Financial risk prediction using rough sets tools: a case study

  • Authors:

  • Santiago Eibe;Raquel Del Saz;Covadonga Fernández;Óscar Marbán;Ernestina Menasalvas;Concepción Pérez

  • Affiliations:

  • DLSIS, Facultad de Informática, Universidad Politécnica de Madrid, Madrid, Spain;DLSIS, Facultad de Informática, Universidad Politécnica de Madrid, Madrid, Spain;DLSIS, Facultad de Informática, Universidad Politécnica de Madrid, Madrid, Spain;DLSIS, Facultad de Informática, Universidad Politécnica de Madrid, Madrid, Spain;DLSIS, Facultad de Informática, Universidad Politécnica de Madrid, Madrid, Spain;Departamento de Informática Escuela Técnica Superior de Ingenieros, Industriales e Informáticos, Universidad de Oviedo, Spain

  • Venue:
  • RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

Since the Rough Sets Theory was first formulated in 1982, different models based on it have been proposed to be applied in economic and financial prediction. Our aim is to show the development of a method of estimation of the financial risk when a credit is granted to a firm, having into account its countable status. This is a classical example of inference of classification/prediction rules, that is the kind of problem in which the adequacy of Rough Sets methods has been proved. Coming from data concerning industrial companies that were given to us by the Banks that granted the credits, we have obtained a set of classification rules to be used to predict the result of future credit operations.