Performance evaluation of fourteen machine learning algorithms on credit card default classification

Abstract

Banks process their financial data by machine learning techniques to get knowledge from the data and use that knowledge in decision making and risk management. In this research, fourteen classification models have been built and trained using a real financial data from a bank in Taiwan. The models forecast the credit card default of a customer which is the repayment delay of the credit granted to the customer. The main idea of the research is evaluating and comparing the models based on their predictive average class accuracy

Machine learning approach to drivers of bank lending: evidence from an emerging economy

“…The extra-trees algorithm is a tree-based ensemble algorithm that uses a conventional top-down procedure (Geurts et al 2006). The empirical procedure of this algorithm is quite similar to that of the random forest algorithm (Altabrawee 2017). There are two main differences between the extra-trees algorithm and the random forest algorithm (Geurts et al 2006):…”