# Gradient Boosting Survival Tree with Applications in Credit Scoring

## TL;DR

This paper introduces a novel gradient boosting survival tree model tailored for credit scoring, effectively handling heterogeneous data and improving risk prediction accuracy in consumer finance applications.

## Contribution

The paper presents a nonparametric ensemble tree model called GBST that extends survival trees with gradient boosting, optimizing survival probabilities across time periods.

## Key findings

- GBST outperforms existing survival models in C-index, KS index, and AUC.
- The model effectively handles large-scale, heterogeneous financial data.
- Application demonstrates improved credit risk quantification.

## 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 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.