DOI: 10.33917/mic-2.127.2026.44-52

The article is devoted to substantiating the importance of the concept of dynamic economic security of an enterprise in the context of digital transformation and high turbulence of the external environment. The limitations of traditional static models of «perimeter» protection, which lose their effectiveness due to information overload and non-linearity of economic processes, are analyzed. The hypothesis is substantiated that the level of security of modern business is determined by the quality and speed of management decisions. A dynamic security architecture based on a symbiosis of artificial intelligence (for 24/7 operational response) and collective intelligence (for strategic thinking) is proposed. The necessity of transition from extrapolation forecasting to scenario modeling and management of a multi-criteria choice is argued. The principles of business resilience are formulated as the ability to maintain a development trajectory through controlled changes and cognitive convergence of heterogeneous data.

References:

[1-21] See in no. 1/2026, p. 50-52.

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25. Robinson J.B. Futures Under Glass: A Recipe for People Who Hate to Predict. Futures. 1990;22(9):820-842. DOI: 10.1016/0016-3287(90)90018-D

26. Gordeev V.V., Abramov V.I. Business Process Forecasting as a Decision-Making Tool within a Proactive Management Approach. Economics and Management. 2025;31(7):893-902. DOI 10.18334/evp.6.4.123876

27. Nikogosyan A.Z., Abramov V.I. Remote Dynamic Control in Transport Logistics: The Role and Benefits of the Internet of Things. Economics and Management: Problems, Solutions. 2025;5(4(157)):183-195. DOI 10.36871/ek.up.p.r.2025.04.05.022.

28. Godet M. Creating Futures: Scenario Planning as a Strategic Management Tool. London: Economica, 2001. 269 p.

29. Ageev A.I., Grabchak E.P., Loginov E.L. A New Model of Managing the Russian Economy in the Context of Hybrid Risks and Threats. Economic Strategies. 2025;5(203): 6-19. DOI: https://doi.org/10.33917/es-5.203.2025.6-19

30. McGrath R.G. The End of Competitive Advantage (translated from English by V.N. Egorova). Moscow: Binom. Lab. of Knowledge, 2013. 248 p.

31. Abramov V.I., Gordeev V.V., Stolyarov A.D. Digital Transformation of Industrial Enterprises into Digital Business Ecosystems: Structural Components and Practical Aspects of Implementation. Fundamental Research. 2024;9:78-85. DOI 10.17513/fr.43680

32. Abramov V.I., Stolyarov A.D. Digital Business Ecosystems: Typology and Design. Modern Economy Success. 2025;6:370-379. DOI 10.18334/vinec.15.4.124210

DOI: 10.33917/mic-1.126.2026.42-52

The article is devoted to substantiating the importance of the concept of dynamic economic security of an enterprise in the context of digital transformation and high turbulence of the external environment. The limitations of traditional static models of «perimeter» protection, which lose their effectiveness due to information overload and non-linearity of economic processes, are analyzed. The hypothesis is substantiated that the level of security of modern business is determined by the quality and speed of management decisions. A dynamic security architecture based on a symbiosis of artificial intelligence (for 24/7 operational response) and collective intelligence (for strategic thinking) is proposed. The necessity of transition from extrapolation forecasting to scenario modeling and management of a multi-criteria choice is argued. The principles of business resilience are formulated as the ability to maintain a development trajectory through controlled changes and cognitive convergence of heterogeneous data.

References:

1. Glazyev S.Yu. State and Prospects of the Sixth Technological Wave in the Russian Economy. Economics of Science. 2024;10(2):11-29. DOI 10.22394/2410-132X-2024-10-2-11-29

2. Schwab K. The Fourth Industrial Revolution. Moscow: Eksmo, 2021. 208 p.

3. Cascio J. Facing the Age of Chaos. Medium. 2020. URL: https://medium.com/@cascio/facing-the-age-of-chaos-b00687b1f51d

4. Gantz J., Reinsel D. The Digital Universe in 2020: Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East. IDC Analyze the Future. 2012. pp. 1-16. URL: https://www.cs.princeton.edu/courses/archive/spring13/cos598C/idc-the-digital-universe-in-2020.pdf

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6. Abramov V.I., Arefyev D.V. Ecosystem Development of Enterprises: Opportunities, Risks, and Features of Assessing Their Digital Maturity. New in Economic Cybernetics. 2025;1:70-84. DOI 10.5281/zenodo.15165454

