Abstract: The subject of the research is the development of a software package for intelligent forecasting of the execution of government contracts using machine learning methods and analysis of unstructured information.
The object of the study is the process of control and decision-making in the field of public procurement, including the selection of contractors, the execution of contracts and the assessment of the timing and cost of their implementation.
Special attention in the study is paid to the development and application of interpreted machine learning methods to solve the problems of assessing the risks of choosing an unscrupulous contractor, the risks of non-fulfillment of the contract on time and forecasting the likely timing and cost of contract implementation.
The authors consider in detail such aspects as a unique set of data that was collected from various information systems. They have also developed automated data collection and update systems that can be installed on customers' servers. The methods of machine learning, analysis of unstructured information and interpreted methods were used in the work. Interpreted machine learning models were built to assess the risk of choosing an unscrupulous contractor, assess the risk of non-fulfillment of the contract on time, as well as assess the likely timing and cost of contract implementation. A unique set of data was collected in the work, including more than 83 thousand data on more than 190 features from various systems, such as the Unified Information System (UIS) Public Procurement Register, the Register of Unscrupulous Suppliers (RNP) EIS and SPARK Information System. Automated data collection and updating systems have been developed that can be deployed on customer servers.
In the course of the study, software packages were developed for intelligent forecasting of the execution of government contracts, which provide an opportunity to conduct a more accurate risk analysis using unstructured information analysis methods, machine learning models and interpreted methods. This makes it possible to increase the effectiveness of monitoring the implementation of government contracts and reduce the likelihood of corruption and violations. The study demonstrates the importance and applicability of machine learning methods and models in the field of public contracts and provides new opportunities for improving control and decision-making processes in the field of public procurement.

Abstract: The research is aimed at solving the problem of the execution of government contracts, the importance of using unstructured information and possible methods of analysis to improve the control and management of this process. The execution of government contracts has a direct impact on the security of the country, its interests, economy and political stability. Proper execution of these contracts contributes to the protection of national interests and ensures the security of the country in every sense. The object of research is algorithms used to extract information from texts. These algorithms include machine learning technologies and natural language processing. They are able to automatically find and structure various entities and data from government contracts. The scientific novelty of this study is the accounting of unstructured information in the analysis of the execution of government contracts. The authors drew attention to the problem-oriented texts in the contract documentation and suggested analyzing them with numerical indicators to assess the current state of the contract. Thus, a contribution was made to the development of methods for analyzing government contracts by taking into account unstructured information. The proposed methods for analyzing problem-oriented texts using machine learning. This approach can significantly improve the evaluation and management of the execution of government contracts. The results of the interpretation of problem-oriented texts can be used to optimize the risk assessment model for the execution of a government contract, as well as to increase its accuracy and efficiency.