Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/258218 
Year of Publication: 
2021
Citation: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 9 [Issue:] 7 [Article No.:] 133 [Publisher:] MDPI [Place:] Basel [Year:] 2021 [Pages:] 1-13
Publisher: 
MDPI, Basel
Abstract: 
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.
Subjects: 
model
trustworthiness
risks
information and analytical systems
machine learning
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Appears in Collections:

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.