Title: A multicriteria forecast of the default probability in credit risk assessment

Authors: Ayrton Benedito Gaia Do Couto; Luiz Flavio Autran Monteiro Gomes

Addresses: BNDES, Av. República do Chile, 100, Rio de Janeiro-RJ, 20031-917, Brazil ' Ibmec/RJ, Av. Presidente Wilson, 118, Rio de Janeiro-RJ, 20030-020, Brazil

Abstract: This study analyses, through a multicriteria approach producing if-then rules, the possibility of forecasting the default probability (PD) for low-default portfolios in credit risk assessment. This approach relies on the simultaneous and complementary application of fuzzy and rough sets theories. The calculation of this probability is normally subject to judgments of experts, which imply subjectivity, imprecision and uncertainty of the results. In the proposed approach, the inferred if-then rules for a decision table generated by a simulation of 50 companies allowed the production of a knowledge base for the forecasting of classes of default probabilities. The 'RoughSets^®' package, in 'R', was used as support for the analysis and forecasting system.

Keywords: default probability; multicriteria analysis; rough sets theory; RST; credit risk assessment; forecasting system.

DOI: 10.1504/IJBSR.2020.108270

International Journal of Business and Systems Research, 2020 Vol.14 No.3, pp.281 - 297

Received: 10 Nov 2018
Accepted: 16 Nov 2018
Published online: 08 Jul 2020 *

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