7. Teece D.J. Business models and dynamic capabilities. Long Range Planning. 2018;51:1. DOI: 10.1016/j.lrp.2017.06.007

8. Knight F.H. Risk, Uncertainty, and Profit. Moscow: DELO Publishing House, 2003. 360 p.

9. Ackoff R.L. Management Misinformation Systems. Management Science. 1967;14(4). URL: http://dx.doi.org/10.1287/mnsc.14.4.B147

10. Kahneman D. Thinking, Fast and Slow. Moscow: AST, 2011. 656 p.

11. McAfee A., Brynjolfsson E. The Second Machine Age. Work, Progress, and Prosperity in the Age of New Technologies. Moscow: AST, 2017. 384 p.

12. Senge P. The Fifth Discipline: The Art and Practice of the Learning Organization. Moscow: MIF. Business, 2018. 496 p.

13. Tett D. The Curse of Efficiency: The Mine Shaft Syndrome. How to Overcome Disunity in Life and Business. Moscow: Olimp-Business, 2017. 336 p.

14. Page S.E. The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies. Princeton: Princeton University Press, 2007. 456 p.

DOI: 10.33917/es-6.204.2025.34-41

The article focuses on three key issues that play a special role in studying and regulating the development of the “big data” digital technology: 1) the content and characteristics of big data as a digital technology both as large volumes of information, which predetermine methodological approaches to studying the trajectories of their development, as well as their legal basis, 2) global trends and challenges of transitioning to a data-driven economy; 3) analysis and assessment of Russian trends in the global landscape.

References:

1. Big Data. Definition. Gartner, available at https://www.gartner.com/en/information-technology/glossary/big-data

2. Gaponenko N.V. Tsifrovye tekhnologii za granitsami khaypa: global’nyy landshaft [Digital Technologies Beyond Hype: The Global Landscape]. Ekonomicheskie strategii, 2022, no 6, pp. 104–110, DOI: https://doi.org/10.33917/es-6.186.2022.104-110

3. Going Digital: Shaping Policies, Improving Lives. Paris: OECD Publishing, 2019.

4. Navin Kumar. Statistika bol’shikh dannykh za 2025 god (dannye o roste i rynke) [Big Data Statistics 2025 (Growth and Market Data)]. DemandSage, 2025, 24 iyunya, available at: https://www.demandsage.com/big-data-statistics/

5. Data and AI Leadership Executive Survey. Wavestone, 2024.

6. Data, BI and Analytics Trend. BARC, 2025.

7. Bol’shie dannye: vygodnoe prilozhenie ili dorogostoyashchiy eksperiment? [Big Data: Profitable Application or Expensive Experiment?]. K2 Cloud i Arenadata, 2025.

8. Gotovnost’ rossiyskogo biznesa k ekonomike dannykh: Monitoring tsifrovoy transformatsii biznesa [Readiness of Russian Business for the Data Economy: Monitoring Digital Transformation of Business]. Vyp. 2. Moscow, NIU VShE, 2023.

DOI: 10.33917/es-6.204.2025.6-15

Despite the diversity of scientific disciplines dealing with management issues, practice very often shows examples of ineffective management. The article identifies and briefly discusses seventeen key barriers to successful management — from complexity issues to implications of the 1990s. These barriers are conditionally divided into three areas related to systemic, instrumental and social aspects of management. At the end of the article the author provides dependence graphs of the barriers to management and an expert assessment of the possibilities to eliminate these barriers.

References:

1. Eshbi U.R. Vvedenie v kibernetiku [Introduction to Cybernetics]. Per. s angl. 4-e izd. Moscow, URSS, 2009, 432 p.

2. Ansoff I. Novaya korporativnaya strategiya [New Corporate Strategy]. Per. s angl. Saint Petersburg, Piter Kom, 1999, 414 p.

3. Ekonomicheskaya strategiya firmy [Economic Strategy of the Firm]. Pod red. A.P. Gradova. 2-e izd. Saint Petersburg, Spets.Lit, 2000, 588 p.

