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Administrative and municipal law
Reference:

Using foreign experience in the use of artificial intelligence in the supervision of financial market participants in Russia.

Atabekov Atabek Rustamovich

PhD in Economics

Associate Professor of the Department of Administrative and Financial Law, Legal Institute, Peoples' Friendship University of Russia

117198, Russia, Moscow, Miklukho-Maklaya str., 6

atabekoff1@mail.ru
Other publications by this author
 

 

DOI:

10.7256/2454-0595.2023.3.40718

EDN:

UDQHSQ

Received:

07-05-2023


Published:

14-05-2023


Abstract: Within the framework of this article, a comparative analysis of existing approaches to the use of artificial intelligence (AI) in the field of control and supervision of financial market participants in foreign countries and Russia is carried out. As part of the comparative analysis, basic problems were identified in the field of ensuring the accuracy of analytical tools used to detect facts of market manipulation and insider trading, theoretical and practical situations of using artificial intelligence in the supervisory activities of public and private financial institutions were considered, and additional compensatory legal measures were proposed to ensure effective integration of artificial intelligence and the use of AI for the purposes of financial supervision in Russia. The subject of the study is the features of legal relations that develop in the course of AI use in the framework of the supervision of financial activities. The object of the study are regulations, recommendations and other documents regulating the use of artificial intelligence for supervisory activities aimed at preventing manipulation of the financial market and insider trading, judicial practice, academic publications and analytical reports on the issues under study. The research methodology integrates a complex of modern philosophical, general scientific, special scientific methods of cognition, including dialectical, systemic, structural-functional, hermeneutical, comparative legal, formal legal (dogmatic), etc. Within the framework of this study, special emphasis is laid on the implementation of a comparative legal study of the areas of AI application in financial supervisory activities, the identification of common problems in the AI application by authorities and the development of common approaches. The measures proposed as a result of the study can be applied in the legislative and legal practice of relevant authorities implementing the integration of artificial intelligence into the sphere of public relations in Russia, including the field of control and supervision activities in the financial market.


Keywords:

artificial intelligence, electronic person, comparative legal research of AI, financial law, algorithmic trading, safety AI, public law, administrative law, information law, law enforcement practice

This article is automatically translated.

It is well known that the modern financial market is the basis for the modern global economy. Financial markets include stock exchanges such as the ASX in Australia, NASDAQ in the USA, and RTSI in Russia, which provide platforms that support corporate fundraising and provide opportunities to facilitate trade and investment.

In his speech, Tony D'Aloisio (ex-chairman of the Australian Financial Regulator ASIC) notes that the fundamental purpose of the Corporations Act is investors' faith in the fairness of the actions of all participants in the financial markets. Actions aimed at market manipulation or illegal insider trading damage their trust and prevent them from participating in these markets[1].

As O.M. Shevchenko notes, the level of effectiveness of regulatory measures in this area directly affects the degree of investment attractiveness of the economy of a particular state where investors are directed[2].

A similar position is shared by Brown and Glodschmidt, who determine that effective tools for detecting unusual trading behavior are the basis for further strengthening the confidence of market participants, increasing market liquidity and reducing the cost of capital for business[3].

The regulatory framework for preventing insider trading and market manipulation consists of the following documents:

1.                 In Australia, the Corporations Act 2001 qualifies actions related to market manipulation and insider trading as crimes (section 1043A and 1041A-H)[4]. Within the framework of these provisions, a person who possesses insider information is prohibited from trading a financial product that this information may "significantly affect". At the same time, the key issue for classifying information as insider is the possibility for the subject of the offense to determine it as significantly affecting the price or value of a financial product. In addition, in accordance with the legislative provisions, manipulation is not required to lead to a beneficial result for the person involved[5].

2.                 In the United States, the Securities and Exchange Commission (SEC) and the Financial Industry Regulatory Authority (FINRA) play a regulatory role in controlling financial markets. Both organizations provided guidance on market manipulation and insider trading[6]. General approaches for identifying actions as insider trading are presented in the FINRA 2020 Rules, which states that no entity should make any transactions or encourage the purchase or sale of any security using any manipulative, misleading or other fraudulent action or device[7]. In addition, SEC rule 10b5-1 prohibits the purchase or sale of securities of any issuer based on material classified information about these securities[8].

