Researcher: Nompumelelo Sibiya, University of the Witwatersrand, Johannesburg
Supervisors: Mr Rendani Mbuvha, University of the Witwatersrand, Johannesburg
Due to increasing popularity associated with artificial intelligence, machine learning and data availability, most banks are renewing their business models. Credit risk predictions is one of the most vital keys to evaluation measure and decision making. This study establishes two binary classifiers based on machine learning models namely; Support Vector Machines and Gradient Boosting Machines as well as the classic Logistic Regression on real credit card data in predicting lo an default probability. Results reveal that class distribution has a major effect on the performance of the models, how ever, gradient boosting can cater to this and produce robust performance.
