Credit Risk Model Based on Central Bank Credit Registry Data
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- Takaya Saito & Marc Rehmsmeier, 2015. "The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-21, March.
- Khandani, Amir E. & Kim, Adlar J. & Lo, Andrew W., 2010. "Consumer credit-risk models via machine-learning algorithms," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2767-2787, November.
- Lili Zhang & Herman Ray & Jennifer Priestley & Soon Tan, 2020. "A descriptive study of variable discretization and cost-sensitive logistic regression on imbalanced credit data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 47(3), pages 568-581, February.
- Shen, Feng & Zhao, Xingchao & Li, Zhiyong & Li, Ke & Meng, Zhiyi, 2019. "A novel ensemble classification model based on neural networks and a classifier optimisation technique for imbalanced credit risk evaluation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
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- Pejman Peykani & Mostafa Sargolzaei & Mohammad Hashem Botshekan & Camelia Oprean-Stan & Amir Takaloo, 2023. "Optimization of Asset and Liability Management of Banks with Minimum Possible Changes," Mathematics, MDPI, vol. 11(12), pages 1-24, June.
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Keywords
credit risk; credit registry; data science;All these keywords.
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