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A comparative study of forecasting corporate credit ratings using neural networks, support vector machines, and decision trees

Golbayani, Parisa; Florescu, Ionuţ; Chatterjee, Rupak - In: The North American journal of economics and finance : a … 54 (2020), pp. 1-16

Persistent link: https://www.econbiz.de/10012665100

Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects

Dumitrescu, Elena; Hué, Sullivan; Hurlin, Christophe; … - In: European journal of operational research : EJOR 297 (2022) 3, pp. 1178-1192

Persistent link: https://www.econbiz.de/10013263050

Credit scoring in context of interpretable machine learning : theory and practice

Kaminski, Bogumil (ed.); Szapiro, Tomasz (ed.); … - 2020 - First edition

Persistent link: https://www.econbiz.de/10012505222

Predictably unequal? : the effects of machine learning on credit markets

Fuster, Andreas; Goldsmith-Pinkham, Paul; Ramadorai, Tarun - 2017

Persistent link: https://www.econbiz.de/10011818460

The Vasicek Credit Risk Model : A Machine Learning Approach

García-Céspedes, Rubén; Moreno, Manuel - 2021

This paper explores the ability of the Machine Learning (ML) techniques to calibrate models that replicate the outputs of the Vasicek credit risk model. This model measures the loss distribution of a portfolio made up of loans that can be exposed to multiple systemic factors and it is widely...

Persistent link: https://www.econbiz.de/10013230146