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- Title
Default Prediction of Internet Finance Users Based on Imbalance-XGBoost.
- Authors
Wenlong LAI
- Abstract
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.
- Subjects
LENDINGCLUB Corp.; INTERNET users; DEFAULT (Finance); COUNTERPARTY risk; LOANS; FRAUD
- Publication
Technical Gazette / Tehnički Vjesnik, 2023, Vol 30, Issue 3, p779
- ISSN
1330-3651
- Publication type
Article
- DOI
10.17559/TV-20230302000395