The need to assess the risks of the trustworthiness of counterparties is increasing every year. The identification of increasing cases of unfair behavior among counterparties only confirms the relevance of this topic. The existing work in the field of information and economic security does not create a reasonable methodology that allows for a comprehensive study and an adequate assessment of a counterparty (for example, a developer company) in the field of software design and development. The purpose of this work is to assess the risks of a counterparty’s trustworthiness in the context of the digital transformation of the economy, which in turn will reduce the risk of offenses and crimes that constitute threats to the security of organizations. This article discusses the main methods used in the construction of a mathematical model for assessing the trustworthiness of a counterparty. The main difficulties in assessing the accuracy and completeness of the model are identified. The use of cross-validation to eliminate difficulties in building a model is described. The developed model, using machine learning methods, gives an accurate result with a small number of compared counterparties, which corresponds to the order of checking a counterparty in a real system. The results of calculations in this model show the possibility of using machine learning methods in assessing the risks of counterparty trustworthiness.
The article considers the binary classification problem of economic security objects on the credit institutions example, for which it is proposed to use machine learning methods. In the study process the expediency of one of the methods of machine learning — the method of k-nearest neighbors — was proved to solve this problem, its efficiency amounted
to 84 %. Key words: machine learning methods, financial statements, performance indicators, credit institutions, binary classification, k-nearest neighbors method.
Mathematical Model for Choosing Counterparty When Assessing Information Security Risks
