Abstract
Presently, credit card the use has become a critical part of contemporary banking and predicting potential credit card defaulters or debtors is a crucial business opportunity for financial institutions. Meanwhile, some machine learning methods have been applied to achieve this task. However, with the dynamic and imbalanced nature of credit card default statistics, it is challenging for classical machine learning algorithms to proffer robust models with optimal performance. Research has shown that the performance of machine learning algorithms can be significantly improved when provided with optimal features. In this paper, we propose an unsupervised feature learning method to improve the performance of various classifiers using a stacked sparse autoencoder (SSAE) approach. The SSAE was optimized to achieve improved performance. The proposed SSAE learned accurate feature representations which were used to train the classifiers. The performance of the proposed approach is compared with an instance where the classifiers were trained using the raw data. Also, a comparison is made with previous scholarly works, and the proposed approach showed superior performance over other methods.