Abstract
Decisions to grant credit to customer is the most crucial part in credit business. In the recent years, advances in information technology have lessened the costs of acquiring, managing and analyzing data in an effort to build more efficient and powerful models for credit rating in credit risk management.
By using common methods as statistical and learning methods, credit scoring models are built to evaluate credit risk for real Vietnamese corporate data. We found that the logistic model give promising results compare with multivariate discrimination analysis, probit and neural network.
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- 1.
Tri Thuc Tre: http://ttvn.vn/kinh-doanh.htm.
- 2.
IMF: 2016 Article IV Consultation—Press Release; Staff Report; and Statement by the Executive Director for Vietnam.
- 3.
In simplest case, we consider that a firm is either default or non-default. In the general case, we can divide firms into groups based on discriminant score.
- 4.
We also use these model to classify customers to different groups.
- 5.
Gini, C. (1912).
- 6.
Basel Committee on Banking Supervision - Revisions to the Standardized Approach for credit risk.
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Acknowledgment
We are immensely grateful to 3 anonymous reviewers for their comments on an earlier version of the manuscript, although any errors are our own and should not tarnish the reputations of these esteemed persons.
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Nguyen, H., Nguyen, T. (2016). Statistical and ANN Approaches in Credit Rating for Vietnamese Corporate: A Comparative Empirical Study. In: Huynh, VN., Inuiguchi, M., Le, B., Le, B., Denoeux, T. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2016. Lecture Notes in Computer Science(), vol 9978. Springer, Cham. https://doi.org/10.1007/978-3-319-49046-5_60
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