Can machine learning models save capital for banks? Evidence from a Spanish credit portfolio
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DOI: 10.1016/j.irfa.2022.102372
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Cited by:
- Cosma, Simona & Rimo, Giuseppe & Torluccio, Giuseppe, 2023. "Knowledge mapping of model risk in banking," International Review of Financial Analysis, Elsevier, vol. 89(C).
- Bolívar, Fernando & Duran, Miguel A. & Lozano-Vivas, Ana, 2023.
"Business model contributions to bank profit performance: A machine learning approach,"
Research in International Business and Finance, Elsevier, vol. 64(C).
- F. Bolivar & Miguel A. Duran & A. Lozano-Vivas, 2024. "Business Model Contributions to Bank Profit Performance: A Machine Learning Approach," Papers 2401.12334, arXiv.org.
- Zhou, Ying & Shen, Long & Ballester, Laura, 2023. "A two-stage credit scoring model based on random forest: Evidence from Chinese small firms," International Review of Financial Analysis, Elsevier, vol. 89(C).
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More about this item
Keywords
Machine learning; Credit risk; Prediction; Probability of default; IRB system;All these keywords.
JEL classification:
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
- C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
- G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
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