CC BY
12
Legal Support of Artificial Intelligence in Countering Anti-Money Laundering and Terrorism Financing Regimes in the BRICS Plus Countries
Marina Aksenova,
RUDN University (Moscow, Russian Federation)
https://doi.org/10.21684/2412-2343-2024-11-3-92-116
Abstract. The increasing integration of artificial intelligence technologies into the financial structures of the BRICS Plus countries (comprising the original member countries of Brazil, Russia, India, China, and South Africa as well as the four new member countries of Egypt, Ethiopia, Iran, and the United Arab Emirates) presents both opportunities and challenges in combating economic crimes, which include money laundering and terrorism financing. This article explores the complex regulatory landscape that governs the application of artificial intelligence in these efforts. It examines how artificial intelligence can enhance the performance of anti-money laundering and counter-terrorism financing frameworks by enabling the evaluation of massive datasets, the identification of anomalous transaction patterns, and the automation of compliance procedures. Simultaneously, the article addresses the highly challenging situations that arise when using artificial intelligence. For instance, these technologies can make it difficult to understand the fluctuation of illicit price ranges, thereby complicating efforts to determine their origins and destinations. Through a comparative analysis of the frameworks throughout the BRICS Plus countries, this research highlights the varying levels of regulatory readiness of these frameworks and proposes pathways for harmonizing artificial intelligence-driven economic security measures. The overarching goal of an artificial intelligence model is to enhance both the effectiveness and the integrity of the financial sectors in the BRICS Plus consortium, necessitating a collaborative approach to combating financial crimes in an increasing number of digital economies across the world.
Keywords: artificial intelligence; anti-money laundering; counter-terrorism financing; transparency; financial intelligence units; compliance; regime; BRICS Plus.
© Aksenova M.
2024 CC BY-NC-ND 4.0
Recommended citation: Marina Aksenova, Legal Support of Artificial Intelligence in Countering Anti-Money Laundering and Terrorism Financing Regimes in the BRICS+ Countries, 11(3) BRICS Law Journal 92-116 (2024).
Table of Contents
Introduction
1. Theoretical Framework for Countering Money Laundering and Terrorism Financing
2. Prospects for the Integration of Artificial Intelligence
into the Financial Monitoring Systems of the BRICS Plus Countries
2.1. Integration of Artificial Intelligence into Financial Monitoring in Russia
2.2. Integration of Artificial Intelligence into Financial Monitoring in India, China, Brazil, South Africa, the United Arab Emirates, and Ethiopia
2.2.1. India
2.2.2. China
2.2.3. Brazil
2.2.4. South Africa
2.2.5. United Arab Emirates
2.2.6. Ethiopia
3. Challenges in the Legal Implementation of Artificial Intelligence
3.1. Technological Limitations and Ethical Considerations
3.2. Regulatory Harmonization Conclusion
Introduction
The phenomena of money laundering and terrorism financing represent some of the most pressing challenges confronting the global financial system today. These illicit activities not only undermine the integrity of financial institutions but also pose severe threats to the economic and political stability of nations worldwide. The estimated amounts laundered globally in one year range between 2% and 5% of the global gross domestic product (GDP), underscoring the massive scale and impact of these offenses. Such financial crimes can destabilize economies by distorting market competition, reducing public revenues, and causing governance crises.1
The United Nations website on Drugs and Crime (UNODC) (June 10, 2024), available at https://www. unodc.org.
Given these challenges, the use of advanced technologies like artificial intelligence (AI) has become crucial in the fight against financial crimes. The capabilities of artificial intelligence in data analysis, pattern recognition, and predictive modeling make it an excellent tool for detecting and preventing complex money laundering schemes and terrorism financing operations. These systems can process vast amounts of financial data far more rapidly and accurately than humans, uncovering hidden connections and suspicious activities that mig ht otherwise go unnoticed. Additionally, a rtificia l i ntel ligence can enhance compliance efficiency by automating routine checks and flagging anomalies for further investigation, thereby strengthening overall financial security.
