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RESEARCH CENTER FOR INDUSTRIAL DEVELOPMENT PROBLEMS of NAS of Ukraine (KHARKIV, UKRAINE)

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Liburkina L. M.

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Synthesis of Artificial Intelligence and Fuzzy Logic Technologies in a Risk Assessment Model For Managerial Decision-Making
Kuzmenko O. O., Vitkin L. M.

Kuzmenko, Oleksandr O., and Vitkin, Leonid M. (2026) “Synthesis of Artificial Intelligence and Fuzzy Logic Technologies in a Risk Assessment Model For Managerial Decision-Making.” Business Inform 8:487–496.
https://doi.org/10.32983/2222-4459-2026-8-487-496

Section: Management and Marketing

Article is written in Ukrainian
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UDC 330.46:658

Abstract:
The rapid development of digital technologies and the increasing complexity of management processes necessitate the improvement of risk assessment approaches in decision support systems. Although numerous studies have explored the application of artificial intelligence and fuzzy logic in risk management, most existing approaches rely on either one of these technologies or provide only limited integration between them. Consequently, insufficient attention has been paid to the development of adaptive risk assessment models capable of simultaneously processing historical data, incorporating expert knowledge, and accounting for the specific characteristics of testing laboratories. Addressing this research gap constitutes the primary focus of the present study. The aim of the study is to develop and scientifically substantiate an integrated risk assessment model for managerial decision support in testing laboratories based on the synthesis of artificial intelligence and fuzzy logic technologies, as well as to demonstrate its practical applicability through scenario-based modeling. The research employs methods of system analysis, comparative analysis, conceptual modeling, and scenario-based modeling, together with the principles of fuzzy set theory and contemporary approaches to artificial intelligence, as well as methods for summarizing and organizing scientific sources. The information base includes recent scientific publications on artificial intelligence, fuzzy logic, and risk management, relevant international standards, and generalized data characterizing the operation of testing laboratories. The study results include a comprehensive analysis of existing hybrid artificial intelligence–fuzzy logic models for risk assessment, identification of their strengths and limitations, and the development of an integrated model consisting of data analysis, fuzzy inference, integrated risk assessment, and managerial recommendation modules. To verify the feasibility of the proposed approach, scenario-based modeling of a typical managerial situation related to the operation of critical testing equipment was conducted. The obtained results demonstrate the possibility of combining historical operational data with expert evaluations to improve the quality and consistency of managerial decisions. The findings indicate that the synthesis of artificial intelligence and fuzzy logic technologies enhances the adaptability of the risk assessment process, improves the interpretability of assessment results, reduces the influence of subjective expert judgments, and increases the reliability of managerial decision-making under uncertainty. The scientific novelty of the research lies in the development of an integrated risk assessment model that, unlike existing approaches, combines intelligent analysis of historical data with fuzzy inference mechanisms for expert knowledge formalization and generation of interpretable managerial recommendations within a unified decision support framework. The practical significance of the proposed model consists in its potential application in testing laboratories, conformity assessment bodies, and other organizations requiring risk-oriented managerial decision-making. The model can be used to improve risk assessment procedures, support preventive and corrective actions, optimize quality management systems, and enhance the efficiency of operational risk management.

Keywords: artificial intelligence; fuzzy logic; risk assessment; managerial decision support; testing laboratory; scenario-based modeling; risk management; quality management systems.

Fig.: 2. Tabl.: 1. Bibl.: 15.

Kuzmenko Oleksandr O. – Postgraduate Student, Department of Management Technologies, University of Economics and Law «KROK» (30-32 Tabirna Str., Kyiv, 03113, Ukraine)
Email: [email protected]
Vitkin Leonid M. – Doctor of Sciences (Engineering), Professor, Department of Management Technologies, University of Economics and Law «KROK» (30-32 Tabirna Str., Kyiv, 03113, Ukraine)
Email: [email protected]

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