PREDICTION MODEL APPLYING MACHINE LEARNING TO FORECAST THE BANKRUPTCY OF COMPANIES. A SYSTEMATIC REVIEW OF THE LITERATURE.

Prediction models that are aimed at companies allow trends to be identified and generate a much broader picture, making decisions more effective and efficient. In that sense, the research study uses a literature review to identify the state of the art of how predictive models can interact and generate more accurate and reliable results to identify, forecast and eventually reduce the bankruptcy of companies. In addition, the systematic analysis of the contributions was carried out where the main models of supervised learning and other techniques were considered. The authors [25] specify that a systematic literature review is a primary tool for developing an evidence base by identifying, evaluating, and interpreting all available research relevant to a particular research question, thematic area, or phenomenon of interest. Likewise, questions were posed through three (3) stages: identification of parameters, calculation of the ACCP and determination of factors. For the calculation of the weighted precision metric (ACCP), the most significant values of each model were used and the results obtained were analyzed. In this sense, the review must contain the following: method of analysis, theoretical basis, classification of payment yield prediction models and analysis of the topics according to the questions asked.