The Predictive Power of Credit Scores: Examining Default Probability in Taiwanese Credit Card Clients

Research Article
Open access

The Predictive Power of Credit Scores: Examining Default Probability in Taiwanese Credit Card Clients

Yaoxin Xiao 1*
  • 1 University of Toronto    
  • *corresponding author yaoxin.xiao@mail.utoronto.ca
Published on 10 November 2023 | https://doi.org/10.54254/2754-1169/42/20232097
AEMPS Vol.42
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-83558-105-6
ISBN (Online): 978-1-83558-106-3

Abstract

The concept of a scorecard originated from the need to establish a standardized and objective approach to evaluate credit applicants. Various techniques have been utilized to build scoring model. This research chooses Logistic regression to construct a scorecard using SPSS modeler. In this way, the study enhances the understanding of the relationship between credit scores and default behavior. By using a scorecard constructed through logistic regression, the study provides a comprehensive and interpretable model for evaluating creditworthiness. The study also employs linear probability models (LPM), logit, and probit models to assess the predictive power of credit scores on default probability. By utilizing these statistical techniques, the research presents robust empirical evidence on the significance of credit scores in predicting default behavior. Moreover, the research paper systematically analyzes prediction effects with and without control variables. By incorporating control variables such as demographic characteristics, the study adds depth to the understanding of scoring models. Overall, the findings provide valuable guidance for credit risk assessment practices and contribute to the ongoing development of effective credit evaluation frameworks.

Keywords:

credit scoring, predictions, statistical learning, machine learning

Xiao,Y. (2023). The Predictive Power of Credit Scores: Examining Default Probability in Taiwanese Credit Card Clients. Advances in Economics, Management and Political Sciences,42,139-147.
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References

[1]. Gjini, V., & Koprencka, L. (2018). Relationship Between Economic Factors and Non- Performing Loans- the Case of Albania. European Journal of Economics and Business Studies, 10(1), 253.

[2]. Louzada, F., Ara, A., & Fernandes, G. B. (2016). Classification methods applied to credit scoring: Systematic review and overall comparison. Surveys in Operations Research and Management Science, 21(2), 117–134.

[3]. Ripley, B. D. (1996). Pattern Recognition and Neural Networks. Cambridge University Press.

[4]. Feng, L., Yao, Y., & Jin, B. (2010). Research on Credit Scoring Model with SVM for Network Management. ResearchGate. https://www.researchgate.net/publication/268266895_Research_on_Credit_Scoring_Model_with_SVM_for_Network_Management

[5]. Berkson, J. (1944). Application of the Logistic Function to Bio-Assay. Journal of the American Statistical Association, 39(227), 357–365.

[6]. Dik, V. V. (2014). Decision support methods in balanced scorecard. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, 4, 120–126.

[7]. Banasik, J., Crook, J., & Thomas, L. (2003). Sample Selection Bias in Credit Scoring Models. The Journal of the Operational Research Society, 54(8), 822–832.

[8]. Traczynski, J. (2017). Firm Default Prediction: A Bayesian Model-Averaging Approach. Journal of Financial and Quantitative Analysis, 52(3), 1211–1245.

[9]. Bücker, M., Szepannek, G., Gosiewska, A., & Biecek, P. (2021). Transparency, auditability, and explainability of machine learning models in credit scoring. Journal of the Operational Research Society, 73(1), 70–90.

[10]. Siddiqi, N. (2005). Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring. http://ci.nii.ac.jp/ncid/BA75777283

[11]. Setting Options for the Binning Node. (n.d.). IBM SPSS modeler help. Retrieved June 23. 2023 from http://127.0.0.1:50727/help/index.jsp?topic=/com.ibm.spss.modeler.help/clementine/binning_settingstab.htm.

[12]. Eeckhoudt, L., & Godfroid, P. (2000). Risk Aversion and the Value of Information. Journal of Economic Education, 31(4), 382–388. https://doi.org/10.1080/00220480009596456

[13]. Dastile, X., Celik, T., & Potsane, M. M. (2020). Statistical and machine learning models in credit scoring: A systematic literature survey. Applied Soft Computing, 91, 106263.