For the BRICS Plus nations, the integration of artificial intelligence into financial security operations holds particular significance. As emerging economic powerhouses, the BRICS Plus countries face unique challenges posed by their rapidly expanding financial markets, which can often outpace regulatory and technological advancements. Each of these countries displays a distinct blend of legal, cultural, and technological backgrounds, which influences their approach to regulating artificial intelligence in financial applications. The need for effective artificial intelligence-driven tools has been made even more pressing by the significant role of the BRICS Plus countries in the global economy, where they are increasingly becoming targets for transnational financial crimes. Consequently, enhancing the legal frameworks that govern the use of artificial intelligence in financial monitoring is crucial, not only to safeguard individual economies but also to contribute to global financial security.
The use of artificial intelligence in financial monitoring in the BRICS Plus countries shows the importance of adapting to new technology in order to protect financial systems from newly emerging forms of financial crime. By using artificial intelligence, these countries can greatly improve their abilities to detect and prevent money laundering and terrorism financing, thus safeguarding their economies and contributing to worldwide financial stability. It is therefore crucial to have strong laws and adequate regulations in place that support the use of artificial intelligence in financial monitoring, as well as ensure that these tools are used responsibly and effectively. As the BRICS Plus countries continue to develop their artificial intelligence capabilities, they also have an opportunity to set an example for the rest of the world in the fight against financial crimes.
1. Theoretical Framework for Countering Money Laundering and Terrorism Financing
Artificial intelligence plays a vital role in countering money laundering and terrorism financing as well as in preserving the integrity of world monetary systems. The theoretical framework for these efforts combines financial and technological techniques that focus on preventing the misuse of the monetary framework by criminal elements.
At its core, the prevention of money laundering and terrorism financing rests on the identification, analysis, and management of financial risks associated with illicit activities. According to the Financial Action Task Force (FATF), the international standard-setter for combating money laundering, the approach starts with the development of robust legal systems that criminalize money laundering and terrorism financing and allow for the tracing, freezing, and confiscation of illicit proceeds.2
Economically, money laundering and terrorism financing strategies involve understanding the patterns of money flows in the economy. As a result, economists play a critical role in the process of analyzing transaction data to detect anomalies that signify money laundering or terrorism financing activities. The IMF highlights the importance of these activities, emphasizing that without effective money laundering and terrorism financing prevention measures, the economic stability of nations can be at risk due to the infiltration of illicit money into the legitimate economy.3
Technologically, advancements in artificial intelligence and data analytics have transformed money laundering and counter terrorism financing efforts. These tools allow for the automated detection of suspicious transactions in large datasets, which is a significant step forward given the volume and complexity of modern financial transactions. Researchers advocate for the use of these technologies not just for detection but also for the prediction of potential money laundering and terrorism financing activities.4
Legal scholars argue that while technology can significantly enhance enforcement capabilities, there must be a balance with legal principles such as privacy and due process. The legal framework should provide clear guidelines on the use of technology so as to ensure that the rights of individuals are protected while effectively countering money laundering and terrorism financing.5
The theoretical framework for preventing money laundering and terrorism financing is multidisciplinary, related to penal, economic, and technological aspects. Each of these aspects plays an important role in creating a complete protection response in opposition to those worldwide threats that undermine both safety and economic stability.
In their research articles, D. Cyman., E. Gromova., and E. Juchnevicius discuss the comparative legal analysis of artificial intelligence regulation in BRICS and the EU,
3
Financial Action Task Force (FATF), International Standards on Combating Money Laundering and the Financing of Terrorism & Proliferation (2012) (June 10, 2024), available at https://www.fatf-gafi.org/content/dam/ fatf-gafi/recommendations/FATF%20Recommendations%202012.pdf.coredownload.inline.pdf.
International Monetary Fund (IMF), Review of the Fund's Anti-Money Laundering and Combating the Financing of Terrorism Strategy (December 2023) (June 10, 2024), available at https://www.imf.org/en/ Publications/Policy-Papers/Issues/2023/12/05/2023-Review-of-The-Funds-Anti-Money-Laundering-and-Combating-The-Financing-of-Terrorism-542015.
Mariam Hamad Al Amer, Artificial Intelligence and Financial Management (2021).
Edvinas Meskys et al., Regulating Deep Fakes: Legal and Ethical Considerations, 15(1) J. Intell. Prop. L. & Prac. 24 (2020).
advocating for a general regulatory approach akin to the EU's trustworthy artificial intelligence model to optimize digital transformation benefits.6 These regulations are essential not only to manage the technological impact but also to foster innovation, ensure economic safety, and balance the interests of individuals, society, and the state. Future trends need to continue to integrate these perspectives, while closely monitoring the implications of technological advancements.
