  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 minimizing the
negative log-likelihood in an additive manner. The proposed model optimizes 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.
