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Generating customer's credit behavior with deep generative models
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AI
DOI：10.1016/j.knosys.2022.108568.png)
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摘要
En 中文
Banks collect data x(1) in loan applications to decide whether to grant credit and accepted applications generate new data x(2) throughout the loan period. Hence, banks have two measurement-modalities, which provide a complete picture about customers. If we can generate x(2) conditioned on x(1) keeping the relationship between these two modalities, credit and behavior scoring may be enabled simultaneously (at the time x(1) 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 x(2) based on x(1) 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 x(2) using the available data x(1) during the application process. The classifier model introduced in CBMD encourages the generative process to generate x(2) accurately. Further, CBMD optimizes a novel objective function introduced in this research, which maximizes mutual information between the latent representation z and the modality x(2) 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. (c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Keyword:
Multi-modal learning
Credit scoring
Deep generative models
Representation learning
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期刊
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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