3.                 In the Russian Federation, the Central Bank of Russia plays a central role in the regulation of financial markets. The fundamental regulatory framework is Federal Law No. 224-FZ of July 27, 2010 "On Countering the Misuse of Insider Information and Market Manipulation and on Amendments to Certain Legislative Acts of the Russian Federation", which regulates relations arising in the commodity, currency and stock organized markets. At the same time, detailed provisions regarding the definition of insiders (12 categories) and manipulative actions in relation to financial markets are presented (8 actions) in Articles 4 and 5, respectively.

It should be noted that the starting point for the emergence of algorithmic decision support systems for exchange trading purposes is 1998, in which the SEC approved the possibility of using trading robots[9].

At the moment, high automation of trade, openness to data and the presence of a large information array give rise to a large list of issues for representatives of control and supervisory activities that require a predictive set of responses.

Financial institutions face a high level of risk of internal and external influence for criminal purposes requiring assistance from supervisory authorities. The IBM report records that financial institutions record a very large number of false positive notifications about potential suspicious activity, which do not lead to its subsequent fixation or registration. The reason for this is a combination of outdated technology and incomplete and inaccurate data[10].

The volume of daily data is constantly growing and requires new models of response and analysis of the actions of market participants by the supervisory authorities[11-12]. The NASDAQ study signals that the growth of stock market participants using trading monitoring algorithms leads to an increase in false positives of algorithms and, as a result, creates information noise for regulatory authorities, complicating their activities[13].

The IOSCO Market Surveillance report signals that trading in financial instruments has become more dispersed across a growing number of trading platforms and is difficult to track. High-frequency asset trading without strict reference to geographical and other boundaries is carried out at speeds faster than a millisecond. This fact is actively used by traders for manipulative activities and is a difficult factor to disclose for regulatory and supervisory authorities[14].

In this regard, it becomes logical to introduce artificial intelligence technology to ensure compliance with regulatory requirements by financial market participants who use high-precision trading algorithms and other automated decision-making and analysis systems.

In this regard, it is necessary to consider the experience of AI forecasting in the related field of law enforcement – law enforcement and judicial activities. Two software tools, ECHO and ALIBI, are particularly interesting examples of the potential use of AI for policing[15]. ECHO uses artificial neural networks to model the judge's reasoning based on the input evidence in the case, and at the output a predictive transcript of the court decision on a specific case is formed. ALIBI is a program that simulates the accused of committing a crime. The algorithm uses a neural network to deconstruct the defendant's actions into their component parts, and then to build a defensive strategy around each of these composite actions to reduce guilt, modeling the behavior of the jury, based on the preliminary state of the evidence that is supposed to be used during the trial.

Considering similar cases with regard to control and supervisory detail, it is necessary to note the SMARTS software tool (created back in the 1990s), which monitors the activities of financial market participants represented at more than 30 sites around the world, including the Hong Kong Stock Exchange[16]. This program analyzes the activity of traders, and then issues warnings about suspicious transactions, which are subsequently reported to the relevant authorities.  At the same time, profile employees have to track the relevant trading data, according to which a signal was received from the system not only in one area (the traded instrument), but also in all related and adjacent segments of the financial market[17].

The Central Bank of Russia also actively uses technologies in the field of algorithmic remote monitoring of financial markets, including SupTech and RegTech technologies[18]. Experts note the problem of unfair assessment by financial organizations of the fair value of assets due to the formalist approach of financial organizations to the establishment of criteria for an active market, and in some cases also by deliberately setting criteria corresponding to the minimum trading activity[19].

Active implementation of machine learning (an integral element of AI, which implies an autonomous solution of the problem without active human intervention on the basis of existing methods of cognition)[20] will reduce the previously mentioned information noise generated by analytical systems and increase the effectiveness of the control and supervisory detail of the authorities. In addition, the existing analytical systems are deprived of the possibility of analyzing the trading system, communication data (online and offline communication of traders) and other implicit sources of information.

