From credit scoring to regulatory scoring: comparing credit scoring models from a regulatory perspective
Abstract
Conventional credit scoring models evaluated by predictive accuracy or profitability typically serve the financial institutions and can hardly reflect their contribution on financial stability. To remedy this, we develop a novel regulatory scoring framework to quantify and compare the corresponding regulatory capital charge errors of credit scoring models. As an application of RegTech, the proposed framework considers the characteristic of example-dependence and costsensitivity in credit scoring, which is expected to enhance the ability of risk absorption of financial institutions and thus benefit the regulators. Validated on two real-world credit datasets, empirical results reveal that credit scoring models with good predictive accuracy or profitability do not necessarily provide low capital charge requirement error, which further highlights the importance of regulatory scoring framework. The family of gradient boosting decision tree (GBDT) provides significantly better average performance than industry benchmarks and deep multilayer perceptron network, especially when financial stability is the primary focus. To further examine the robustness of the proposed regulatory scoring, sampling techniques, cut-off value modification, and probability calibration are employed within the framework and the main conclusions hold in most cases. Furthermore, the analysis on the interpretability via TreeSHAP algorithm alleviates the concerns on transparency of GBDT-based models, and confirms the important roles of loan characteristics, borrowers’ solvency and creditworthiness as powerful predictors in credit scoring. Finally, the managerial implications for both financial institutions and regulators are discussed.
Keyword : credit scoring, RegTech, regulatory scoring, probability of default, financial regulation, gradient boosting decision tree
How to Cite
Xia, Y., Liao, Z., Xu, J., & Li, Y. (2022). From credit scoring to regulatory scoring: comparing credit scoring models from a regulatory perspective. Technological and Economic Development of Economy, 28(6), 1954–1990. https://doi.org/10.3846/tede.2022.17045
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Chi, B.-W., & Hsu, C.-C. (2012). A hybrid approach to integrate genetic algorithm into dual scoring model in enhancing the performance of credit scoring model. Expert Systems with Applications, 39(3), 2650–2661. https://doi.org/10.1016/j.eswa.2011.08.120
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Eisenbeis, R. A. (1977). Pitfalls in the application of discriminant analysis in business, finance, and economics. The Journal of Finance, 32(3), 875–900. https://doi.org/10.2307/2326320
Feng, X., Xiao, Z., Zhong, B., Dong, Y., & Qiu, J. (2019). Dynamic weighted ensemble classification for credit scoring using Markov Chain. Applied Intelligence, 49(2), 555–568. https://doi.org/10.1007/s10489-018-1253-8
Finlay, S. (2010). Credit scoring for profitability objectives. European Journal of Operational Research, 202(2), 528–537. https://doi.org/10.1016/j.ejor.2009.05.025
Florez-Lopez, R., & Ramon-Jeronimo, J. M. (2015). Enhancing accuracy and interpretability of ensemble strategies in credit risk assessment. A correlated-adjusted decision forest proposal. Expert Systems with Applications, 42(13), 5737–5753. https://doi.org/10.1016/j.eswa.2015.02.042
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Gunnarsson, B. R., Vanden Broucke, S., Baesens, B., Óskarsdóttir, M., & Lemahieu, W. (2021). Deep learning for credit scoring: Do or don’t? European Journal of Operational Research, 295(1), 292–305. https://doi.org/10.1016/j.ejor.2021.03.006
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Hanson, S. G., Kashyap, A. K., & Stein, J. C. (2011). A macroprudential approach to financial regulation. Journal of Economic Perspectives, 25(1), 3–28. https://doi.org/10.1257/jep.25.1.3
He, H., Zhang, W., & Zhang, S. (2018). A novel ensemble method for credit scoring: Adaption of different imbalance ratios. Expert Systems with Applications, 98, 105–117. https://doi.org/10.1016/j.eswa.2018.01.012
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