Risk Free Credit: Estimating Risk of Debt Delinquency on Credit Cards : Using Machine Learning Methodology

Sammanfattning: A well functioning economy requires a stable credit market. Computational intelligence methods could provide a method to reduce the amount of uncertainty in the markets. This report examines four different methods for predicting the probability for defaults of credit card clients in Taiwan. The four selected methods were Linear Discriminant Analysis, Support Vector Machines, Artificial Neural Networks and Deep Neural Networks. The models were then evaluated with regards to five different methods: Area Under the Curve for the Receiver Operating Characteristic, accuracy, precision, sensitivity and specificity. The results showed that all models performed better than random with similar results, except for the Support Vector Machine which in our testing configuration incorrectly classified almost all debtors that defaulted on their debt. Although there was no clearly superior model the results showed that the Deep Neural Networks and Linear Discriminant Analysis were the two most promising methods.