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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 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.

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This work was supported by the National Funding from the FCT - Fundação para a Ciência e a Tecnologia through the UID/EEA/50008/2019 Project; by the Government of the Russian Federation, Grant 08-08; by Brazilian National Council for Research and Development (CNPq) via Grant No. 309335/2017-5; by Ciência sem Fronteiras of CNPq, Brazil, process number 200450/2015-8; and by the International Scientific Partnership Program ISPP at King Saud University through ISPP #0129.

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Correspondence to Gernmanno Teles.

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Teles, G., Rodrigues, J.J.P.C., Saleem, K. et al. Machine learning and decision support system on credit scoring. Neural Comput & Applic 32, 9809–9826 (2020).

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  • Machine learning
  • Decision trees
  • Fuzzy logic
  • Credit scoring
  • Performance evaluation