scholarly journals Credit scoring with macroeconomic variables using survival analysis

2009 ◽  
Vol 60 (12) ◽  
pp. 1699-1707 ◽  
Author(s):  
T Bellotti ◽  
J Crook
2017 ◽  
Vol 68 (6) ◽  
pp. 652-665 ◽  
Author(s):  
Lore Dirick ◽  
Gerda Claeskens ◽  
Bart Baesens

2013 ◽  
Vol 13 (1) ◽  
Author(s):  
Shi-Wei Shen ◽  
Tri-Dung Nguyen ◽  
Udechukwu Ojiako

Orientation: The article discussed the importance of rigour in credit risk assessment.Research purpose: The purpose of this empirical paper was to examine the predictive performance of credit scoring systems in Taiwan.Motivation for the study: Corporate lending remains a major business line for financial institutions. However, in light of the recent global financial crises, it has become extremely important for financial institutions to implement rigorous means of assessing clients seeking access to credit facilities.Research design, approach and method: Using a data sample of 10 349 observations drawn between 1992 and 2010, logistic regression models were utilised to examine the predictive performance of credit scoring systems.Main findings: A test of Goodness of fit demonstrated that credit scoring models that incorporated the Taiwan Corporate Credit Risk Index (TCRI), micro- and also macroeconomic variables possessed greater predictive power. This suggests that macroeconomic variables do have explanatory power for default credit risk.Practical/managerial implications: The originality in the study was that three models were developed to predict corporate firms’ defaults based on different microeconomic and macroeconomic factors such as the TCRI, asset growth rates, stock index and gross domestic product.Contribution/value-add: The study utilises different goodness of fits and receiver operator characteristics during the examination of the robustness of the predictive power of these factors.


2020 ◽  
Vol 0 (0) ◽  
pp. 1-24
Author(s):  
Yufei Xia ◽  
Lingyun He ◽  
Yinguo Li ◽  
Yating Fu ◽  
Yixin Xu

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.


2020 ◽  
Vol 67 (6) ◽  
pp. 712-722
Author(s):  
Sebastian Gmeinwieser ◽  
Kai Sebastian Schneider ◽  
Maximilian Bardo ◽  
Timo Brockmeyer ◽  
York Hagmayer

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