Machine Learning and Credit Risk: Empirical Evidence from SMEs
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More about this item
Keywords
Credit Rating; SME; Historical Random Forest; Machine Learning; Relationship Banking; Soft Information;All these keywords.
JEL classification:
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
- D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
- G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
- G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
NEP fields
This paper has been announced in the following NEP Reports:- NEP-BAN-2021-03-08 (Banking)
- NEP-BIG-2021-03-08 (Big Data)
- NEP-CMP-2021-03-08 (Computational Economics)
- NEP-CWA-2021-03-08 (Central and Western Asia)
- NEP-ENT-2021-03-08 (Entrepreneurship)
- NEP-EUR-2021-03-08 (Microeconomic European Issues)
- NEP-FMK-2021-03-08 (Financial Markets)
- NEP-RMG-2021-03-08 (Risk Management)
- NEP-SBM-2021-03-08 (Small Business Management)
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