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Addendum published on 11 March 2021, see Entropy 2021, 23(3), 330.
Article

Deep Neural Networks for Behavioral Credit Rating

1
Laboratory for Financial and Risk Analytics, Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia
2
Privredna Banka Zagreb, Member of Intesa Sanpaolo Group, 10000 Zagreb, Croatia
*
Author to whom correspondence should be addressed.
Entropy 2021, 23(1), 27; https://doi.org/10.3390/e23010027
Received: 30 November 2020 / Revised: 21 December 2020 / Accepted: 22 December 2020 / Published: 27 December 2020
(This article belongs to the Special Issue Human-Centric AI: The Symbiosis of Human and Artificial Intelligence)
Logistic regression is the industry standard in credit risk modeling. Regulatory requirements for model explainability have halted the implementation of more advanced, non-linear machine learning algorithms, even though more accurate predictions would benefit consumers and banks alike. Deep neural networks are certainly some of the most prominent non-linear algorithms. In this paper, we propose a deep neural network model for behavioral credit rating. Behavioral models are used to assess the future performance of a bank’s existing portfolio in order to meet the capital requirements introduced by the Basel regulatory framework, which are designed to increase the banks’ ability to absorb large financial shocks. The proposed deep neural network was trained on two different datasets: the first one contains information on loans between 2009 and 2013 (during the financial crisis) and the second one from 2014 to 2018 (after the financial crisis); combined, they include more than 1.5 million examples. The proposed network outperformed multiple benchmarks and was evenly matched with the XGBoost model. Long-term credit rating performance is also presented, as well as a detailed analysis of the reprogrammed facilities’ impact on model performance. View Full-Text
Keywords: deep neural network; credit rating; credit risk assessment; behavioral model deep neural network; credit rating; credit risk assessment; behavioral model
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MDPI and ACS Style

Merćep, A.; Mrčela, L.; Birov, M.; Kostanjčar, Z. Deep Neural Networks for Behavioral Credit Rating. Entropy 2021, 23, 27. https://doi.org/10.3390/e23010027

AMA Style

Merćep A, Mrčela L, Birov M, Kostanjčar Z. Deep Neural Networks for Behavioral Credit Rating. Entropy. 2021; 23(1):27. https://doi.org/10.3390/e23010027

Chicago/Turabian Style

Merćep, Andro, Lovre Mrčela, Matija Birov, and Zvonko Kostanjčar. 2021. "Deep Neural Networks for Behavioral Credit Rating" Entropy 23, no. 1: 27. https://doi.org/10.3390/e23010027

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