Generating customer's credit behavior with deep generative models

Abstract

Banks collect data x₁ in loan applications to decide whether to grant credit and accepted applications generate new data x₂ throughout the loan period. Hence, banks have two measurement-modalities, which provide a complete picture about customers. If we can generate x₂ conditioned on x₁ keeping the relationship between these two modalities, credit and behavior scoring may be enabled simultaneously (at the time x₁ is obtained) to support cross-selling, launching of new products or marketing campaigns. Therefore, we develop a novel conditional bi-modal discriminative (CBMD) model for credit scoring, which is able to generate x₂ based on x₁ and can classify the outcome of loans in an unified framework. The idea behind CBMD is to learn joint (among modalities) latent representations that are useful to generate x₂ using the available data x1 during the application process. The classifier model introduced in CBMD encourages the generative process to generate x₂ accurately. Further, CBMD optimizes a novel objective function introduced in this research, which maximizes mutual information between the latent representation z and the modality x₂ to improve the generative process in the model. We benchmark the generative process of our proposed model and CBMD outperforms other multi-learning models. Similarly, the classification performance of CBMD is tested under different scenarios and it achieves higher or on a par model performance compared to the state-of-the-art in multi-modal learning models.