One such practical example is IBM Financial Crimes Insight, which offers a holistic and cognitive approach to monitoring virtually all activities related to employees involved in trading and related activities with financial markets. It goes beyond the traditional detection of alerts based on algorithmic trading and actively monitors employees in real time, including emails, chat transcripts, voice recordings, trading and market data[21].

At the same time, one of the constant problems for the use of such AI tools is access to data and confidentiality. Financial regulators, trading platforms and market participants have access to data that may be useful for identifying and investigating financial offenses, but all relevant data is usually not available to all participants. This may limit the applicability of surveillance technologies based on pooled data sources. Referring to the combination of artificial intelligence and human intelligence, IBM recognizes this risk and argues that AI is the next stage in the fight against financial crimes, which can be effectively implemented with a consolidated approach.

In practice, market manipulation is carried out by "stuffing" false information through mailing lists, news feeds, etc., which allows unreasonably raising or lowering the rate of the quoted instrument[22], or creating other strategies inside the "glass" that create an artificial deficit or surplus of the traded instrument[23]. In addition, the behavior of attackers when manipulating the market will depend on the trading volume of a particular financial product and the volatility of the market of this product, which will also significantly affect the AI analysis algorithms and potentially mislead it.

In this context, it is necessary to consider the experience of using digital sandboxes that allow you to create a secure environment based on real data sets together with synthetically generated scenarios that provide testing in market conditions that are not present in historical data sets. At the level of practical implementation, it is necessary to note the positive experience of the UK financial regulator, which processes over 200 databases on traded instruments within the sandbox[24].

V.V. Mazurin in his research comes to the conclusion that one of the positive aspects of the integration of AI into the sphere of financial law and regulation of relations arising in financial markets for the purposes of supervisory activities is that AI allows you to remove a significant part of the analytical burden and collection of evidence, and is not subject to corruption risks[25].

M.V. Anashkina notes a significant time delay in the detection of cases of manipulation of the domestic financial market, amounting to more than 3 years. At the heart of this delay, the author notes the technological lag of market analysis tools, the imperfection of legislation in this area and the lack of existing real-time analysis tools[26].

As part of a comprehensive review of the digitalization of the public administration function, including the introduction of AI in financial supervision, A.E. Morozov notes the need to create regulatory platforms and strengthen the role of self-regulation for professional intermediaries and IT companies (including those engaged in AI development) with the direct participation of the Bank of Russia.[27] E.V. Goryan declares a similar position on the basis of legal research, having studied the experience of implementing AI in the public and financial sector of Singapore[28]. As a result of the analysis of the instrument developed by the Monetary Authority of Singapore for regulating processes in the field of artificial intelligence, and the practical case of DBS Bank, the need to develop flexible regulatory approaches to AI technology and the formation of a framework model for AI management is noted.

Considering the issue of separate regulation of AI in the context of countering the legalization of proceeds from crime, it is necessary to highlight and take into account the position of O.A. Yastrebov, who proposes to use the design of an electronic person to determine the actions of artificial intelligence, as well as to ensure the introduction of this category into the current legislation in order to regulate relations, one of the elements of which is artificial intelligence[29].

Based on the above, the author of this article formulates the following proposals for the further effective use of AI in the field of control and supervisory activities of the financial markets of Russia:

1. It is necessary to form regulatory approaches that ensure the creation of digital twins and regulatory sandboxes for AI, which is planned to be implemented and applied within the framework of the supervisory activities of the Central Bank of Russia and other authorities.

2. It is necessary to legislate additional requirements for government officials implementing AI; the requirements should take into account both qualification positions (education, experience in AI development, maintenance, advanced training in AI, etc.) and minimizing the risks of bias against technological solutions in the field of AI.

3. When developing AI technology for the financial supervision of the Bank of Russia, it is necessary to legislate a direct ban on the dissemination of the AI technology being developed for participants in the financial market and related sub-sectors of the specified market.

4. It is also proposed to legislate the obligation to provide the source code and the tool for the operational shutdown of AI to the Bank of Russia in the case of the use of AI by financial market participants.

