Generating customer’s credit behavior with deep generative models

摘要

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.