Abstract
This paper presents the development of an intelligent, machine learning-based Markov chain model to investigate loan risk and strategies for controlling credit risk. The model involves modeling state transitions of loan accounts using a Markov transition matrix and optimizing collection actions at each state and age for each consumer type to maximize the expected value for the lender. To enhance the performance of the minority class, we have designed some new algorithms and developed a consecutive incremental batch learning framework within the model. In addition, we use the variational inference method and logistic regression model to address the imbalanced data gap during the traditional machine learning process. We expect that the results of this study will lead to a more accurate and effective machine learning-based prediction method and make a significant contribution to the credit risk field.
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Notes
- 1.
The derivation in this section is standard in the literature on variational approximation and will at times follow the arguments in Bishop (2006) and Jordan et al. (1999).
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Wang, K., Li, A., Wang, X., Sun, L. (2023). Study on Credit Risk Control by Variational Inference. In: Zhang, F., Wang, H., Barhamgi, M., Chen, L., Zhou, R. (eds) Web Information Systems Engineering – WISE 2023. WISE 2023. Lecture Notes in Computer Science, vol 14306. Springer, Singapore. https://doi.org/10.1007/978-981-99-7254-8_62
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