References
1. Li X. et al. Design theory for market surveillance systems //Journal of Management Information Systems. – 2015. – Ò. 32. – ¹. 2. – P. 278-313.
2. Shevchenko O. M. The concept and types of insiders under Russian law //Courier of Kutafin Moscow State Law University (MSAL)). – 2015. – ¹. 1. – P. 92-101.
3. Brown P., Goldschmidt P. Alcod idss: Assisting the Australian stock market surveillance team's review process //Applied artificial intelligence. – 1996. – Ò. 10. – ¹. 6. – P. 625-642.
4. Corporations Act 2001 ¹ 50, 2001: [Website]. — URL: https://www.legislation.gov.au/Details/C2019C00216 (Accessed: 03.05.2023).
5. Chitimira H. The regulation of market manipulation in Australia: a historical comparative perspective //Potchefstroom Electronic Law Journal. – 2015. – Ò. 18. – ¹. 2. – P. 111-148.
6. The Code of Federal Regulations (CFR) 15 U.S.C. 78j, § 240.10b5-1: [Website]. — URL: https://www.ecfr.gov/current/title-17/chapter-II/part-240/subpart-A/subject-group-ECFR71e2d22647918b0/section-240.10b5-1 (Accessed: 03.05.2023).
7. Use of Manipulative, Deceptive or Other Fraudulent Devices: [Website]. — URL: https://www.finra.org/rules-guidance/rulebooks/finra-rules/2020 (Accessed: 03.05.2023).
8. SEC Rule 10b5-1: [Website]. — URL: https://www.sec.gov/news/press-release/2022-222 (Accessed: 03.05.2023).
9. SEC Modernizes Regulations for Alternative Trading Systems; Streamlines Process of Introducing Products and Operating Pilot Trading Systems: [Website]. — URL: https://www.sec.gov/news/press/pressarchive/1998/98-127.txt (Accessed: 03.05.2023).
10. IBM, ITERA Report 2021, Fighting Financial Crime with AI: [Website]. — URL: https://f.hubspotusercontent10.net/hubfs/8054464/Reports/Fighting%20financial%20crime%20with%20AI%20(2).pdf (Accessed: 03.05.2023).
11. ASIC is responsible for the supervision of real-time trading on Australia's domestic licensed markets: [Website]. — URL: https://asic.gov.au/regulatory-resources/markets/market-supervision/ (Accessed: 03.05.2023).
12. Annual report of the Bank of Russia for 2022: [Website]. — URL: https://www.cbr.ru/Collection/Collection/File/43872/ar_2022.pdf (Accessed: 03.05.2023).
13. Nasdaq Global Compliance Survey: [Website]. — URL: https://www.nasdaq.com/Nasdaq-Global-Compliance-Survey (Accessed: 03.05.2023).
14. Technological Challenges to Effective Market Surveillance Issues and Regulatory Tools: [Website]. — URL: https://www.iosco.org/library/pubdocs/pdf/IOSCOPD389.pdf (Accessed: 03.05.2023).
15. Nissan E. Digital technologies and artificial intelligence’s present and foreseeable impact on lawyering, judging, policing and law enforcement //Ai & Society. – 2017. – Ò. 32. – P. 441-464.
