasets (structured and unstructured) in a simple and reproducible way. Domain knowledge will have increased importance, and the skillset of the auditor will need to include several technologies in the future [3].

AI denotes that a computer can be utilized to accomplish more complicated tasks that require a human operator and skill set [44]. AI, and ML, a branch of AI, is used for the automation of tasks that require decision making rather than a linear progression that was the focus of the industrial revolution [18]. Most AI solutions are focused on language recognition, logical problem solving through iterations and visual pattern recognition [45]. For auditing purposes, the most common use for AI is for the identification of irregularities in accounting data [3, 18, 45]. As financial markets and products become more complicated, so does the process for identifying fraud and financial crime; this can be achieved with ML. It is noted that some companies are using AI for data collection and validation during the audit execution phase [45]. While the use cases of AI are still being identified, the full utility of AI in auditing is not codified. The World Economic Forum, however, predicts that 30% of audits will be completed with AI by the year 2025 [46].

A blockchain is a decentralized database that chronologically stores information about transactions of any kind [47]. As it is decentralized, a public blockchain does not have the weakness of a normal database that it can be modified or, without proper disaster recovery, data can be lost. Every member that has access to the network has an identical copy of the database, and every new transaction is validated by each member of the network [47–49]. As it is available to all parties on the network, there is an exponential increase in transparency, and this itself helps illuminate or prevent financial misdeeds.

A potential use for a public blockchain has been floated as a new way to transact and eliminate the current system where a financial institution (most commonly a bank) is the central authority in verifying and overseeing financial transactions [49]. Since auditors validate the accuracy of financial transactions and financial reporting, their role in this system would be greatly reduced [3]. It is highly doubtful that all enterprises will prescribe using a public blockchain and will prefer the security and safety of having a private blockchain, then allowing for the skills required, auditors will still be needed to validate the accuracy of the transactions [3, 49]. The regulations are currently unclear regarding regulating and the use of blockchain technology in financial systems. Hence, full-scale adoption has not occurred, and risks and benefits of using blockchain in auditing have yet to be discovered [48, 49].
