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Leveraging Data Science Technology for Advancing Credit Risk Assessment

The evaluation of credit risk (CR) has become prominent in recent years, particularly among banks, as default rates are on the rise and economic insecurity remains persistent. Traditional credit scoring techniques oftentimes are inadequate and provide little means for risk estimation, necessitati...

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Main Authors:	Deepak, Bhanot, Sadaf, Hashmi, K., Suneetha, Devendra, Parmar
Format:	Article
Language:	English English
Published:	INTI International University 2024
Subjects:	HG Finance QA75 Electronic computers. Computer science QA76 Computer software
Online Access:	http://eprints.intimal.edu.my/2019/1/jods2024_38.pdf http://eprints.intimal.edu.my/2019/2/559 http://eprints.intimal.edu.my/2019/ http://ipublishing.intimal.edu.my/jods.html
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id	my-inti-eprints.2019
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spelling	my-inti-eprints.20192024-11-08T00:33:37Z http://eprints.intimal.edu.my/2019/ Leveraging Data Science Technology for Advancing Credit Risk Assessment Deepak, Bhanot Sadaf, Hashmi K., Suneetha Devendra, Parmar HG Finance QA75 Electronic computers. Computer science QA76 Computer software The evaluation of credit risk (CR) has become prominent in recent years, particularly among banks, as default rates are on the rise and economic insecurity remains persistent. Traditional credit scoring techniques oftentimes are inadequate and provide little means for risk estimation, necessitating the development of new models using data science methodologies. In this study, a novel Intelligent Dwarf Mongoose tuned Light Gradient Boosting Machine (IDM-LGBM) model that boosts the accuracy of CR and improves forecasting performance, is introduced. The Light Gradient Boosting Machine model's hyperparameters were optimized using the Intelligent Dwarf Mongoose technique, improving the model's predictive strength. The CR dataset was gathered from the Kaggle platform. The data is then pre-processed using Z-score normalization. To evaluate the efficiency of the suggested IDM-LGBM technique, which has been implemented employing a Python platform. Results show that the IDM-LGBM model performed significantly better than conventional methods in terms of recall (98.1%), accuracy (97.2%), F1-score (97.4%), and precision (96.5%). Subsequent studies could concentrate on addressing real-time data streams and enhancing models to respond to the changing credit environment. INTI International University 2024-11 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2019/1/jods2024_38.pdf text en cc_by_4 http://eprints.intimal.edu.my/2019/2/559 Deepak, Bhanot and Sadaf, Hashmi and K., Suneetha and Devendra, Parmar (2024) Leveraging Data Science Technology for Advancing Credit Risk Assessment. Journal of Data Science, 2024 (38). pp. 1-12. ISSN 2805-5160 http://ipublishing.intimal.edu.my/jods.html
institution	INTI International University
building	INTI Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	INTI International University
content_source	INTI Institutional Repository
url_provider	http://eprints.intimal.edu.my
language	English English
topic	HG Finance QA75 Electronic computers. Computer science QA76 Computer software
spellingShingle	HG Finance QA75 Electronic computers. Computer science QA76 Computer software Deepak, Bhanot Sadaf, Hashmi K., Suneetha Devendra, Parmar Leveraging Data Science Technology for Advancing Credit Risk Assessment
description	The evaluation of credit risk (CR) has become prominent in recent years, particularly among banks, as default rates are on the rise and economic insecurity remains persistent. Traditional credit scoring techniques oftentimes are inadequate and provide little means for risk estimation, necessitating the development of new models using data science methodologies. In this study, a novel Intelligent Dwarf Mongoose tuned Light Gradient Boosting Machine (IDM-LGBM) model that boosts the accuracy of CR and improves forecasting performance, is introduced. The Light Gradient Boosting Machine model's hyperparameters were optimized using the Intelligent Dwarf Mongoose technique, improving the model's predictive strength. The CR dataset was gathered from the Kaggle platform. The data is then pre-processed using Z-score normalization. To evaluate the efficiency of the suggested IDM-LGBM technique, which has been implemented employing a Python platform. Results show that the IDM-LGBM model performed significantly better than conventional methods in terms of recall (98.1%), accuracy (97.2%), F1-score (97.4%), and precision (96.5%). Subsequent studies could concentrate on addressing real-time data streams and enhancing models to respond to the changing credit environment.
format	Article
author	Deepak, Bhanot Sadaf, Hashmi K., Suneetha Devendra, Parmar
author_facet	Deepak, Bhanot Sadaf, Hashmi K., Suneetha Devendra, Parmar
author_sort	Deepak, Bhanot
title	Leveraging Data Science Technology for Advancing Credit Risk Assessment
title_short	Leveraging Data Science Technology for Advancing Credit Risk Assessment
title_full	Leveraging Data Science Technology for Advancing Credit Risk Assessment
title_fullStr	Leveraging Data Science Technology for Advancing Credit Risk Assessment
title_full_unstemmed	Leveraging Data Science Technology for Advancing Credit Risk Assessment
title_sort	leveraging data science technology for advancing credit risk assessment
publisher	INTI International University
publishDate	2024
url	http://eprints.intimal.edu.my/2019/1/jods2024_38.pdf http://eprints.intimal.edu.my/2019/2/559 http://eprints.intimal.edu.my/2019/ http://ipublishing.intimal.edu.my/jods.html
_version_	1817849521147740160
score	13.223943

Leveraging Data Science Technology for Advancing Credit Risk Assessment

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