Predict Sequential Credit Card Delinquency with VaDE-Seq2Seq

Abstract

For successful debt collection, it is important for credit card companies to judge the users' capability of debt repayment. This has been assessed by domain experts in the past, but as the amount of data increases, there has been a rising demand for a more effective decision-making methods. Several machine learning algorithms have been proposed to pursue interpretation and high performance. We newly propose Variational Deep Embedding with Sequence to Sequence (VaDE-Seq2Seq), based on a deep neural network. By adding the VaDE structure to the encoder, the model properly reflects information on cluster assignments in latent space, and the model explains decision-making by tracking the cluster assignments. Most delinquency prediction studies predict only the next time step, whereas our model predicts the future sequence. It is a strength of our model because sequence prediction is difficult, but more practical. The model was tested with the data of 10,000 users from a Korean credit card company, and VaDE-Seq2Seq outperforms the other baseline models in terms of performance. In addition, we observe the history in the latent cluster assignment that was clearly distinguished between non-delinquency users and delinquency users.