Default Prediction of Internet Finance Users Based on Imbalance-XGBoost
Fast and accurate identification of financial fraud is a challenge in Internet finance. Based on the characteristics of imbalanced distribution of Internet financial data, this paper integrates machine learning methods and Internet financial data to propose a prediction model for loan defaults, and...
| Main Author: | |
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| Format: | Article |
| Language: | English |
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Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2023-01-01
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| Series: | Tehnički Vjesnik |
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| Online Access: | https://hrcak.srce.hr/file/433792 |
| _version_ | 1797206558397956096 |
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| author | Wenlong Lai |
| author_facet | Wenlong Lai |
| author_sort | Wenlong Lai |
| collection | DOAJ |
| description | Fast and accurate identification of financial fraud is a challenge in Internet finance. Based on the characteristics of imbalanced distribution of Internet financial data, this paper integrates machine learning methods and Internet financial data to propose a prediction model for loan defaults, and proves its effectiveness and generalizability through empirical research. In this paper, we introduce a processing method (link processing method) for imbalance data based on the traditional early warning model. In this paper, we conduct experiments using the financial dataset of Lending Club platform and prove that our model is superior to XGBoost, NGBoost, Ada Boost, and GBDT in the prediction of default risk. |
| first_indexed | 2024-04-24T09:08:56Z |
| format | Article |
| id | doaj.art-f403b7e3ef5c46b5815c3a96a9a12f91 |
| institution | Directory Open Access Journal |
| issn | 1330-3651 1848-6339 |
| language | English |
| last_indexed | 2024-04-24T09:08:56Z |
| publishDate | 2023-01-01 |
| publisher | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
| record_format | Article |
| series | Tehnički Vjesnik |
| spelling | doaj.art-f403b7e3ef5c46b5815c3a96a9a12f912024-04-15T18:25:47ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in OsijekTehnički Vjesnik1330-36511848-63392023-01-0130377978610.17559/TV-20230302000395Default Prediction of Internet Finance Users Based on Imbalance-XGBoostWenlong Lai0Statistics and Information Department, Shanghai Zheshang Borui Asset Management Research Company, Shanghai 200023, ChinaFast and accurate identification of financial fraud is a challenge in Internet finance. Based on the characteristics of imbalanced distribution of Internet financial data, this paper integrates machine learning methods and Internet financial data to propose a prediction model for loan defaults, and proves its effectiveness and generalizability through empirical research. In this paper, we introduce a processing method (link processing method) for imbalance data based on the traditional early warning model. In this paper, we conduct experiments using the financial dataset of Lending Club platform and prove that our model is superior to XGBoost, NGBoost, Ada Boost, and GBDT in the prediction of default risk.https://hrcak.srce.hr/file/433792imbalanced datainternet financeP2P lendingXGBoost |
| spellingShingle | Wenlong Lai Default Prediction of Internet Finance Users Based on Imbalance-XGBoost Tehnički Vjesnik imbalanced data internet finance P2P lending XGBoost |
| title | Default Prediction of Internet Finance Users Based on Imbalance-XGBoost |
| title_full | Default Prediction of Internet Finance Users Based on Imbalance-XGBoost |
| title_fullStr | Default Prediction of Internet Finance Users Based on Imbalance-XGBoost |
| title_full_unstemmed | Default Prediction of Internet Finance Users Based on Imbalance-XGBoost |
| title_short | Default Prediction of Internet Finance Users Based on Imbalance-XGBoost |
| title_sort | default prediction of internet finance users based on imbalance xgboost |
| topic | imbalanced data internet finance P2P lending XGBoost |
| url | https://hrcak.srce.hr/file/433792 |
| work_keys_str_mv | AT wenlonglai defaultpredictionofinternetfinanceusersbasedonimbalancexgboost |