2. Prospects for the Integration of Artificial Intelligence into the Financial Monitoring Systems of the BRICS Plus Countries
Each of the BRICS Plus countries possesses distinctive legal systems that reflect their unique historical, cultural, and social developments. Understanding the legal families and characteristics of these systems provides insights into how law is practiced and evolved in these influential economies.
Brazil's legal system is firmly rooted in the civil law tradition, strongly influenced by the Portuguese legal system due to its colonial history. This system is characterized by a comprehensive set of codified laws that are meticulously written and systematically organized. The Brazilian Constitution stands as the supreme law of the land, and its legal framework includes various substantive codes, such as the Civil Code and Criminal Code, which comprehensively cover different aspects of law. This systematic organization facilitates consistency and predictability in legal interpretations and applications.
Russia also adheres to the civil law system, with historical influences from Roman law and subsequent German and Dutch legal principles. The 1917 Bolshevik Revolution introduced Soviet legal theories that dominated until the dissolution of the Soviet Union in 1991. Post-Soviet Russia has experienced significant legal transformations, aiming to incorporate international legal standards to facilitate global economic interactions. Consequently, modern Russian law has integrated many aspects of business and corporate law from Western jurisdictions, reflecting a blend of traditional civil law principles and contemporary global practices.
India's legal system is a hybrid structure classified under the Common Law tradition, heavily influenced by its British colonial heritage. The Indian legal framework is comprehensive, incorporating a wide range of legislation, jurisprudence, and judicial precedents. Notably, Indian law includes detailed statutory codes in areas such as criminal law, civil procedure, and taxation. However, it relies significantly on judicial interpretations and case law to resolve disputes and develop legal principles, demonstrating the dynamic interplay between statutory law and judicial discretion characteristic of common law systems.
Damian Cyman et al., Regulation of Artificial Intelligence in BRICS and the European Union, 8(1) BRICS L.J. 86 (2021).
China's legal system is categorized under the framework of socialist law, significantly influenced by Soviet socialist law. Since the economic reforms initiated in the late 20th century, China has increasingly integrated elements of the civil law system, particularly in the country's commercial and civil laws, in order to better align with the global economy. Chinese law is distinguished by the pervasive influence of state authority and the policies of the ruling Communist Party, which shape the majority of its legal practices and interpretations. This integration reflects China's strategic adaptation of its policies to facilitate economic development while maintaining political control.
South Africa's legal system is an amalgamation of civilian law, common law, and customary law, reflecting its diverse cultural heritage. This mixed legal system incorporates elements from Dutch Roman Law, English Common Law, and indigenous African customary law. South Africa is notable for its robust constitutional framework, which emphasizes rights and freedoms. This country's framework has been deeply influenced by its historical context of apartheid and its subsequent abolition. The interplay of these legal traditions creates a unique legal landscape that balances historical influences with contemporary human rights principles.
The legal system of the United Arab Emirates is a hybrid that integrates aspects of both Islamic (Sharia) law and civil law. The UAE's legal framework is based on the Constitution of 1971, which establishes the federal structure of the country, comprising seven emirates. The Constitution provides for an independent judiciary and the rule of law, while also guaranteeing fundamental rights and freedoms.
The UAE has taken significant steps to modernize and internationalize its legal system with the aim to facilitate business and attract foreign investment. This includes establishing free zones with their own regulatory frameworks, such as the Dubai International Financial Centre (DIFC), which operates under an independent legal system based on English common law and has its own courts.
Overall, the UAE's legal system reflects a blend of traditional Islamic values and modern legislative practices, creating a unique legal environment that supports its rapid development and integration into the global economy.
The legal system in Ethiopia is a distinctive blend reflecting the country's diverse historical and cultural heritage. It is primarily based on civil law, with significant customary and religious law influences. The supreme law of the land is the 1995 Constitution of the Federal Democratic Republic of Ethiopia, which establishes a federal structure, the separation of powers, and fundamental rights and freedoms. Legislation is enacted by the federal House of Peoples' Representatives and regional councils for their respective states, covering various civil, criminal, commercial, and administrative areas.