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5. Solomatin A.N. Entropic Approach to Sustainable Development Issues: Thirteenth International Conference “Management of Large-Scale System Development” (MLSD). Moscow (September 2020). IEEE Conference Publications, IEEE Xplore Digital Library, pp. 1–5, available at: https://doi.org/10.1109/ MLSD49919.2020.9247737

6. Viner N. Kibernetika i obshchestvo [Cybernetics and Society]. Per. s angl. Moscow, AST, 2019, 285 p.

7. Solomatin A.N. Postroenie dopustimykh krupnomasshtabnykh sistem kak uslovie ikh upravlyaemosti i samoorganizatsii: Upravlenie razvitiem krupnomasshtabnykh sistem (MLSD’2010): Trudy Chetvertoy mezhdunarodnoy konferentsii [Construction of Admissible Large-Scale Systems as a Condition of Their Controllability and Self-Organization: Management of Large-Scale Systems Development (MLSD’2010): Proceedings of the Fourth International Conference]. Vol. 1. Moscow, IPU RAN, 2010, pp. 18–26.

8. Prangishvili I.V. Sistemnyy podkhod i obshchesistemnye zakonomernosti [Systems Approach and General System Regularities]. Moscow, SINTEG, 2000, 528 p.

9. Rumyantsev V.Yu., Shokhov A.S. Konveyer platformizatsii ekonomiki Rossii [The Conveyor Belt of Platformization of the Russian Economy], pp. 1–12, available at: https://spkurdyumov.ru/uploads/2025/04/konvejer-platformizacii-ekonomiki-rossii.pdf.

10. Oleskin A.V. Detsentralizovannaya setevaya organizatsiya nauchnogo soobshchestva: perspektivy i problemy [Decentralized Network Organization of the Scientific Community: Prospects and Challenges]. Moscow, URSS, LENAND, 2021, 142 p.

11. Zub A.T. Strategicheskiy menedzhment: teoriya i praktika [Strategic Management: Theory and Practice]. 4-e izd., dop. Moscow, Yurayt, 2014, 375 p.

12. Federal’nyy zakon ot 28 iyunya 2014 g. N 172-FZ “O strategicheskom planirovanii v Rossiyskoy Federatsii” [Federal Law of June 28, 2014 No. 172-FZ “On Strategic Planning in the Russian Federation”]. Konsul’tantPlyus, available at: https://www.consultant.ru/document/cons_doc_LAW_164841

DOI: 10.33917/mic-6.125.2025.5-11

The article dwells on the use of digital technologies for operating in the information sphere the activity of the subject of information relations based on the elements of artificial intelligence. The authors present an information technology for adapting individually oriented information messages generated by artificial intelligence services (semantic blocks), taking into account the personality characteristics, identified during observation, and formation of adapted messages that interpret events to set the vector of individualized logic for choosing a actions chain of the subject of information relations in the context of a complex epidemiological situation. It is proposed to provide digital support of information services for a configurable model of targeted development and targeted delivery of adapted packages of information messages to a consumer – a process with an expanded NLP component, in relation to the information picture of reality, a constantly reproducible action within the framework of information activities.

References:

1. Ageev A.I., Grabchak E.P., Loginov E.L. Using Supercomputer Technologies to Manage the Operations of Very Large Organizational Systems in the Implementation of Complex Special Projects (Operations). Microeconomics. 2024;1:5–10.

2. Ageev A.I., Grigoriev V.V., Loginov E.L. Quantum Simulators as a Tool for Observability of a Digital Supersystem with a Significant Component of Unpredictable Behavior of Its Elements. Microeconomics. 2024;5:5–13.

3. Ageev A.I., Loginov E.L., Ziyadinov A.S., Ziyadinov D.S. Key Trends in the Development of Artificial Intelligence in the Global Economy. Microeconomics. 2025;4:5–18.

4. Ageev A.I., Loginov E.L. Modeling the Processes of Ensuring the Resilience of State Organizational Systems to Attempts to Seize Power by an Identified and Unidentified Adversary. Microeconomics. 2025;3:5–14.

5. Ageev A.I., Loginov E.L. Selection and Training of Personnel for Structures with Critical Cognitive and Psychological Loads in Complex Special Situations (Operations). Economic Strategies. 2024;26(2 (194)):78–87.

6. Loginov E.L. Digital Technologies of Information Warfare: Neural Network Imperatives of Information Counteraction to Attempts to Intercept Control in the Social and Information Environment. Moscow: Rusains, 2024. 234 p.

DOI: 10.33917/es-4.202.2025.70-77

The article dwells on the essence of artificial intelligence as a mathematical model used to automate human cognitive activity. The author identifies practical tasks that can be solved with the help of the above technology, such as automation of routine business processes, marketing offers personalization, fraud detection, management decision-making support and others. Industries where artificial intelligence is actively used are studied, including healthcare, finance, retail, manufacturing, energy and telecommunications. Special attention is paid to specific examples of applying ar tificial intelligence in auctions, in particular in real estate, energy, online advertising and distribution of computing resources. The author reveals auction models, their essence and areas of application, such as online auctions, programmatic adver tising, spot electricity markets, financial markets and government procurement.

