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

<p>This study examines the existing credit rating methodology proposed in the literature to explore the development of a new credit rating model based on the financial variables of the enterprise. The focus is on the period after the financial crisis of 2018. This study aims to develop a credit rating model using neural networking and tests the same for its accuracy. The goal of this study is to address the issue brought up by previous research on subjectivity in the data used to determine creditworthiness. The database for the study includes financial data up to July 2022 from December 2018. A model is created to assess an enterprise's creditworthiness using neural networking. This study first evaluated the existing credit rating models proposed in the literature. Next, based on financial data and neural networking, a model is developed. It was evident that the model developed in this study has an accuracy of 85.16% and 76.47% on train and test data respectively. There exist several models to determine the creditworthiness of borrowers but all failed to address the concern of subjectivity in the data. The model created in this study made use of cutting-edge technology such as neural networking and financial data. This paper's unique approach and model construction based on a comparison of existing models is rare in the literature and justify the originality of this paper with a practical value at the global level.</p>
</abstract>

This study examines the existing credit rating methodology proposed in the literature to explore the development of a new credit rating model based on the financial variables of the enterprise. The focus is on the period after the financial crisis of 2018. This study aims to develop a credit rating model using neural networking and tests the same for its accuracy. The goal of this study is to address the issue brought up by previous research on subjectivity in the data used to determine creditworthiness. The database for the study includes financial data up to July 2022 from December 2018. A model is created to assess an enterprise's creditworthiness using neural networking. This study first evaluated the existing credit rating models proposed in the literature. Next, based on financial data and neural networking, a model is developed. It was evident that the model developed in this study has an accuracy of 85.16% and 76.47% on train and test data respectively. There exist several models to determine the creditworthiness of borrowers but all failed to address the concern of subjectivity in the data. The model created in this study made use of cutting-edge technology such as neural networking and financial data. This paper's unique approach and model construction based on a comparison of existing models is rare in the literature and justify the originality of this paper with a practical value at the global level.

Modelling Financial Variables Using Neural Networking to Access Creditworthiness

Symbiosis Institute of Business Management

Financial Internet Quarterly

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Modelling Financial Variables Using Neural Networking to Access Creditworthiness"}

This study examines the existing credit rating methodology proposed in the literature to explore the development of a new credit rating model based on the...

Modelling Financial Variables Using Neural Networking to Access Creditworthiness

Online veröffentlicht: 20. Juni 2024

Seitenbereich: 62 - 76

Eingereicht: 16. Dez. 2023

Akzeptiert: 15. Mai 2024

DOI: https://doi.org/10.2478/fiqf-2024-0012

Schlüsselwörter
Creditworthiness, Credit Rating Model, Neural Networking, Test Data, Accuracy Model testing

© 2024 Prashant Ubarhande et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Modelling Financial Variables Using Neural Networking to Access Creditworthiness

Online veröffentlicht: 20. Juni 2024

Seitenbereich: 62 - 76

Eingereicht: 16. Dez. 2023

Akzeptiert: 15. Mai 2024

DOI: https://doi.org/10.2478/fiqf-2024-0012

SchlüsselwörterCreditworthiness, Credit Rating Model, Neural Networking, Test Data, Accuracy Model testing

© 2024 Prashant Ubarhande et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Creditworthiness, Credit Rating Model, Neural Networking, Test Data, Accuracy Model testing