Comparison of the applicability of neural networks and cluster classification methods on the example company's financial situation

The paper is focused on comparing the classification ability of the model with self-learning neutral network and methods from cluster analysis. The emphasis is particularly on the comparison of different approaches to a specific application example of the commitment, the classification of then financial situation. The aim is to critically evaluate different approaches at the level of application and deployment options.
The verify the classification capability of the different approaches were used financial data from the database "Credit Info", in particular data describing the financial situation of the two hundred eleven farms of homogeneous and uniform primary field.
Input data were from the methods used, modified and evaluated by appropriate methodology. Found the final solution showed that the used approaches do not show significant differences, and they can say that they are equivalent. Based on this finding can formulate the conclusion that the approach of artificial intelligence (self-learning neural network) is as effective as a partial methods in the field of cluster analysis. In both cases, these approaches can be an invaluable tool in decision making.
When the financial situation is evaluated by the expert, the calculation of liquidity, profitability and other financial indicators are making some simplification. In this respect, neural networks perform better, since these simplifications in them selves are not natively included. They can better assess and somewhat ambiguous cases, including businesses with undefined financial situation, the so-called data in the border region. In this respect, support and representation of the graphical layout of the resulting situation sorted out objects using software implemented neural network model.

Keywords: neural network, self-learning, clustering, classification, financial situation of companies