The Bulletin of the Institute of Economics of the Russian Academy of Sciences № 5/2026. Finance.

JATS XML

Valeria V. Litvin

Dr. Sci. (Econ.), Associate Professor, Director of the Institute of Financial Research, Faculty of Finance, Associate Professor of the Department of Banking and Monetary Regulation, Financial University under the Government of the Russian Federation, Moscow, Russia

ORCID: 0000-0002-1677-8138

Dmitry I. Korovin

Dr. Sci. (Econ.), Cand. Sci. (Phys.–Math.), Associate Professor, Professor of the Department of Modeling and System Analysis, Faculty of Information Technology and Big Data Analysis, Financial University under the Government of the Russian Federation, Moscow, Russia

ORCID: 0000-0001-9941-0322

Konstantin V. Krinichansky

Dr. Sci. (Econ.), Chief Researcher, Institute of Financial Research, Professor of the Department of Financial Markets and Financial Engineering, Faculty of Finance, Financial University under the Government of the Russian Federation, Moscow, Russia

ORCID: 0000-0002-1225-7263

Timofei V. Maniakhin

Cand. Sci. (Econ.), Associate Professor of the Department of Financial Markets and Financial Engineering, Leading Researcher, Institute of Financial Research of the Finance Faculty, Financial University under the Government of the RussianFederation, Moscow, Russia

ORCID: 0009-0005-7315-286X

EXPERT ANALYSIS OF THE RISKS OF CREATING AND INCREASING “LONG MONEY” IN THE RUSSIAN ECONOMY

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The formation of long-term savings and their transformation into productive investments are the central pillars of sustainable economic growth and economic security. In a number of developing economies, including Russia’s, this process is structurally limited by a complex system of interconnected risks, including geopolitical changes, macroeconomic instability, institutional fragility, infrastructure deficiencies, behavioural factors, and others. To conduct an in-depth analysis of these risks, this study employs a multi-level analytical model that combines the ideas of macroeconomics, financial intermediation theory, institutional economics, financial economics, and macrofinancial stability. This conceptual framework made it possible to systematize and characterize the risks that mediate the process of «long-term money» reproduction in the Russian economy. Using an expert survey, the study assesses the risks and potential economic damage caused by risky events in 2026-2028. Dynamic risk maps are compiled and analysed, and a set of recommendations is developed to minimize the risks associated with the creation and accumulation of long-term money in the Russian economy.

Keywords: long-money, long-term savings, long-term investments, economic loss, expert assessment, risk map, risk mitigation mechanisms.

JEL: D14, D81, D91, E21, G40

EDN: TXQCJM

DOI: https://doi.org/10.52180/2073-6487_2026_5_109_134

Financial support: The article was prepared based on the results of studies carried out at the expense of budgetary funds on a state assignment Financial University under the Government of the Russian Federation.

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Manuscript submission date: 11.06.2026

Manuscript acceptance date: 20.10.2026

For citation:

Litvin V.V., Korovin D.I., Krinichansky K.V., Maniakhin T.V. Expert analysis of the risks of creating and increasing “long money” in the Russian economy // Vestnik Instituta Ekonomiki Rossiyskoy Akademii Nauk. 2026. № 5. Pp. 109-134. (In Russ.). https://doi.org/10.52180/2073-6487_2026_5_109_134 EDN: TXQCJM

  Creative Commons 4.0

The Bulletin of the Institute of Economics of the Russian Academy of Sciences № 4/2026. Finance.

JATS XML

Victoria A. Bannikova

Cand. Sci. (Econ.), Junior Researcher, Mathematical Modeling of Economic Processes Department, Institute of Applied Economic Research, Russian Presidential Academy of National Economy and Public Administration, Moscow, Russia

ORCID: 0000-0002-2140-3875

MARKER GOODS OF INFLATION ATTENTION: CHANGES IN THEIR CATEGORIES AND TYPES UNDER THE TRANSFORMATION OF THE RUSSIAN ECONOMY, 2014–2026

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The article analyzes changes in the population’s attention to information related to inflation for the periods from 2014 to 2021 and 2022 to 2026. Based on the results of a study conducted using regularization and control function methods on data from Internet queries on the topic of “inflation” and Russian statistics, it is shown that in 2014–2021, the leading marker goods were petroleum products and food products (meat, fish, dairy products), while in 2022–2026 importance of several food products (meat, fish) decreased, while the importance of import-dependent non-food products (clothing, knitwear, tobacco products, printed products) increased. Categories with a negative price–attention relationship (medical goods, footwear, construction materials, public catering) are identified, which is explained by limited availability of these goods and, consequently, limited attention to inflation in these categories, postponed consumption, and substitution with cheaper alter-natives. Monitoring marker goods of inflation attention growth is essential for anchoring expectations and improving inflation targeting effectiveness.

Keywords: inflation attention, marker goods, Internet queries, consumer price index.

JEL: D12, E31, E52

EDN: HTLAKC

DOI: https://doi.org/10.52180/2073-6487_2026_4_96_117

Financial support: The article was written on the basis of the RANEPA state assignment research programme.

