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Enfoque UTE

On-line version ISSN 1390-6542Print version ISSN 1390-9363


VALDES, Marcia M. Lastre; ALEAGA, Arlys M. Lastre  and  VIDAL, Gelmar García. Artificial Neural Networks in the prediction of insolvency. A paradigm shift to traditional business practices recipes. Enfoque UTE [online]. 2014, vol.5, n.2, pp.38-58. ISSN 1390-6542.

In this paper a review and analysis of the major theories and models that address the prediction of corporate bankruptcy and insolvency is made. Neural networks are a tool of most recent appearance, although in recent years have received considerable attention from the academic and professional world, and have started to be implemented in different models testing organizations insolvency based on neural computation. The purpose of this paper is to yield evidence of the usefulness of Artificial Neural Networks in the problem of bankruptcy prediction insolence or so compare its predictive ability with the methods commonly used in that context. The findings suggest that high predictive capabilities can be achieved using artificial neural networks, with qualitative and quantitative variables.

Keywords : Neural Networks; Petri nets; Insolvency; Bankruptcy..

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