Gradient boosting survival tree with applications in credit scoring

Miaojun Bai, Yan Zheng and Yun Shen

Journal of the Operational Research Society, 2022, vol. 73, issue 1, 39-55

Abstract: Credit scoring plays a vital role in the field of consumer finance. Survival analysis provides an advanced solution to the credit-scoring problem by quantifying the probability of survival time. In order to deal with highly heterogeneous industrial data collected in Chinese market of consumer finance, we propose a nonparametric ensemble tree model called gradient boosting survival tree (GBST) that extends the survival tree models with a gradient boosting algorithm. The survival tree ensemble is learned by minimising the negative log-likelihood in an additive manner. The proposed model optimises the survival probability simultaneously for each time period, which can reduce the overall error significantly. Finally, as a test of the applicability, we apply the GBST model to quantify the credit risk with large-scale real market datasets. The results show that the GBST model outperforms the existing survival models measured by the concordance index (C-index), Kolmogorov–Smirnov (KS) index, as well as by the area under the receiver operating characteristic curve (AUC) of each time period.

Date: 2022
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DOI: 10.1080/01605682.2021.1919035

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