[14]. Zeng, G. (2013). Metric Divergence Measures and Information Value in Credit Scoring. Journal of Mathematics, 2013, 1–10.

[15]. Mize, T. D., Doan, L., & Long, J. S. (2019). A General Framework for Comparing Predictions and Marginal Effects across Models. Sociological Methodology, 49(1), 152–189.

[16]. Nguyen Viet, C. (2007). The average treatment effect and average partial effect in nonlinear models. Mpra.ub.uni-Muenchen.de. https://mpra.ub.uni-muenchen.de/44483/

Cite this article

Xiao,Y. (2023). The Predictive Power of Credit Scores: Examining Default Probability in Taiwanese Credit Card Clients. Advances in Economics, Management and Political Sciences,42,139-147.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 7th International Conference on Economic Management and Green Development

ISBN：978-1-83558-105-6(Print) / 978-1-83558-106-3(Online)
Editor：Canh Thien Dang
Conference website: https://www.icemgd.org/
Conference date: 6 August 2023
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.42
ISSN：2754-1169(Print) / 2754-1177(Online)

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References

[1]. Gjini, V., & Koprencka, L. (2018). Relationship Between Economic Factors and Non- Performing Loans- the Case of Albania. European Journal of Economics and Business Studies, 10(1), 253.

[2]. Louzada, F., Ara, A., & Fernandes, G. B. (2016). Classification methods applied to credit scoring: Systematic review and overall comparison. Surveys in Operations Research and Management Science, 21(2), 117–134.

[3]. Ripley, B. D. (1996). Pattern Recognition and Neural Networks. Cambridge University Press.

[4]. Feng, L., Yao, Y., & Jin, B. (2010). Research on Credit Scoring Model with SVM for Network Management. ResearchGate. https://www.researchgate.net/publication/268266895_Research_on_Credit_Scoring_Model_with_SVM_for_Network_Management

[5]. Berkson, J. (1944). Application of the Logistic Function to Bio-Assay. Journal of the American Statistical Association, 39(227), 357–365.

[6]. Dik, V. V. (2014). Decision support methods in balanced scorecard. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, 4, 120–126.

[7]. Banasik, J., Crook, J., & Thomas, L. (2003). Sample Selection Bias in Credit Scoring Models. The Journal of the Operational Research Society, 54(8), 822–832.

[8]. Traczynski, J. (2017). Firm Default Prediction: A Bayesian Model-Averaging Approach. Journal of Financial and Quantitative Analysis, 52(3), 1211–1245.

[9]. Bücker, M., Szepannek, G., Gosiewska, A., & Biecek, P. (2021). Transparency, auditability, and explainability of machine learning models in credit scoring. Journal of the Operational Research Society, 73(1), 70–90.

[10]. Siddiqi, N. (2005). Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring. http://ci.nii.ac.jp/ncid/BA75777283

[11]. Setting Options for the Binning Node. (n.d.). IBM SPSS modeler help. Retrieved June 23. 2023 from http://127.0.0.1:50727/help/index.jsp?topic=/com.ibm.spss.modeler.help/clementine/binning_settingstab.htm.

[12]. Eeckhoudt, L., & Godfroid, P. (2000). Risk Aversion and the Value of Information. Journal of Economic Education, 31(4), 382–388. https://doi.org/10.1080/00220480009596456

[13]. Dastile, X., Celik, T., & Potsane, M. M. (2020). Statistical and machine learning models in credit scoring: A systematic literature survey. Applied Soft Computing, 91, 106263.

[14]. Zeng, G. (2013). Metric Divergence Measures and Information Value in Credit Scoring. Journal of Mathematics, 2013, 1–10.

[15]. Mize, T. D., Doan, L., & Long, J. S. (2019). A General Framework for Comparing Predictions and Marginal Effects across Models. Sociological Methodology, 49(1), 152–189.

[16]. Nguyen Viet, C. (2007). The average treatment effect and average partial effect in nonlinear models. Mpra.ub.uni-Muenchen.de. https://mpra.ub.uni-muenchen.de/44483/

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