16. Nasdaq Trade Surveillance (SMARTS): [Website]. — URL: https://www.nasdaq.com/solutions/nasdaq-trade-surveillance (Accessed: 03.05.2023).
17. FMSB, «Spotlight Review: Monitoring FICC markets and the impact of machine learning»: [Website]. — URL: https://service.betterregulation.com/document/455188 (Accessed: 03.05.2023).
18. The Bank of Russia, «RegTech è SupTech»: [Website]. — URL: https://www.cbr.ru/fintech/reg_sup/ (Accessed: 03.05.2023).
19. The Bank of Russia, Main directions of technology development SupTech è RegTech for the period 2021-2023: [Website]. — URL: http://www.cbr.ru/content/document/file/120709/suptech_regtech_2021-2023.pdf (Accessed: 03.05.2023).
20. Slemmer D. W. Artificial Intelligence & Artificial Prices: Safeguarding Securities Markets from Manipulation by Non-Human Actors //Brook. J. Corp. Fin. & Com. L. – 2019. – Ò. 14. – P. 149.
21. Fighting financial crime with AI: [Website]. — URL: https://www.acfcs.org/fighting-financial-crime-with-ai-how-cognitive-solutions-are-changing-the-way-institutions-manage-aml-compliance-fraud-and-conduct-surveillance-new-ibm-whitepaper/ (Accessed: 03.05.2023).
22. SEC, «Pump and Dump Schemes»: [Website]. — URL: https://www.investor.gov/introduction-investing/investing-basics/glossary/pump-and-dump-schemes (Accessed: 03.05.2023).
23. Fox M. B., Glosten L. R., Rauterberg G. V. Stock market manipulation and its regulation //Yale J. on Reg. – 2018. – Ò. 35. – P. 67.
24. FCA confirms Digital Sandbox for UK financial services as it continues AI work: [Website]. — URL: https://techmonitor.ai/technology/emerging-technology/fca-digital-sandbox-financial-services (Accessed: 03.05.2023).
25. Mazurin V. V. Artificial intelligence in the implementation of the norms of financial law // Altai Legal Bulletin. – 2021. – ¹. 3. – P. 34-38.
26. Anashkina M.V. The evolution of the securities market and modern problems of control of its participants. // Vestnik Universiteta. – 2022. – ¹. 8. – P. 129-138.
27. Morozov A.E. MODIFICATION OF THE FINANCIAL CONTROL MODEL IN THE CONDITIONS OF DIGITAL TRANSFORMATION // Courier of Kutafin Moscow State Law University (MSAL)). – 2019. – ¹. 7 (59). – P. 22-26.
28. Gorian E.V. Artificial Intelligence in the financial and banking sector: experience of Singapore//The Territory of New Opportunities. The Herald of Vladivostok State University of Economics and Service. – 2020. – Ò. 12. – ¹. 3. – P. 86-99.
29. Yastrebov Î. À., Aksenova Ì. À. THE LAW ISSUES OF IMPACT OF ARTIFICIAL INTELLIGENCE ON THE ADMINISTRATIVE REGIME FOR COMBATING MONEY LAUNDERING AND TERRORISM FINANCING// Legal policy and legal life. – 2022. – ¹. 3. – P. 84-109.