The article analyzes the prospects for introducing artificial intelligence-based tools into auctions in the context of fur ther digitalization of the Russian economy and potential benefits of their application, such as increased profits, optimized use of resources, reduced labour costs and other operating costs.

References:

1. Smith C. Introduction. The History of Artificial Intelligence, available at: https://courses.cs.washington.edu/ courses/csep590/06au/projects/history-ai.pdf

2. Timofeeva A.Yu., Koroleva E.N. Iskusstvennyy intellekt v upravlenii ekonomikoy [Artificial Intelligence in Economic Management]. Nauka XXI veka: aktual’nye napravleniya razvitiya, 2021, no 1-2, pp. 184–188.

3. Reutov R.V. Vliyanie innovatsiy tsifrovoy ekonomiki na mekhanizm neytralizatsii defektov bankovskogo rynka: Ekonomiko-pravovye problem obespecheniya ekonomicheskoy bezopasnosti [The Impact of Digital Economy Innovations on the Mechanism for Neutralizing Defects in the Banking Market:

Economic and Legal Problems of Ensuring Economic Security]. 2021, pp. 99–102.

4. Talapina E.V. Ispol’zovanie iskusstvennogo intellekta v gosudarstvennom upravlenii [Use of Artificial Intelligence in Public Administration]. Informatsionnoe obshchestvo, 2021, no 3, pp. 16–22.

5. Nikitaeva A.Y., Salem A.B.M. Institutional framework for the development of artificial intelligence in the industry. Journal of Institutional Studies, 2022, vol. 14, no 1, pp. 108–126.

6. Zhang C., Lu Y. Study on artificial intelligence: The state of the art and future prospects. Journal of Industrial Information Integration, 2021, available at:

https://www.sciencedirect.com/science/article/abs/pii/S2452414X21000248

7. Fiala P., Flusserova L. Modelirovanie i reshenie dvustoronnikh auktsionov [Modeling and Solving Two-sided Auctions]. Khronoekonomika, 2022, no 4(38), pp. 4–8.

8. Kapusto A.V., Banshchikov D.O. Auktsiony i ikh modelirovanie: Tendentsii ekonomicheskogo razvitiya v XXI veke [Auctions and Their Modeling: Trends of Economic Development in the 21st Century]. 2021, pp. 428–431.

DOI: 10.33917/mic-4.123.2025.5-18

The article considers key factors determining the development of artificial intelligence in the global economy, including the likelihood of artificial intelligence dominance over humanity in the future, key aspects of artificial intelligence development with an emphasis on neural networks, possible prospects for the development of artificial intelligence until 2070, prospects for possible existential risks of artificial intelligence, as well as the risks of a cyber conflict. To study the problem, a dialogue with artificial intelligence was used – with the ChatGPT neural network from OpenAi. It is concluded that the future of artificial intelligence development in the global economy remains uncertain and depends on many factors, including technological advances and social changes. An unsolved problem is the establishment of a harmonious regulatory mechanism for relations between humans and artificial intelligence, taking into account its intensive development and implementation in various control systems of the global economy. Important tasks for humanity are to create ethical standards for the use of artificial intelligence and develop effective strategies for managing these technologies in order to minimize risks and maximize benefits from their implementation.

References: 

1. Ageev A.I., Grigoriev V.V., Loginov E.L. Quantum simulators as a tool for observability of a digital supersystem with a significant component of unpredictable behavior of its elements. Microeconomics. 2024;5:5-13.

2. Ageev A.I., Ivanova O.D., Loshchinin A.A. Technology of information space of data and improvement of public administration. Economic strategies. 2023;25(6 (192)):62-67.

3. Ageev A.I., Zolotareva O.A., Zolotarev V.A. Russia in the global world of artificial intelligence: assessment by world rankings. Economic strategies. 2022;24(2 (182)):20-31.

4. Loginov E.L. Interception of control of complex organizational systems in the context of blurring boundaries between physical, cognitive and digital spaces of activity and control environments. Economy: Theory and Practice, 2024;1(73):3-10.