Customary law is prevalent, especially in rural areas, and governs many aspects of personal and community life such as marriage, inheritance, and conflict resolution. These customary practices operate alongside formal judicial institutions.
Thus, the legal systems of the BRICS Plus countries exemplify diverse approaches to governance and legal structuring, with each adapting to its unique historical, political, and social contexts. These systems shape the administration of justice and compliance within each country, reflecting the complex interplay between tradition and modernity in legal evolution.
The prospects for integrating artificial intelligence into the financial systems of the BRICS Plus countries are interesting from the point of view of harmonizing the legislation of these countries. Each country has its own unique peculiarities in terms of national legislation, and hence it is important to thoroughly examine the experience and practice of applying legal regulations in each particular jurisdiction.
2.1. Integration of Artificial Intelligence into Financial Monitoring in Russia
The integration of artificial intelligence into financial monitoring systems in Russia marks a significant shift in the regulatory and operational landscape of the country's financial sector. Artificial intelligence technologies are increasingly being employed to enhance the efficiency and effectiveness of financial oversight mechanisms, particularly in regard to combating money laundering and financial fraud.
Artificial intelligence technologies in Russia are being used to analyze vast amounts of transactional data, identify patterns indicative of fraudulent activities, and improve the predictive capabilities of financial monitoring systems. The Russian government and private financial institutions recognize artificial intelligence's potential to streamline compliance processes and reduce the human workload in monitoring transactions. For example, artificial intelligence-based surveillance systems are now being utilized to enhance regulatory compliance by flagging trades that might violate rules, thereby improving the overall security of financial transactions.
In terms of the legal framework, Russia has been proactive in adapting its regulations in order to accommodate the use of artificial intelligence in financial services. The Russian Central Bank has issued guidelines aimed at encouraging the adoption of technological innovations in the financial sector while ensuring data protection and security. This is part of a broader strategy under Russia's "Digital Economy of the Russian Federation" program, which underscores the nation's commitment to integrating advanced technologies like artificial intelligence into various economic sectors, including finance.
Presidential Decree No. 83 of 2 March 2022, "On Measures to Ensure the Accelerated Development of the Information Technology Industry in the Russian Federation,"7 adopted a number of measures to support the technology industry in the Russian Federation.
Указ Президента Российской Федерации от 2 марта 2022 г. № 83 «О мерах по обеспечению ускоренного развития отрасли информационных технологий в Российской Федерации» // СПС «Кон-сультантПлюс» [Decree of the President of the Russian Federation No. 83 of 2 March 2022. On Measures to Ensure the Accelerated Development of the Information Technology Industry in the Russian
Federal Law No. 123 of 24 April 2020, "On Conducting an Experiment to Establish Special Regulation to Create the Necessary Conditions for the Development and Implementation of Artificial Intelligence Technologies in the Subject of the Russian Federation - the Federal City of Moscow and amending Articles 6 and 10 of the Federal Law'On Personal Data,'" established a special regime"to create the necessary conditions for the development and implementation of artificial intelligence technologies in the subject of the Russian Federation - the Federal City of Moscow, as well as the subsequent possible use of the results of the application of artificial intelligence."8
Furthermore, to implement the federal project "Artificial Intelligence," which is a part of the national program"Digital Economy of the Russian Federation," a subsidy of 2 billion rubles was allocated in 2022 for grants to support enterprises developing artificial intelligence technologies.
In addition, the Government of the Russian Federation adopted Resolution No. 309 of 9 March 2022, "On Establishing an Experimental Legal Regime in the Field of Digital Innovations and Approving the Program of an Experimental Legal Regime in the Field of Digital Innovations for the Operation of Highly Automated Vehicles." This resolution includes the use of artificial intelligence technologies for developing unmanned vehicles powered using artificial intelligence technologies.
Moreover, as part of the international forum "Ethics of Artificial Intelligence: the Beginning of Trust," which was held in 2021, Russia's largest technology companies adopted the Code of Ethics in the field of artificial intelligence.9 This Code was developed by the Artificial Intelligence Industry Alliance with the support of the Presidential Administration of the Russian Federation, the Analytical Center under the Government of the Russian Federation and the Ministry of Economic Development, platforms of the autonomous non-profit organization titled "Digital Economy," the Federation Council of the Federal Assembly of the Russian Federation, and the Public Chamber of the Russian Federation.
