Bagging Supervised Autoencoder Classifier for credit scoring

Automatic credit scoring, a crucial risk management tool for banks and financial institutes, has attracted much attention in the past few decades. As such, various approaches have been developed to accurately and efficiently estimate defaults in loan applicants and seamlessly improve and facilitate decision-making in the lending process. However, the imbalanced nature of credit scoring datasets, as well as the heterogeneous nature of features in credit scoring task pose many challenges in developing and implementing effective credit scoring models, targeting the generalization power of classification models on unseen data. To mitigate these challenges, in this paper, we propose the Bagging Supervised Autoencoder Classifier (BSAC). BSAC is a learning model which simultaneously leverages the superior power of supervised autoencoders and representation learning in classification, as well as the Bagging mechanism to handle the irregularities in feature space. Supervised autoencoder has been exploited to learn an optimal latent space from heterogeneous features and perform classification on top of the learned latent space. In particular, the Bagging mechanism has been employed in the learning process to construct various samples of original data to tackle the problem that arises from imbalanced data and irregularities of features in latent space. Extensive experiments on various real-world and benchmark datasets validate the superiority and robustness of the proposed method in predicting the outcome of loan applications. •A novel credit scoring model using representation, ensemble, and multi-task learning.•In BSAC, the learned representations are guided by the label information of samples.•BSAC outperforms state-of-art baseline models in credit scoring imbalanced data.•BSAC performs significantly better than the best base classifier in the pool.•The model shows a balanced performance in classifying positive and negative samples.