5. Bahtizin A.R., Soldatov A.I. Using artificial intelligence to optimize intermodal networking of organizational agents within the digital economy. Journal of Physics: Conference Series, 2019, 12042.

6. Grabchak E.P. Ensuring observability and controllability of complex technical systems in difficult and irregular situations when commands with a large distortion component are received. Lecture Notes in Electrical Engineering, 2021, Vol. 729 LNEE, pp. 624-631.

7. Grigoriev V.V., Balandin V.S., Shkuta A.A., Boyko P.A. The use of electronic semantization of the cognitive activity manifestations with the aim of detection of intentions of the group of people leading to the destabilization of the digital super system. IOP Conference Series: Materials Science and Engineering. 1. «1st International Conference on Innovative Informational and Engineering Technologies», 2020, IIET 2020, 012002.

DOI: 10.33917/es-3.201.2025.98-101

The article examines the concept of an enterprise digital twin (EDT) as an organizational and managerial mechanism based on business capabilities. Business capability is a holistic system for carrying out a specific type of activity, whose behaviour is strategically predetermined in a unique and unambiguous way within an ecosystem and its implementation is variable, including processes, organization and all types of resources. The author proposes a methodology for forming a business capability map using a federalistic approach to integrating data and artificial intelligence (AI) technologies, which ensures high adaptability and ecosystems transparency. Based on theoretical analysis and testing in real companies, practical significance of EDT for vertical and horizontal integration of corporations and, accordingly, management of multi-industry ecosystems is shown.

References:

1. Grieves M. Digital Twin: Manufacturing Excellence through Virtual Factory Replication. 2014.

2. Tao F., Zhang H., Liu A., Nee A.Y.C. Digital Twin in Industry: State-of-the-Art. 2019.

3. Ulrich D., Smallwood N. Capitalizing on Capabilities. Harvard Business Review, 2004.

4. Ross J.W., Weill P., Robertson D. Enterprise Architecture as Strategy: Creating a Foundation for Business Execution. 2006.

5. Kairouz P., McMahan H.B., Avent B., Bellet A., Bennis M., Bhagoji A.N., et al. Advances and Open Problems in Federated Learning. 2021.

DOI: 10.33917/es-3.201.2025.66-76

In the proposed material, the author presents a very favourable forecast for the appraisal industry development, based on wide opportunities opened up by the use of artificial intelligence and other digital technologies. For Russian conditions, the priority is to improve the quality of appraiser services. For these purposes, a new version of the Law on Appraisal Activities was adopted in the first reading. Overall, the past year was successful for the appraisal industry. The article briefly analyzes the state of the valuation market segments from the future prospects point of view. At the same time, the author points out that such a phenomenon as global turbulence can cause radical changes in the economy. Therefore, the main strategic task of companies is to achieve the maximum degree of sustainability in understanding resilience.

References:

1. Clemons D. The Future of Appraisal Management. Gosourceval, January 14, 2025, available at: https://gosourceval.com/appraisal-management-trends/

2. The future of the appraisal industry: trends and predictions. Appraisal Partners, December 29, 2024, available at: https://www.appraisalpartners.com/thefuture-of-the-appraisal-industry-trends-and-predictions/

3. Novye metody uluchsheniya kachestva otsenochnykh uslug: zakonodatel’nyy aspekt [New methods of improving the quality of appraisal services: legislative aspect]. NIK Otsenka, available at: https://nikocenka.ru/akcii_i_novosti/novye_metody_uluchsheniya_kachestva_ocenochnyh_uslug-_zakonodatelnyj_aspekt

4. Otsenshchiki ne mogut ostavat’sya v storone ot razvitiya sovremennykh tekhnologiy [Appraisers cannot remain aloof from the modern technologies development]. Kommersant””, 2024, 29 avgusta, available at: https://www.kommersant.ru/doc/6906381

5. Kakie izmeneniya proizoshli na rynke otsenki za god [What changes have occurred in the appraisal market over the year?]. Soyuz, available at: https:// srosoyz.ru/01.01.02.01/1552

6. Zarplaty rastut, lyudey net. Kak i pochemu Rossiya okazalas’ v lovushke kadrovogo goloda [Salaries are growing, but there are no people. How and why Russia found itself trapped in a personnel shortage]. Sekret firmy, 2024, 4 fevralya, available at: https://secretmag.ru/survival/rossiya-okazalas-v-lovushkekadrovogo-goloda.htm/

