Default in payment, an application of statistical learning techniques

Abstract

The ability of financial institutions to detect whether a customer will default on their credit card payment is essential for its profitability. To that effect, financial institutions have credit scoring systems in place to be able to estimate the credit risk associated with a customer. Various classification models are used to develop credit scoring systems such as k-nearest neighbours, logistic regression and classification trees. This study aims to assess the performance of different classification models on the prediction of credit card payment default. Credit data is usually of high dimension and as a result dimension reduction techniques, namely principal component analysis and linear discriminant analysis, are used in this study as a means to improve model performance. Two classification models are used, namely neural networks and support vector machines. Model performance is evaluated using accuracy and area under the curve (AUC). The neuarl network classifier performed better than the support vector machine classifier as it produced higher accuracy rates and AUC values. Dimension reduction techniques were not effective in improving model performance but did result in less computationally expensive models.