# Gradient Boosting Survival Tree with Applications in Credit Scoring

arXiv: https://arxiv.org/abs/1908.03385 · HTML: https://mlsanity.com/paper/1908.03385/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2019.

## Built on by (most cited first, 4 of 4)

- [Interpretable Prediction and Feature Selection for Survival Analysis](https://mlsanity.com/paper/2404.14689/) (2024)
- [FSL-BDP: Federated Survival Learning with Bayesian Differential Privacy for Credit Risk Modeling](https://mlsanity.com/paper/2601.11134/) (2026)
- [PDx -- Adaptive Credit Risk Forecasting Model in Digital Lending using Machine Learning Operations](https://mlsanity.com/paper/2512.22305/) (2025)
- [Generalized Bayesian Ensemble Survival Tree (GBEST) model](https://mlsanity.com/paper/2503.11738/) (2025)

## Abstract (arXiv)

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.