7. Kadrovyy golod v Rossii udvoitsya k 2029 godu [Russia’s labour shortage to double by 2029]. Finansy, 2025, 14 aprelya, available at: https://finance.mail. ru/2025-04-14/kadrovyy-golod-v-rossii-udvoitsya-k-2029-godu-65716697/

8. Rost IPO na fone kadrovogo goloda [IPO growth amid personnel shortage]. Kommersant””, 2024, 29 avgusta, available at: https://www.kommersant.ru/ doc/6892955

9. Rost rynka otsenki: kak izmeneniya v zakonodatel’stve, kadrovyy golod i novye tekhnologii sposobstvovali dinamike v otrasli v 2024 godu [Appraisal market growth: how legislative changes, labour shortages and new technologies have contributed to the industry dynamics in 2024]. Tsentr Analitik, 2024, 15 dekabrya, available at: https://analitik77.ru/rost-rynka-ocenki-kak-izmenenija-v-zakonodatelstve-i-kadrovyj-golod-i-novye-tehnologii-sposobstvovali-dinamikev-otrasli-v-2024-godu/

DOI: 10.33917/es-2.200.2025.104-109

The article examines fundamental causes of fraud in the financial sector based on global practice and Russian experience in fighting cybercrimes. Growing indicators prove ineffectiveness of all measures taken, including restrictions imposed by states. The issue can be resolved only by turning to the alpha and omega of economic science — to a man or more precisely to a human capital, which the author in his previous studies, based on works of the Soviet cybernetic school, have defined not only as knowledge, talents and abilities, but also as directed thinking algorithms. These algorithms now have become the main object of appropriation. Banks, IT-corporations, telecom operators and fraudsters compete for them in order to control a person and induce him to transfer his financial resources or his data for their appropriation by fraudsters.

References: 

1. Rezul’taty ezhegodnogo vserossiyskogo sotsiologicheskogo monitoringa “Finansovaya gramotnost’ rossiyan — 2024” [Results of the Annual All-Russian Sociological Monitoring “Financial Literacy of Russians — 2024”]. Analiticheskiy tsentr NAFI, Moscow, 2024, available at: https://nafi.ru/upload/iblock/9bd/9bd4081

a5c55f503ab6610296892ed2a.pdf?utm_referrer=https%3A%2F%2Fdzen.ru%2Fmedia%2Fid%2F5d41a1ee8da1ce00afb2d1e5%2F66c5dd22a7321d7404b4c06e

2. Alekseevskikh A. “Rodstvennik popal v bedu”. Novye skhemy moshennichestva i layfkhaki dlya rossiyan [“A Relative Got into Trouble.” New Fraud Schemes and Life Hacks for Russians]. Gazeta.ru, 2024, 6 sentyabrya, available at: https://www.gazeta.ru/amp/business/2024/09/06/19704493.shtml

3. Dornadula V.N., Geetha S. Credit Card Fraud Detection Using Machine Learning Algorithms. Procedia Computer Science, 2019, no 165, pp. 631–641, DOI:

https://doi.org/10.1016/j.procs.2020.01.057

4. Thennakoon A., Bhagyani C., Premadasa S., Mihiranga S., Kuruwitaarachchi N. Real-Time Credit Card Fraud Detection Using Machine Learning. 2019 9^th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, 2019, 10–11 January, pp. 488–493, available at: https://ieeexplore.ieee.org/document/8776942; https://doi.org/10.1109/CONFLUENCE.2019.8776942

5. Bouch A. 3-D Secure: A critical review of 3-D Secure and its effectiveness in preventing card not present fraud. University of London, Londra, 2011, DOI:

https://doi.org/10.14201/ADCAIJ2016535561

6. Buell S. What is Securities Fraud 61 Duke L.J. 511. 2011–2012, available at: https://scholar.google.com/citations?view_op=view_citation&hl=en&user=OTE8FpMAAAAJ&citation_for_view=OTE8FpMAAAAJ:UebtZRa9Y70C

7. Pokida A.N., Zybunovskaya N.V. Razlichiya v povedencheskikh praktikakh po sokhraneniyu i ukrepleniyu zdorov’ya sredi rabotnikov umstvennogo i fizicheskogo truda [Differences in Behavioral Practices for Maintaining and Improving Health Among Mental and Physical Workers]. Zdorov’e naseleniya i sreda obitaniya — ZNiSO, 2022. № 9. S. 18–28. DOI: https://doi.org/10.35627/2219-5238/2022-30-9-18-28