A dynamic credit scoring model based on survival gradient boosting decision tree approach

    Yufei Xia Affiliation
    ; Lingyun He Affiliation
    ; Yinguo Li Affiliation
    ; Yating Fu Affiliation
    ; Yixin Xu Affiliation


Credit scoring, which is typically transformed into a classification problem, is a powerful tool to manage credit risk since it forecasts the probability of default (PD) of a loan application. However, there is a growing trend of integrating survival analysis into credit scoring to provide a dynamic prediction on PD over time and a clear explanation on censoring. A novel dynamic credit scoring model (i.e., SurvXGBoost) is proposed based on survival gradient boosting decision tree (GBDT) approach. Our proposal, which combines survival analysis and GBDT approach, is expected to enhance predictability relative to statistical survival models. The proposed method is compared with several common benchmark models on a real-world consumer loan dataset. The results of out-of-sample and out-of-time validation indicate that SurvXGBoost outperform the benchmarks in terms of predictability and misclassification cost. The incorporation of macroeconomic variables can further enhance performance of survival models. The proposed SurvXGBoost meanwhile maintains some interpretability since it provides information on feature importance.

First published online 14 December 2020

Keyword : credit scoring, survival analysis, survival gradient boosting decision tree, probability of default, consumer loan, machine learning

How to Cite
Xia, Y., He, L., Li, Y., Fu, Y., & Xu, Y. (2021). A dynamic credit scoring model based on survival gradient boosting decision tree approach. Technological and Economic Development of Economy, 27(1), 96-119.
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Jan 18, 2021
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