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

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.