The Code establishes general ethical principles and standards of conduct that should guide participants in artificial intelligence relations in their activities, which
Federation, SPS "ConsultantPlus"] (June 10, 2024), available at https://www.consultant.ru/document/ cons_doc_LAW_410684/.
Федеральный закон от 24 апреля 2020 г. № 123-Ф3 «О проведении эксперимента по установлению специального регулирования в целях создания необходимых условий для разработки и внедрения технологий искусственного интеллекта в субъекте Российской Федерации - городе федерального значения Москве и внесении изменений в статьи 6 и 10 Федерального закона «О персональных данных»» // СПС «КонсультантПлюс» [Federal Law No. 123 of 24 April 2020. On Conducting an Experiment to Establish Special Regulation in Order to Create the Necessary Conditions for the Development and Implementation of Artificial Intelligence Technologies in the Subject of the Russian Federation - the Federal City of Moscow and Amendments to Articles 6 and 10 of the Federal Law "On Personal Data," SPS "ConsultantPlus"] (June 10, 2024), available at https://www. consultant.ru/document/cons_doc_LAW_351127/.
Кодекс этики в сфере искусственного интеллекта (2021) [Code of Ethics in the Field of Artificial Intelligence (2021)] (June 10, 2024), available at http://cdn.tass.ru/data/files/ru/kodeks-etiki-ii.pdf.
are currently not regulated by the Russian Federation's legislation of technical regulation acts.
Financial institutions in Russia are leveraging artificial intelligence to bolster their anti-money laundering and counter terrorism financing systems. Artificial intelligence systems today are particularly adept at identifying complex patterns and anomalies that traditional monitoring systems might miss, thus improving the accuracy and speed of detection and intervention.
However, the adoption of artificial intelligence in financial monitoring additionally poses legal and ethical challenges, particularly with regard to privacy and the potential for AI-driven decisions to impact client rights. Russian law is evolving to address those challenges, seeking to ensure that artificial intelligence establishes a balance between technological development and the protection of human rights. This entails making sure that artificial intelligence systems observe specific guidelines that govern a wide range of operations, from credit choices to trade surveillance.
In conclusion, the integration of artificial intelligence into the financial monitoring systems in Russia represents a paradigm shift in handling monetary safety. While it offers sizable improvements in efficiency and effectiveness, it also necessitates ongoing diversifications in legal frameworks to cope with the unconventional challenges that are brought about when using artificial intelligence technology.
2.2. Integration of Artificial Intelligence into Financial Monitoring in India, China, Brazil, South Africa, the United Arab Emirates, and Ethiopia
India, China, Brazil, South Africa, the United Arab Emirates, and Ethiopia are actively introducing artificial intelligence technologies into their financial systems to improve efficiency, accuracy, and regulatory compliance. These developments are changing the existing regulatory framework governing artificial intelligence issues.
2.2.1. India
India's approach to using artificial intelligence in financial monitoring is more centered on leveraging technology to enhance the performance of its anti-money laundering and combating the financing of terrorism frameworks. The Reserve Bank of India (RBI) has been proactive in its virtual transformation, encouraging banks to adopt new technologies, such as artificial intelligence, for better risk-management and compliance. Indian economic institutions are utilizing artificial intelligence to investigate large amounts of data for suspicious transaction patterns, which facilitates the early detection and prevention of fraud. According to a report by PwC, India's financial sector is rapidly adopting artificial intelligence to not only comply with regulatory requirements but also to gain competitive advantages by improving customer experience and operational efficiency.10
PricewaterhouseCoopers (PwC), PwCs Global Artificial Intelligence Study: Sizing the Prize (2017) (June 10, 2024), available at https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html.
