Calculating the risk of insolvency, from traditional methods to artificial neural networks. A literature review
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Abstract
In the financial management of any organization, the calculation of the risk of insolvency has become an important parameter, seeking to anticipate the eventuality of having an economic problem and generating insolvency. The objective of this work is to compare the classical methodologies and the artificial neural networks applied to calculate the risk of insolvency, looking for the main characteristics within the applications carried out by different authors over time. In this way, the main variables that can show that the application of the neural network methodology facilitates the calculation of the risk of insolvency are observed. Through the bibliographic review, between the years 1992-2021, with the use of the analytical-synthetic method, it can be seen that the exposed model is considered efficient according to its results, with adjustments that, in most of the exposed cases, exceed 80% efficiency. The results found allowed us to conclude that the basic structure of a neural network is given by three layers: an input layer, an output layer and a hidden layer. However, the number of nodes varies in each of the applications carried out by the different authors, since they represent the variables, in this case the most relevant financial indicators according to the proposed application. Finally, it was possible to show which are the most used financial indicators in the different neural network applications.
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