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Machine learning and credit risk : empirical evidence from SMEs

Bitetto, Alessandro; Cerchiello, Paola; Filomeni, Stefano; … - 2021

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Using meta learning methods to forecast sub-sovereign credit ratings

Evelyn, Toseafa - In: Inventi impact: emerging economies (2019) 2, pp. 117-128

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Using meta learning methods to forecast sub-sovereign credit ratings

Evelyn, Toseafa - In: Journal of Eastern Europe research in business & … 2018 (2018), pp. 1-12

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Predicting sovereign credit ratings for portfolio stress testing

Campino, Jonas de Oliveira; Galizia, Frederico; … - In: Journal of risk management in financial institutions 14 (2020/2021) 3, pp. 229-241

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The marginal impact of predicted climate risk scenarios on portfolio credit risk stress testing

Campino, Jonas de Oliveira - In: Journal of risk management in financial institutions 16 (2022/2023) 2, pp. 124-137

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Credit rating prediction through supply chains : a machine learning approach

Wu, Jing; Zhang, Zhaocheng; Zhou, Sean Xiang - In: Production and operations management : the flagship … 31 (2022) 4, pp. 1613-1629

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Revisiting the determinants of sovereign debt ratings in Europe through artificial intelligence techniques

Galnares, Carlos; Martínez-Estudillo, Alfonso Carlos; … - In: Applied economics letters 30 (2023) 17, pp. 2360-2363

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Artificial intelligence and relationship lending

Gambacorta, Leonardo; Sabatini, Fabiana; Schiaffi, Stefano - 2025

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Efficacies of artificial neural networks ushering improvement in the prediction of extant credit risk models

Aranha, Meera; Bolar, Kartikeya - In: Cogent economics & finance 11 (2023) 1, pp. 1-13

The study's objective is to check whether the predictive power of Machine Learning Techniques is better than Logistic Regression in predicting the bankruptcy of firms and that the same predictive power of ascertaining bankruptcy improves when a proxy for uncertainty is added to the model as a...

Persistent link: https://www.econbiz.de/10014500824