The "National Strategy for Artificial Intelligence" or "#AIForAll" was published in June 2018 and focuses on five key sectors: healthcare, agriculture, education, smart cities, and infrastructure, as well as smart mobility and transport.11
Additionally, in 2024, India's artificial intelligence sector launched a bold undertaking geared toward strengthening the ecosystem of artificial intelligence innovation. This mission includes building an infrastructure for computing, supporting startups, and developing domestic tools for the safe and ethical use of artificial intelligence.12
2.2.2. China
China has made colossal strides in integrating artificial intelligence into its financial tracking systems, backed by both government projects and private sector innovation. The People's Bank of China has implemented artificial intelligence-driven projects aimed at improving the monitoring and analysis of financial transactions in order to prevent illegal sports along with money laundering, and fraud. China's synthetic intelligence improvements are supported by strong government backing, with rules that encourage the development of artificial intelligence and its software across distinct sectors, such as finance. This strategic emphasis is part of a broader plan to position China as a global leader in artificial intelligence technology by 2030.13
China's professional strategy in the area of artificial intelligence, published with the aid of the State Council of China, is entitled "A New Generation of Artificial Intelligence Development Plan." This plan, which was launched in 2017, includes the primary guidelines for the advancement of artificial intelligence until 2030.
The predominant goals of the strategy include the following:
• to attain a position of global leadership in artificial intelligence technologies and their applications by 2020;
• to emerge as a worldwide center of artificial intelligence innovation by the year 2025;
• to grow to be the world's leading nation in synthetic intelligence with the main emphasis of achieving monetary benefits and social improvements by the year 2030.
In addition, the strategy focuses on the significance of synthetic intelligence for national safety, financial development, and social stability. China is also actively
11 NITI Aayog, National Strategy for Artificial Intelligence (June 2018) (June 10, 2024), available at https:// www.niti.gov.in/sites/default/files/2023-03/National-Strategy-for-Artificial-Intelligence.pdf
12 Cabinet Approves Ambitious India AI Mission to Strengthen the AI Innovation Ecosystem, Prime Minister of India, 7 March 2024 (June 10, 2024), available at https://www.pmindia.gov.in/en/news_updates/ cabinet-approves-ambitious-indiaai-mission-to-strengthen-the-ai-innovation-ecosystem/.
13 New Generation of Artificial Intelligence Development Plan, State Council Document [2017] No. 35, Foundation for Law and International Affairs (2017) (June 10, 2024), available at https://flia.org/notice-state-council-issuing-new-generation-artificial-intelligence-development-plan/.
developing artificial intelligence infrastructure, which includes standardization, moral requirements, and research centers.
Artificial intelligence is used to reduce labor costs, predict risks, and improve regulatory efficiency in the financial markets. Lin Fei Duan highlights the significant results achieved by artificial intelligence in financial market applications, emphasizing the importance of rational and deeper studies to mitigate the risks associated with artificial intelligence use.14
Artificial intelligence plays a crucial role in financial risk management by providing innovative solutions for controlling and managing financial risks. Wanting Hu and YiXian Chen discuss the application of artificial intelligence in developing intelligent financial risk management systems, emphasizing the necessity of continuous innovation to enhance China's financial risk management.15
The development of technologies such as cloud computing, big data, blockchain, and artificial intelligence is deeply influencing the financial industry, driving it towards intelligent finance.
2.2.3. Brazil
In Brazil, comprehensive techniques to combine artificial intelligence into countrywide policies are being initiated. The primary focus is on fostering innovation while ensuring robust data security.16 Brazilian law on digital transformation emphasizes ethical artificial intelligence development and the protection of personal information.17
Brazil's strategy for the improvement of artificial intelligence, called "Estrategia Brasileira de Inteligencia Artificial" (EBIA), aims to promote the ethical and responsible use of artificial intelligence, stimulate research and innovation, and improve coordination between the public and private sectors.18 The foremost targets of the strategy include:
• Developing ethical standards for the responsible use of synthetic intelligence;
• Removing boundaries to innovation and encouraging investments in synthetic intelligence research and development;
• Improving cooperation among authorities, personal corporations, and research facilities;
Linfei Duan, Artificial Intelligence and its Applications in the Chinese Financial Market (2022).
Wanting Hu & Yixian Chen, Application of Artificial Intelligence in Financial Risk Management, in Artificial Intelligence & Security, Lecture Notes in Computer Science 180 (2022).
Luis C. Kubota & Mauricio B. Rosa, Artificial Intelligence in Brazil: Adoption, Scientific Production and Regulation (2024).
D.A. Gayan Nayanajith, Artificial Intelligence (AI) in Banking (2021).
Ministry of Science, Technology and Innovation, Estrategia Brasileira de Inteligencia Artificial (July 2021) (June 10, 2024), available at https://www.gov.br/mcti/pt-br/acompanhe-o-mcti/transforma-caodigital/arquivosinteligenciaartificial/ebia-documento_referencia_4-979_2021.pdf.
