<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In the financial industry, models are pervasive, and their quantity and complexity continue to increase. Constant advancements are made in econometric and statistical theory, but a fast-developing body of rules and regulations governing their use needs modeling specialists to remain vigilant and adaptable. The tendency of these regulations to be ambiguous necessitates that industry professionals and institutions interpret them independently and jointly. This leads in what is referred to as a “industry standard,” or a set of procedures that are recognized among modeling professionals but not necessarily to those outside of the industry. Non-practitioners in the industry may view the modeling department as a “black box” for these reasons. The accurate evaluation of financial credit risk and the forecasting of bankruptcy are crucial to both the economy and society. In recent years, more and more approaches and algorithms have been advanced for this purpose. At this point, it is of the highest concern to investigate the current credit risk assessment methods. In this paper, we review the traditional statistical models and cutting-edge intelligent methods for forecasting financial distress, with a focus on the greatest advances in the academic literature, as the promising trend in this field. Lastly, the paper will conclude with an overview of the evolution of methodologies and conceptual frameworks in credit risk management research, as well as possible future research directions.</p>
</abstract>

In the financial industry, models are pervasive, and their quantity and complexity continue to increase. Constant advancements are made in econometric and statistical theory, but a fast-developing body of rules and regulations governing their use needs modeling specialists to remain vigilant and adaptable. The tendency of these regulations to be ambiguous necessitates that industry professionals and institutions interpret them independently and jointly. This leads in what is referred to as a “industry standard,” or a set of procedures that are recognized among modeling professionals but not necessarily to those outside of the industry. Non-practitioners in the industry may view the modeling department as a “black box” for these reasons. The accurate evaluation of financial credit risk and the forecasting of bankruptcy are crucial to both the economy and society. In recent years, more and more approaches and algorithms have been advanced for this purpose. At this point, it is of the highest concern to investigate the current credit risk assessment methods. In this paper, we review the traditional statistical models and cutting-edge intelligent methods for forecasting financial distress, with a focus on the greatest advances in the academic literature, as the promising trend in this field. Lastly, the paper will conclude with an overview of the evolution of methodologies and conceptual frameworks in credit risk management research, as well as possible future research directions.

Modern Approaches in Credit Risk Modeling: A Literature Review

Proceedings of the International Conference on Business Excellence

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Modern Approaches in Credit Risk Modeling: A Literature Review"}

In the financial industry, models are pervasive, and their quantity and complexity continue to increase. Constant advancements are made in econometric and...

Modern Approaches in Credit Risk Modeling: A Literature Review

Online veröffentlicht: 14. Juli 2023

Seitenbereich: 1617 - 1627

DOI: https://doi.org/10.2478/picbe-2023-0145

Schlüsselwörter
credit risk modeling, credit risk, machine learning models, traditional models, bibliometric analysis

© 2023 Luana Cristina Rogojan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Modern Approaches in Credit Risk Modeling: A Literature Review

Online veröffentlicht: 14. Juli 2023

Seitenbereich: 1617 - 1627

DOI: https://doi.org/10.2478/picbe-2023-0145

Schlüsselwörtercredit risk modeling, credit risk, machine learning models, traditional models, bibliometric analysis

© 2023 Luana Cristina Rogojan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
credit risk modeling, credit risk, machine learning models, traditional models, bibliometric analysis