Masters Degrees (School of Accountancy)

Permanent URI for this collection

https://scholar.sun.ac.za/handle/10019.1/704

Browse

Browsing Masters Degrees (School of Accountancy) by Subject "Artificial intelligence -- Risk assessment"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    A structured approach to mitigate significant risks associated with the use of machine learning models
    (Stellenbosch : Stellenbosch University, 2020-03) Swanepoel, Roedolf Johannes; Sahd, Lize-Marie; Stellenbosch University. Faculty of Economic and Management Sciences. School of Accountancy.
    ENGLISH SUMMARY: Many organisations find it challenging to analyse large and varied big data sets to extract relevant insights providing competitive advantage. Traditional modelling and statistical techniques are not able to effectively analyse large and varied big data sets. The use of machine learning models presents a potential solution. The problem is that governing bodies and senior management do not always understand machine learning, the significant risks associated with the use, development and deployment of machine learning models and the controls required to mitigate the risks. The aim of this research is to investigate machine learning, machine learning models, big data and data analytics, identify significant risks and recommend mitigating controls. A literature review provided a theoretical foundation for the research performed. The literature review focused on understanding machine learning, big data, data analytics, corporate governance, information and technology governance and the use of frameworks to facilitate effective governance. COBIT 2019 was selected as the most appropriate framework to identify and mitigate significant risks associated with machine learning models. To further facilitate the identification of significant risks, the core components of machine learning, as well as a machine learning development life cycle, were identified and described. The research found that machine learning consisted of four core components, namely tasks, data, algorithms and models, that are combined into a functional machine learning model through an iterative machine learning development life cycle. Using the understanding of the core components of machine learning and the machine learning development life cycle, COBIT 2019 was used to identify significant risks related to the use of machine learning models on a strategic and operational or technological level. Strategic level risks included inadequate governance and management practices, a lack of benefits realisation and a lack of skills to develop and deploy machine learning models. Operational or technological level significant risks included: (i) risks affecting the ability of machine learning models to achieve their objectives, such as cost and data and model-related risks, (ii) risks affecting the operational effectiveness of machine learning, such as information security risks, scalability and integration, and (iii)risks relating to the machine learning development life cycle. After the identification of significantrisks, mitigating controls were formulated to address the significant risks identified. These controls included appropriate governance and management practices, strategies and policies, controls over human skills and resources and organisational change management, data management controls, controls over the IT infrastructure, model validation controls, controls over vendors and third parties and controls over the machine learning development life cycle. To summarise the research a risk-and-control matrix was prepared to link the significant risks identified to the relevant mitigating controls.