Credit Scoring on Cash Flow Table with Machine Learning: XGBoost Approach
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DOI: 10.26650/JEPR1114842
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References listed on IDEAS
- Dominique Guegan & Bertrand Hassani, 2018. "Regulatory learning: How to supervise machine learning models? An application to credit scoring," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01835213, HAL.
- Nehrebecka Natalia, 2018. "Predicting the Default Risk of Companies. Comparison of Credit Scoring Models: Logit Vs Support Vector Machines," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 22(2), pages 54-73, June.
- Yiheng Li & Weidong Chen, 2020. "A Comparative Performance Assessment of Ensemble Learning for Credit Scoring," Mathematics, MDPI, vol. 8(10), pages 1-19, October.
- Dominique Guegan & Bertrand Hassani, 2018. "Regulatory learning: How to supervise machine learning models? An application to credit scoring," Post-Print halshs-01835213, HAL.
- Apostolos Ampountolas & Titus Nyarko Nde & Paresh Date & Corina Constantinescu, 2021. "A Machine Learning Approach for Micro-Credit Scoring," Risks, MDPI, vol. 9(3), pages 1-20, March.
- Lara Marie Demajo & Vince Vella & Alexiei Dingli, 2020. "Explainable AI for Interpretable Credit Scoring," Papers 2012.03749, arXiv.org.
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More about this item
Keywords
Machine Learning; XGBoost; Credit Scoring; Python; Artificial Neural Network JEL Classification : C13; C62; C69;All these keywords.
JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C62 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Existence and Stability Conditions of Equilibrium
- C69 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Other
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