A DYNAMIC CREDIT SCORING MODEL BASED ON SURVIVAL GRADIENT BOOSTING DECISION TREE APPROACH

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.

Automated summary

A novel dynamic credit scoring model, SurvXGBoost, is proposed by combining survival analysis and GBDT approach, which is expected to enhance predictability compared to statistical survival models. Empirical results demonstrate that SurvXGBoost outperforms other benchmark models on a real-world dataset, particularly in terms of misclassification cost.