Author(s)

Xigan Sun ¹

Affiliation(s)

¹ School of Aeronautical Science and Engineering, Beihang University, Beijing 100000, China

Corresponding Author

Xigan Sun

ABSTRACT

In recent years, with the rapid development of the online credit industry and the wide application of big data technology, using an integrated learning model to evaluate loan risk quickly and accurately has been a concern by academics and practitioners. In order to predict the default rate of the financing projects of the online loan platform with high accuracy and efficiency, this paper adopts the XGBoost model based on the importance of certain features to process loan application data of an online loan platform and establishes the default rate prediction model of online loan projects. Ten years' loan application data of American online lending platforms were selected to verify the model, and the prediction results were compared with those of Random Forest (RF) and LightGBM. The results show that the XGBoost model based on the optimization derivation and the second-order Taylor expansion has higher accuracy in the evaluation.

KEYWORDS

Default Rate, Financing Projects, Online Lending, XGBoost Model

CITE THIS PAPER

Xigan Sun, Research on Default Rate of Financing Projects of Online Lending Platform Based on XGBoost Model. Financial Engineering and Risk Management (2021) 4: 60-68. DOI: http://dx.doi.org/10.23977/ferm.2021.040104

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