The issue of corporate failure in market economy serves as vital and up-to-date theme elaborated in economic literature and market practice [1], [3]. Nowadays, worldwide economic situations prove that all businesses irrespectively of size, turnover or geographical position are endangered of corporate crisis and bankruptcy.  Each entrepreneur needs to monitor their company's financial state incessantly as well as their competitors, suppliers and partners. Only sufficient management and controlling may ensure flexible response to market threats [5].

Polish businesses increasingly become inclined to the launch of Early Warning Systems in order to gather information concerning instant corporate financial condition and forecasts for the future. Despite discriminant analysis being widely explored within corporate failure prediction  research, the number of modern models for bankruptcy prediction, using artificial intelligence powers, steadely increases [3].

The main objective of the paper is a proposal of a new methodology for corporate distress and failure prediction in terms of selected artificial intelligence methods with special attention paid to rough set theory [2]. Financial condition of 240 cases is described by means of 30 financial ratios. The data comes from financial reports for two years in a row and all bankruptcies occurred within 2 to 5 years since the forecast.  

For the research there is Rosetta [4] expert software used, designed to data analysis on the rough set theory basis.

[1] Kuciński, K., Mączyńska, E. (red.), Zagrożenie upadłością. SGH, Warszawa 2005

[2] Pawlak, Z.: Rough Sets - theoretical aspects of reasoning about data. Kluwer

Academic Publishers, Dordrecht 1991.

[3] Prusak B., Nowoczesne metody prognozowania zagrożenia finansowego przedsiębiorstw, Difin, Warszawa 2005

[4] ROSETTA homepage (2004). Available: http://rosetta.lcb.uu.se/general/

[5] Zelek A., Zarządzanie kryzysem w przedsiębiorstwie. Perspektywa strategiczna, Orgmasz, Warszawa 2003.

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