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Manuscript submission date: 12.06.2026

Manuscript acceptance date: 13.08.2026

For citation:

Bannikova V.A. Marker goods of inflation attention: changes in their categories and types under the transformation of the Russian economy, 2014–2026 // Vestnik Instituta Ekonomiki Rossiyskoy Akademii Nauk. 2026. № 4. Pp. 96-117. (In Russ.). https://doi.org/10.52180/2073-6487_2026_4_96_117 EDN: HTLAKC

  Creative Commons 4.0

The Bulletin of the Institute of Economics of the Russian Academy of Sciences № 4/2026. Finance.

JATS XML

Dmitry M. Kruk

Postgraduate Student of the Department of World Economy, Faculty of Economics, Lomonosov Moscow State University, Moscow, Russia;

Employee of the Global Markets Department, Moscow Branch of Sberbank of Russia, Moscow, Russia

ORCID: 0009-0001-7463-2065

THE IMPACT OF DIGITAL TECHNOLOGIES ON THE TRANSFORMATION OF KEY ELEMENTS OF FINANCIAL ARCHITECTURE

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The article provides a comprehensive analysis of the convergence of innovative financial technologies driving the transformation of the global financial architecture (GFA) in the 2020–2026 period. Based on systematized data from the Bank for International Settlements (BIS), the International Monetary Fund (IMF), the Financial Stability Board (FSB), and the World Economic Forum (WEF), three key technological vectors are examined: tokenization of real-world assets (RWA), central bank digital currencies (CBDC), and the application of artificial intelligence (AI) in the financial sector. The author’s contribution consists of a quantitative verification of factors determining the CBDC implementation stage, using panel data from 85 countries for the 2020–2024 period with ordered logistic regression. The study finds that cryptocurrency penetration, quality of the regulatory environment, and financial sector depth significantly increase the probability of a country advancing to a more developed CBDC project stage. It was found that the intensity of institutional response–the adoption of the MiCA regulation in the EU and the GENIUS Act in the US–is a valid indica-tor of the transformative impact of platform solutions on financial institutions.

Keywords: global financial architecture, real-world asset tokenization, central bank digital currencies, CBDC, blockchain, artificial intelligence, stablecoins, DeFi, MiCA, cross-border payments.

JEL: E42, F33, G18, G23, O33

EDN: MUIEHB

DOI: https://doi.org/10.52180/2073-6487_2026_4_118_133

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Manuscript submission date: 08.06.2026

Manuscript acceptance date: 13.08.2026

For citation:

Kruk D.M. The impact of digital technologies on the transformation of key elements of financial architecture // Vestnik Instituta Ekonomiki Rossiyskoy Akademii Nauk. 2026. № 4. Pp. 118-133. (In Russ.). https://doi.org/10.52180/2073-6487_2026_4_118_133 EDN: MUIEHB

  Creative Commons 4.0

The Bulletin of the Institute of Economics of the Russian Academy of Sciences № 4/2026. Finance.

JATS XML

Aleksandr V. Milenkov

Dr. Sci. (Econ.), Associate Professor, Professor of the Department of Global Financial Markets and Fintech, Plekhanov Russian University of Economics, Moscow, Russia

ORCID: 0000-0001-7812-2348

Svetlana V. Frumina

Cand. Sci. (Econ.), Associate Professor, Head of the Department of Global Financial Markets and Fintech, Plekhanov Russian University of Economics, Moscow, Russia;

Associate Professor of the Department of Public Finance, Financial University under the Government of the Russian Federation, Moscow, Russia

ORCID: 0000-0001-5143-9417

AI-DRIVEN FINANCIAL ADVISORY: FROM OVERCOMING INFORMATION ASYMMETRY TO NEW RISKS AND REGULATORY CHALLENGES

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The article analyzes the Bank of Russia’s initiative to create a specialized AI agent for selecting financial products, which was announced in April 2026. It examines architectural and regulatory approaches to implementing agentic systems in financial advisory services. Based on a synthesis of international and Russian research, the study explores the economic nature of AI intermediation as a mechanism for overcoming information asymmetry, which simultaneously creates new challenges: technical vulnerabilities of algorithms, institutional risks, user interest manipulation, market latency, and the problem of liability distribution. Special attention is paid to the evolution of regulatory approaches abroad, the identification of factors determining the success of agentic systems implementation, as well as the analysis of international precedents. The author proposes a classification of maturity levels for AI financial intermediaries. Recommendations are formulated for the governance of the AI agent aimed at balancing technological autonomy, fiduciary responsibility, and competitive neutrality.

Keywords: artificial intelligence, AI agent, financial advisory, information asymmetry, Bank of Russia, AI technical risks, behavioral risks, market latency, responsibility for AI recommendations.

JEL: G18, G23, G28, O33

EDN: HICHNS

DOI: https://doi.org/10.52180/2073-6487_2026_4_70_95  

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Manuscript submission date: 08.06.2026

Manuscript acceptance date: 13.08.2026

For citation:

Milenkov A.V., Frumina S.V. AI-driven financial advisory: from overcoming information asymmetry to new risks and regulatory challenges // Vestnik Instituta Ekonomiki Rossiyskoy Akademii Nauk. 2026. № 4. Pp. 70-95. (In Russ.). https://doi.org/10.52180/2073-6487_2026_4_70_95 EDN: HICHNS

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