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Credit risk modeling using Bayesian network with a latent variable
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DOI：10.1016/j.eswa.2019.03.014.png)
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En 中文
Credit risk assessment is an important task for the implementation of the bank policies and commercial strategies. In this paper, we used a discrete Bayesian network with a latent variable to model the payment default of loans subscribers. The proposed Bayesian network includes a built-in clustering feature. A full procedure for learning its parameters, based on a customized Expectation-Maximization algorithm was provided. This model allows evaluating the payment default probability taking into account several factors and handling a multi-class situation. Relying on a real data set describing loans contracts, we calibrated the model and performed several analyses. The obtained results highlight a regime switching of the default probability distribution: Two classes were determined showing a change in credit risk profiles. (C) 2019 Elsevier Ltd. All rights reserved.
Keyword:
Credit risk
Bayesian network
Latent variable
EM algorithm
Mixture model
Payment default
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IF:
7.5
论文数:
3.0W
被引数:
10.2W
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