Masters Degrees (School of Accountancy)

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Browsing Masters Degrees (School of Accountancy) by Subject "Accounting -- Technological innovations -- Risk assessment"

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    User considerations when applying machine learning technology to accounting tasks
    (Stellenbosch : Stellenbosch University, 2018-12) Smith, Liezl; Boshoff, W. H.; Lamprecht, C.; Stellenbosch University. Faculty of Economic and Management Sciences. School of Accountancy.
    ENGLISH SUMMARY : Machine learning is a strategic technology that can have an important effect on business, as it is able to perform tasks efficiently that were previously only performed by humans. When implementing this technology in the relevant business processes and utilising it effectively, users have to understand both it as well as other aspects have to be considered. It was found that one area that is well suited to the adoption of machine learning, is accounting. In addition, prior research has shown a need for accounting users to be educated in machine learning as part of their professional training. Therefore, the aim of this study was to enhance users’ understanding of machine learning technology specifically in the performance of accounting processes. A grounded theory methodology was employed to identifying the accounting tasks machine learning could perform, to describe how machine learning functions and to identify the risks, benefits and limitations associated with the technology. Finally, steps and considerations when implementing machine learning technology in the accounting process were provided. The findings of this research are that the user has a key role to play when using machine learning technology in the accounting processes and thus has to understand the technology, the risks and limitations, as well as the benefits of the technology. The risks discussed relate not only to machine learning technology but also to all the components that enable the functioning of the technology to ensure alignment with the accounting process goals. Based on these findings, this research presents the user considerations and steps to take when implementing machine learning in selected accounting processes. These can be used to identify areas that may require attention when a business is adopting machine learning. One important consideration is the implementation of adequate data governance. This is because most of the risks identified for machine learning technology are data risks. Further research could therefore be directed at developing a data governance framework for machine learning technologies.