Peer Review

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A REVIEW of an article on the topic "Using foreign experience in the use of artificial intelligence in the supervision of financial market participants in Russia". The subject of the study. The article proposed for review is devoted to topical issues of the use of artificial intelligence in the field of supervision of financial market participants in Russia. The author studies foreign experience in this field (in particular, in the USA and Australia), and also suggests ideas for improving legal regulation in our country. The subject of the study was the norms of Russian and foreign legislation, the opinions of scientists, and business practice in the field under consideration. Research methodology. The purpose of the study is not stated directly in the article. At the same time, it can be clearly understood from the title and content of the work. The purpose can be designated as the consideration and resolution of certain problematic aspects of the issue of the use of artificial intelligence in the supervision of financial market participants in Russia, including on the basis of generalization of foreign experience. Based on the set goals and objectives, the author has chosen the methodological basis of the study. In particular, the author uses a set of general scientific methods of cognition: analysis, synthesis, analogy, deduction, induction, and others. In particular, the methods of analysis and synthesis made it possible to summarize and share the conclusions of various scientific approaches to the proposed topic, as well as draw specific conclusions from business practice materials. The most important role was played by special legal methods. In particular, the author actively applied the formal legal method, which made it possible to analyze and interpret the norms of current legislation (primarily the norms of Russian and foreign legislation). For example, the following conclusion of the author: "In the Russian Federation, the Central Bank of Russia plays a central role in the regulation of financial markets. The fundamental regulatory framework is Federal Law No. 224-FZ of July 27, 2010 "On Countering the Misuse of Insider Information and Market Manipulation and on Amending Certain Legislative Acts of the Russian Federation", which regulates relations arising in commodity, currency and stock organized markets. At the same time, detailed provisions regarding the definition of insiders (12 categories) and manipulative actions in relation to financial markets are presented (8 actions) in Articles 4 and 5, respectively." The possibilities of a comparative legal research method related to the study and generalization of the experience of foreign countries should be positively assessed. Thus, it is noted that "In Australia, the Corporations Act 2001 qualifies actions related to market manipulation and insider trading as crimes (Section 1043A and 1041A-H)[4]. Within the framework of these provisions, a person in possession of insider information is prohibited from trading a financial product that this information may "significantly affect". At the same time, the key issue for classifying information as insider information is the possibility for the subject of the offense to determine it as significantly affecting the price or value of a financial product. In addition, in accordance with the legislative provisions, it is not required that manipulation lead to a beneficial result for the person involved[5]." Thus, the methodology chosen by the author is fully adequate to the purpose of the study, allows you to study all aspects of the topic in its entirety. Relevance. The relevance of the stated issues is beyond doubt. There are both theoretical and practical aspects of the significance of the proposed topic. From the point of view of theory, the topic of using modern technologies in the supervision of financial market participants in Russia is complex and ambiguous. To understand it correctly, it is necessary to take into account various legal theoretical models, as well as the development of technologies. The author is right to highlight this aspect of relevance. On the practical side, it should be recognized that ideas for improving Russian legislation may be important, which can be helped, among other things, by foreign experience. Thus, scientific research in the proposed field should only be welcomed. Scientific novelty. The scientific novelty of the proposed article is beyond doubt. Firstly, it is expressed in the author's specific conclusions. Among them, for example, is the following conclusion: "It is necessary to form regulatory approaches that ensure the creation of digital twins and regulatory sandboxes for AI, which is planned to be implemented and applied within the framework of the supervisory activities of the Central Bank of Russia and other authorities." These and other theoretical conclusions can be used in further scientific research. Secondly, the author suggests ideas for improving the current legislation. In particular, "It is necessary to legislate additional requirements for government officials implementing AI; requirements should take into account both qualification positions (education, experience in AI development, maintenance, advanced training in AI, etc.) and minimizing the risks of bias against technological solutions in the field of AI. When developing AI technology for financial supervision of the Bank of Russia, it is necessary to legislate a direct ban on the dissemination of AI technology being developed for participants in the financial market and related sub-sectors of the specified market." The above conclusion may be relevant and useful for law-making activities. Thus, the materials of the article may be of particular interest to the scientific community in terms of contributing to the development of science. Style, structure, content. The subject of the article corresponds to the specialization of the journal "Administrative and Municipal Law", as it is devoted to legal problems related to the supervision of financial market participants in Russia. The content of the article fully corresponds to the title, as the author considered the stated problems and achieved the research goal. The quality of the presentation of the study and its results should be recognized as fully positive. The subject, objectives, methodology and main results of the study follow directly from the text of the article. The design of the work generally meets the requirements for this kind of work. No significant violations of these requirements were found. Bibliography. The quality of the literature used should be highly appreciated. The author actively uses the literature presented by authors from Russia and abroad (Li X., Brown P., Goldschmidt P., Chitimira H., Shevchenko O.M., Mazurin V.V., Anashkina M.V., Morozov A.E. and others). It can also be noted that the author actively uses sources of foreign authors and legal acts in English, which is especially important in the context of the purpose of the reviewed article. Thus, the works of the above authors correspond to the research topic, have a sign of sufficiency, and contribute to the disclosure of various aspects of the topic. Appeal to opponents. The author conducted a serious analysis of the current state of the problem under study. All quotations of scientists are accompanied by author's comments. That is, the author shows different points of view on the problem and tries to argue for a more correct one in his opinion. Conclusions, the interest of the readership. The conclusions are fully logical, as they are obtained using a generally accepted methodology. The article may be of interest to the readership in terms of the systematic positions of the author in relation to the issues of improving aspects of the use of artificial intelligence in the supervision of financial market participants in Russia. Based on the above, summing up all the positive and negative sides of the article, "I recommend publishing"