@INPROCEEDINGS { Rsds3002,
author = {Perez, Concepcion and Menasalvas Ruiz, Ernestina and Marban, Oscar and Fernandez, Covadonga and Saz, Raquel del and Eibe, Santiago}, 
title = {Financial Risk Prediction Using Rough Sets Tools: A Case Study}, 
booktitle = {Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, Part II}, 
series =  = {Lecture Notes in Artificial Intelligence}, 
volume = 3642, 
pages = {495--502}, 
publisher = {Springer-Verlag}, 
address = {Heidelberg - Berlin, Germany}, 
month = {August}, 
year = 2005, 
editor = {Slezak, Dominik and Yao, Jing Tao and Peters, James F. and Ziarko, Wojciech and Hu, Xiaohua}, 
issn = {0302-9743}, 
isbn = {3-540-28660-8}, 
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.}, 
keywords = {financial risk, prediction rules, classification rules, rough sets, machine learning, data mining, }, 
}