References
- Bielecki, T.R., Rutkowski, M., 2013. Credit Risk: Modeling, Valuation and Hedging. Berlin, Heidelberg: Springer-Verlag.
- Bishop, C.M., 2006. Pattern recognition and Machine Learning. Springer.
- Bluhm, C., Overbeck, L., and Wagner, C., 2016. Introduction to Credit Risk Modeling. Florida: CRC Press.
- Chicco, D., Tötsch, N., and Jurman, G., 2021. The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation. BioData Mining, Vol.14(13), pp.1-22.
- Cramer, J.S., 2002. The Origins of Logistic Regression. Tinbergen Institute Working Paper, No.119/4.
- Dahooie, J.H., Hajiagha, S.H., Farazmehr, S., Zavadskas, E.K., and Antucheviciene, J., 2021. A novel dynamic credit risk evaluation method using data envelopment analysis with common weights and combination of multi-attribute decision-making methods. Computers & Operations Research, No. 129, 105223.
- Djeundje, V.B., Crook, J., Calabrese, R., and Hamid, M., 2021. Enhancing credit scoring with alternative data. Expert Systems with Applications, No. 163, 113766.
- Fawcett, T., 2006. An introduction to ROC analysis. Pattern Recognition Letters, 27, pp.861-874.
- Figlewski, S., Frydman, H., and Liang, W., 2012. Modeling the effect of macroeconomic factors on corporate default and credit rating transitions. International Review of Economics & Finance, No. 21(I), pp.87-105.
- Forster, J.J., Buzzacchi, M., Sudjianto, A., and Nagao, R., 2016. Modelling credit grade migration in large portfolios using cumulative t-link transition models. European Journal of Operational Research, Vol. 254(3), pp.977-984.
- Greiff, W.R., 1999. Maximum Entropy, Weight of Evidence and Information Retrieval. PhD Thesis, University of Massachusetts Amherst.
- Gujarati, D.N., Porter, D.C., 2009. Basic Econometrics. McGraw-Hill/Irwin.
- Ha, D.T., 2019. Early risk warning for credit provision to customers and related individuals is a critical concern for commercial banks in Vietnam. Banking Science & Education Journal.
- Hand, D., Christen, P., 2017. A note on using the F-measure for evaluating record linkage algorithms. Statistics and Computing, Vol. 28(3), pp.539-547.
- Ionela, S.A., 2014. Early Warning Systems - Anticipationʼs Factors of Banking Crises. Procedia Economics and Finance, No.10, pp.158-166.
- Kim, Y., Sohn, S.Y., 2008. Random effects model for credit rating transitions. European Journal of Operational Research, Vol. 184(2), pp.561-573.
- Kwon, Y., Park, S.Y., 2023. Modeling an early warning system for household debt risk in Korea: A simple deep learning approach. Journal of Asian Economics, No.84, 101574.
- Markov, A., Seleznyova, Z., and Lapshin, V., 2022. Credit scoring methods: Latest trends and points to consider. The Journal of Finance and Data Science, No.8, pp.180-201.
- Murphy, K.P., 2012. Machine Learning: A Probabilistic Perspective. Massachusetts Institute of Technology.
- Powers, D.M., 2011. Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation. Journal of Machine Learning Technologies, Vol. 2(1), pp.37-63.
- Reinhart, C., Goldstein, M., and Kaminsky, G., 2010. Methodology for an Early Warning: The Signals Approach. University of Maryland, College Park, Department of Economics.
- Rokach, L., Maimon, O., 2008. Data Mining with Decision Trees: Theory and Applications. World Scientific Publishing.
- Shalev-Shwartz, S., Ben-David, S., 2014. 18 - Decision Trees. In Understanding Machine Learning. Cambridge University Press.
- Siddiqi, N., 2006. Credit Risk Scorecards, Developing and Implementing Intelligent Credit Scoring. Hoboken, NJ: John Wiley & Sons, Inc.
- Slapnik, U., Lončarski, I., 2021. On the information content of sovereign credit rating reports: Improving the predictability of rating transitions. Journal of International Financial Markets, Institutions and Money, No. 73, 101344.
- State Bank of Vietnam., 2021. Circular No. 11/2021/TT-NHNN: Prescribing classification of assets, amounts and methods of setting up risk provisions and use of provisions for control and management of risks arising from operations of credit institutions and foreign bank branches.
- Wang, L., Zhang, W., 2023. A qualitatively analyzable two-stage ensemble model based on machine learning for credit risk early warning: Evidence from Chinese manufacturing companies. Information Processing & Management, No. 60, 103267.