Explainable AI based LightGBM prediction model to predict default borrower in social lending platform
This paper proposes an explainable AI (XAI)-based prediction model utilizing the LightGBM algorithm to predict the likelihood of borrower default on a social lending platform. The dataset used in this study was obtained from Lending Club and consisted of various borrower characteristics and loan fea...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Intelligent Systems with Applications |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667305325000407 |
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| author | Li-Hua Li Alok Kumar Sharma Sheng-Tzong Cheng |
| author_facet | Li-Hua Li Alok Kumar Sharma Sheng-Tzong Cheng |
| author_sort | Li-Hua Li |
| collection | DOAJ |
| description | This paper proposes an explainable AI (XAI)-based prediction model utilizing the LightGBM algorithm to predict the likelihood of borrower default on a social lending platform. The dataset used in this study was obtained from Lending Club and consisted of various borrower characteristics and loan features. The proposed model not only provides high accuracy (0.87) in predicting defaulted borrowers, but also offers an explanation of the factors that contribute to the prediction. The model interpretability is facilitated through LIME and SHAP values, where SHAP values provide insights into the feature importance for the prediction. The outcome shows that the proposed model outperforms traditional approaches and delivers valuable insights for lending decision-making. The proposed model can be useful for lenders and regulators in the lending industry to improve decision-making processes and mitigating risk. Moreover, the XAI approach enables transparency and accountability in the decision-making process, making it more understandable and trustworthy for stakeholders. |
| format | Article |
| id | doaj-art-4afca19946b6451c9a68ea5d96b3c730 |
| institution | OA Journals |
| issn | 2667-3053 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Intelligent Systems with Applications |
| spelling | doaj-art-4afca19946b6451c9a68ea5d96b3c7302025-08-20T01:53:04ZengElsevierIntelligent Systems with Applications2667-30532025-06-012620051410.1016/j.iswa.2025.200514Explainable AI based LightGBM prediction model to predict default borrower in social lending platformLi-Hua Li0Alok Kumar Sharma1Sheng-Tzong Cheng2Department of Information Management, Chaoyang University of Technology, Wufeng, Taichung City, 413310, Taiwan (R.O.C.)Department of Information Management, Chaoyang University of Technology, Wufeng, Taichung City, 413310, Taiwan (R.O.C.); Department of Computer Science and Information Engineering, Chaoyang University of Technology, Wufeng, Taichung City, 413310, Taiwan (R.O.C.); Corresponding author at: Department of Computer Science and Information Engineering, Chaoyang University of Technology, Wufeng, Taichung City, 413310, Taiwan (R.O.C.).Department of Computer Science & Information Engineering, National Cheng Kung University, Tainan City, 701, Taiwan (R.O.C.)This paper proposes an explainable AI (XAI)-based prediction model utilizing the LightGBM algorithm to predict the likelihood of borrower default on a social lending platform. The dataset used in this study was obtained from Lending Club and consisted of various borrower characteristics and loan features. The proposed model not only provides high accuracy (0.87) in predicting defaulted borrowers, but also offers an explanation of the factors that contribute to the prediction. The model interpretability is facilitated through LIME and SHAP values, where SHAP values provide insights into the feature importance for the prediction. The outcome shows that the proposed model outperforms traditional approaches and delivers valuable insights for lending decision-making. The proposed model can be useful for lenders and regulators in the lending industry to improve decision-making processes and mitigating risk. Moreover, the XAI approach enables transparency and accountability in the decision-making process, making it more understandable and trustworthy for stakeholders.http://www.sciencedirect.com/science/article/pii/S2667305325000407Social lendingCredit risk predictionMachine learningExplainable AIRecursive feature elimination |
| spellingShingle | Li-Hua Li Alok Kumar Sharma Sheng-Tzong Cheng Explainable AI based LightGBM prediction model to predict default borrower in social lending platform Intelligent Systems with Applications Social lending Credit risk prediction Machine learning Explainable AI Recursive feature elimination |
| title | Explainable AI based LightGBM prediction model to predict default borrower in social lending platform |
| title_full | Explainable AI based LightGBM prediction model to predict default borrower in social lending platform |
| title_fullStr | Explainable AI based LightGBM prediction model to predict default borrower in social lending platform |
| title_full_unstemmed | Explainable AI based LightGBM prediction model to predict default borrower in social lending platform |
| title_short | Explainable AI based LightGBM prediction model to predict default borrower in social lending platform |
| title_sort | explainable ai based lightgbm prediction model to predict default borrower in social lending platform |
| topic | Social lending Credit risk prediction Machine learning Explainable AI Recursive feature elimination |
| url | http://www.sciencedirect.com/science/article/pii/S2667305325000407 |
| work_keys_str_mv | AT lihuali explainableaibasedlightgbmpredictionmodeltopredictdefaultborrowerinsociallendingplatform AT alokkumarsharma explainableaibasedlightgbmpredictionmodeltopredictdefaultborrowerinsociallendingplatform AT shengtzongcheng explainableaibasedlightgbmpredictionmodeltopredictdefaultborrowerinsociallendingplatform |