Enhancing loan approval decision-making: an interpretable machine learning approach using LightGBM for digital economy development / Teuku Rizky Noviandy, Ghalieb Mutig Idroes and Irsan Hardi
This study aims to enhance loan approval decision-making in the digital economy using an interpretable machine learning approach. The primary research question investigates how integrating an interpretable machine learning approach can improve the accuracy and transparency of loan approval processes...
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| Format: | Article |
| Language: | en |
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Universiti Teknologi MARA
2024
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| Online Access: | https://ir.uitm.edu.my/id/eprint/62001/1/62001.pdf https://ir.uitm.edu.my/id/eprint/62001/ |
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| author | Noviandy, Teuku Rizky Idroes, Ghalieb Mutig Hardi, Irsan |
| author_facet | Noviandy, Teuku Rizky Idroes, Ghalieb Mutig Hardi, Irsan |
| author_sort | Noviandy, Teuku Rizky |
| building | Tun Abdul Razak Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Mara |
| content_source | UiTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | This study aims to enhance loan approval decision-making in the digital economy using an interpretable machine learning approach. The primary research question investigates how integrating an interpretable machine learning approach can improve the accuracy and transparency of loan approval processes. We employed LightGBM, a gradient-boosting framework for loan approval classification, optimized via Random Search hyperparameter tuning and validated using 10-fold cross-validation. We incorporated the Shapley Additive exPlanations (SHAP) framework to address the challenge of interpretability in machine learning. The LightGBM model outperformed conventional algorithms (Decision Tree, Random Forest, AdaBoost, and Extra Trees) in accuracy (98.13%), precision (97.78%), recall (97.17%), and F1-score (97.48%). The study demonstrates that using an interpretable machine learning approach with LightGBM and SHAP can significantly improve the accuracy and transparency of loan approval decisions. This method offers a promising avenue for financial institutions to enhance their loan approval mechanisms, ensuring more reliable, efficient, and transparent decision-making in the digital economy. The study also underscores the importance of interpretability in deploying machine learning solutions in sectors with significant socio-economic impacts. |
| format | Article |
| id | my.uitm.ir-62001 |
| institution | Universiti Teknologi Mara |
| language | en |
| publishDate | 2024 |
| publisher | Universiti Teknologi MARA |
| record_format | eprints |
| spelling | my.uitm.ir-620012024-04-18T08:41:26Z https://ir.uitm.edu.my/id/eprint/62001/ Enhancing loan approval decision-making: an interpretable machine learning approach using LightGBM for digital economy development / Teuku Rizky Noviandy, Ghalieb Mutig Idroes and Irsan Hardi mjoc Noviandy, Teuku Rizky Idroes, Ghalieb Mutig Hardi, Irsan Machine learning This study aims to enhance loan approval decision-making in the digital economy using an interpretable machine learning approach. The primary research question investigates how integrating an interpretable machine learning approach can improve the accuracy and transparency of loan approval processes. We employed LightGBM, a gradient-boosting framework for loan approval classification, optimized via Random Search hyperparameter tuning and validated using 10-fold cross-validation. We incorporated the Shapley Additive exPlanations (SHAP) framework to address the challenge of interpretability in machine learning. The LightGBM model outperformed conventional algorithms (Decision Tree, Random Forest, AdaBoost, and Extra Trees) in accuracy (98.13%), precision (97.78%), recall (97.17%), and F1-score (97.48%). The study demonstrates that using an interpretable machine learning approach with LightGBM and SHAP can significantly improve the accuracy and transparency of loan approval decisions. This method offers a promising avenue for financial institutions to enhance their loan approval mechanisms, ensuring more reliable, efficient, and transparent decision-making in the digital economy. The study also underscores the importance of interpretability in deploying machine learning solutions in sectors with significant socio-economic impacts. Universiti Teknologi MARA 2024-04 Article PeerReviewed text en https://ir.uitm.edu.my/id/eprint/62001/1/62001.pdf Enhancing loan approval decision-making: an interpretable machine learning approach using LightGBM for digital economy development / Teuku Rizky Noviandy, Ghalieb Mutig Idroes and Irsan Hardi. (2024) Malaysian Journal of Computing (MJoC) <https://ir.uitm.edu.my/view/publication/Malaysian_Journal_of_Computing_=28MJoC=29/>, 9 (1): 6. pp. 1734-1745. ISSN 2600-8238 |
| spellingShingle | Machine learning Noviandy, Teuku Rizky Idroes, Ghalieb Mutig Hardi, Irsan Enhancing loan approval decision-making: an interpretable machine learning approach using LightGBM for digital economy development / Teuku Rizky Noviandy, Ghalieb Mutig Idroes and Irsan Hardi |
| title | Enhancing loan approval decision-making: an interpretable machine learning approach using LightGBM for digital economy development / Teuku Rizky Noviandy, Ghalieb Mutig Idroes and Irsan Hardi |
| title_full | Enhancing loan approval decision-making: an interpretable machine learning approach using LightGBM for digital economy development / Teuku Rizky Noviandy, Ghalieb Mutig Idroes and Irsan Hardi |
| title_fullStr | Enhancing loan approval decision-making: an interpretable machine learning approach using LightGBM for digital economy development / Teuku Rizky Noviandy, Ghalieb Mutig Idroes and Irsan Hardi |
| title_full_unstemmed | Enhancing loan approval decision-making: an interpretable machine learning approach using LightGBM for digital economy development / Teuku Rizky Noviandy, Ghalieb Mutig Idroes and Irsan Hardi |
| title_short | Enhancing loan approval decision-making: an interpretable machine learning approach using LightGBM for digital economy development / Teuku Rizky Noviandy, Ghalieb Mutig Idroes and Irsan Hardi |
| title_sort | enhancing loan approval decision-making: an interpretable machine learning approach using lightgbm for digital economy development / teuku rizky noviandy, ghalieb mutig idroes and irsan hardi |
| topic | Machine learning |
| url | https://ir.uitm.edu.my/id/eprint/62001/1/62001.pdf https://ir.uitm.edu.my/id/eprint/62001/ |
| url_provider | http://ir.uitm.edu.my/ |