Browsing by Author "Landy, Angelique"

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    Effective implementation and governance of association rules in credit scoring and default management
    (Stellenbosch : Stellenbosch University, 2022-04) Landy, Angelique; Rudman, Riaan; Stellenbosch University. Faculty of Economic and Management Sciences. School of Accountancy.
    ENGLISH SUMMARY: Advancements in technology and an increase in the availability of alternative consumer data enabled an evolution in credit risk modelling. The result being traditional credit scoring and default management procedures becoming redundant. Encapsulating the alternative dimensions into one credit score is challenging, but machine learning techniques are well positioned to facilitate more accurate and modern credit scoring. To effectively leverage alternative data to increase the accuracy, validity and completeness of credit scores and reduce default rates, association rule learning is suggested. The extraction of associations between various alternative data and credit risk factors facilitates credit providers to assess credit risk more accurately but also enables early detection of possible credit defaults. Effectively implementing new technology, such as association rule learning, is a complex process. Therefore, to effectively implement association rules the features and characteristics relevant to implementation thereof needs to be considered against the life cycle of system development. Fundamental to the successful implementation of association rule Learning is sufficient quality data, therefore making proper data governance essential. The objective of this research was to provide a framework for credit providers to use when implementing association rules for credit scoring and default management. The framework is in the form of an application checklist that focuses on how to implement an unsupervised machine learning algorithm effectively, purposely considering the data governance issues faced by credit providers when implementing it for credit scoring and default management. To define association rule implementation guidelines for credit scoring and default management, this research provides an understanding of the evolution in the credit industry, the underlying data considerations and enabling technologies of big data, machine learning and data mining. The study further considers both the data governance and system development life cycle to identify applicable considerations relevant to implementing association rules for credit scoring and default management. Based on the investigation, an implementation checklist was designed to map the investigated implementation considerations with the system development life cycle, structured using the COBIT 2019 enablers. This model will provide guidance to data scientists and other users to assess their data and infrastructure requirements necessary and most appropriate for implementation.