Approaches for credit scorecard calibration: An empirical analysis
Author
Abstract
Suggested Citation
DOI: 10.1016/j.knosys.2017.07.034
Download full text from publisher
To our knowledge, this item is not available for download. To find whether it is available, there are three options:1. Check below whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Wosnitza, Jan Henrik, 2022. "Calibration alternatives to logistic regression and their potential for transferring the dispersion of discriminatory power into uncertainties of probabilities of default," Discussion Papers 04/2022, Deutsche Bundesbank.
- Matthieu Garcin & Samuel Stéphan, 2023. "Credit scoring using neural networks and SURE posterior probability calibration," Working Papers hal-03286760, HAL.
- Manta Eduard Mihai & Bogoevici Flavia, 2023. "Clustering the AI Landscape: Navigating Global Insights from Leading AI Indexes," Journal of Social and Economic Statistics, Sciendo, vol. 12(2), pages 88-108, December.
- Kolesnikova, A. & Yang, Y. & Lessmann, S. & Ma, T. & Sung, M.-C. & Johnson, J.E.V., 2019. "Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk Behavior Forecasting," IRTG 1792 Discussion Papers 2019-023, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
- Siyi Wang & Xing Yan & Bangqi Zheng & Hu Wang & Wangli Xu & Nanbo Peng & Qi Wu, 2021. "Risk and return prediction for pricing portfolios of non-performing consumer credit," Papers 2110.15102, arXiv.org.
- Weidong Guo & Zach Zhizhong Zhou, 2022. "A comparative study of combining tree‐based feature selection methods and classifiers in personal loan default prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1248-1313, September.
- Matthieu Garcin & Samuel St'ephan, 2021. "Credit scoring using neural networks and SURE posterior probability calibration," Papers 2107.07206, arXiv.org.
- Lessmann, Stefan & Coussement, Kristof & De Bock, Koen W. & Haupt, Johannes, 2018. "Targeting customers for profit: An ensemble learning framework to support marketing decision making," IRTG 1792 Discussion Papers 2018-012, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
- Maria Tragouda & Michalis Doumpos & Constantin Zopounidis, 2024. "Identification of fraudulent financial statements through a multi‐label classification approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(2), June.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:journl:hal-01745262. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.