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: | , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
INTI International University
2024
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| Subjects: | |
| 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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| Summary: | 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. |
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