Machine learning and decision support system on credit scoring

Among the numerous alternatives used in the world of risk balance, it highlights the provision of guarantees in the formalization of credit agreements. The objective of this paper is to compare the achievement of fuzzy sets with that of artificial neural network-based decision trees on credit scorin...

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Bibliographic Details
Published in:Neural computing & applications 2020-07, Vol.32 (14), p.9809-9826
Main Authors: Teles, Gernmanno, Rodrigues, Joel J. P. C., Saleem, Kashif, Kozlov, Sergei, Rabêlo, Ricardo A. L.
Format: Article
Language:English
Subjects:
Artificial Intelligence
Artificial neural networks
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Credit scoring
Data Mining and Knowledge Discovery
Decision analysis
Decision making
Decision support systems
Decision trees
Fuzzy logic
Fuzzy sets
Image Processing and Computer Vision
Machine learning
Modelling
Probability and Statistics in Computer Science
Review Article
Uncertainty
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Summary:Among the numerous alternatives used in the world of risk balance, it highlights the provision of guarantees in the formalization of credit agreements. The objective of this paper is to compare the achievement of fuzzy sets with that of artificial neural network-based decision trees on credit scoring to predict the recovered value using a sample of 1890 borrowers. Comparing with fuzzy logic, the decision analytic approach can more easily present the outcomes of the analysis. On the other hand, fuzzy logic makes some implicit assumptions that may make it even harder for credit-grantors to follow the logical decision-making process. This paper leads an initial study of collateral as a variable in the calculation of the credit scoring. The study concludes that the two models make modelling of uncertainty in the credit scoring process possible. Although more difficult to implement, fuzzy logic is more accurate for modelling the uncertainty. However, the decision tree model is more favourable to the presentation of the problem.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-019-04537-7