A neural network model for assessing the reliability of counterparties within a metallurgical enterprise's procurement system

The article presents a neural network model for evaluating the reliability of counterparties within a metallurgical enterprise's procurement system. Aim. The study aims to develop a neural network model to assess the reliability of counterparties within a metallurgical enterprise's procurement management system. Results. The study involved collecting, analyzing and processing relevant data; conducting a comprehensive analysis of parameters characterizing suppliers, including financial, legal, operational, organizational and reputational indicators; constructing the model architecture; training and testing it on a sample; and comparing it with traditional assessment approaches. Testing the model showed that it has high forecasting accuracy and can be used in conditions of information uncertainty. This paper presents the prospects for integrating the model into the corporate information systems of metallurgical enterprises.