14
• Education and training of experts for employment in the synthetic intelligence field;
• International advertising of Brazilian synthetic intelligence technologies;
• The use of artificial intelligence in public services to improve their quality and efficiency."
In addition, Brazil has passed Bill No. 21/2020, which establishes the legal framework for the use of artificial intelligence in the country. This bill defines the principles, rights, and responsibilities related to artificial intelligence while emphasizing the importance of transparency, and security in the use of such technologies. The bill also encompasses risk management measures and data protection obligations.
Brazil's financial zone is increasingly turning to artificial intelligence to tackle the demanding situations of regulatory compliance and fraud detection. The Brazilian Central Bank has been supportive of artificial intelligence tasks, recognizing the potential of these technologies to transform monetary offerings. Artificial intelligence tools are now being used in Brazil to improve the effectiveness of regulatory reporting and to detect unconventional patterns that can suggest fraudulent activities. Brazilian banks, including Banco Bradesco, which is one of the largest financial institutions in Latin America, are at the forefront of adopting artificial intelligence technology to streamline operations and decorate safety features. The integration of artificial intelligence in Brazil is seen as an essential step in the direction of modernizing the banking sector and improving general economic stability.
2.2.4. South Africa
South Africa, while nevertheless still in the early stages of adopting artificial intelligence in monetary monitoring, is recognizing the potential blessings offered by this technology. The South African Reserve Bank (SARB) has expressed interest in exploring how artificial intelligence can be used to enhance the efficiency of its monetary surveillance and compliance systems. Artificial intelligence in South Africa is predicted to play a key role in detecting and preventing economic crimes, as well as in improving the velocity and accuracy of regulatory reporting. However, challenges such as data privacy concerns, a lack of skilled artificial intelligence professionals, and regulatory uncertainties are currently still being addressed in an effort to facilitate smoother integration of artificial intelligence technologies.20
South Africa's artificial intelligence strategy is being developed in collaboration with the Ministry of Communications and Digital Technology (DCDT). In April 2024,
19 Juliana Roman, Artificial Intelligence in Brazil: The Brazilian Strategy for Artificial Intelligence (BSAI/EBIA) and Bill No. 21/2020, IRIS-BH, 5 October 2021 (June 10, 2024), available at https://irisbh.com.br/en/ artificial-intelligence-in-brazil-the-brazilian-strategy-for-artificial-intelligence-bsai-ebia-and-bill-no-21-2020/.
Nihmal Marrie et al., South Africa and Artificial Intelligence, Boston Consulting Group, 28 September 2023 (June 10, 2024), available at
a dialogue paper was proposed at the National Artificial Intelligence Summit, which aims to facilitate discussions among the public and private sectors in order to promote artificial intelligence innovation and expand nationwide policies in this area.
The foremost components of the strategy encompass the following objectives:
1. The development of a national approach to stimulate economic growth in key sectors such as manufacturing, agriculture, navy capabilities, the transition to green energy, healthcare, and the automotive industry;
2. The creation of a regulatory approach to synthetic intelligence that takes into account international experience and at the same time is tailored to the South African context;
3. The consideration of ethical issues while using artificial intelligence to prevent dire consequences, including anti-competitive conduct, social dangers, and protection threats.
2.2.5. United Arab Emirates
The United Arab Emirates (UAE) has been proactive in setting up a comprehensive framework for artificial intelligence through various legal guidelines, strategies, and initiatives. One of its key components is the UAE Strategy for Artificial Intelligence, which aims to transform the UAE into an international leader in synthetic intelligence by 2031. This strategy is part of the wider UAE Centennial 2071 plan, which focuses on enhancing government performance, fostering synthetic intelligence investments, and developing excessive-fee monetary markets.
The UAE has also established the UAE Council for Artificial Intelligence, which oversees the integration of artificial intelligence in various government departments and the educational sector. This council promotes policies to create an artificial intelligence-friendly environment, encourages advanced research, and fosters collaboration between the private and non-private sectors. Additionally, the Artificial Intelligence and Advanced Technology Council in Abu Dhabi seeks to establish the emirate as a global hub for artificial intelligence investments, partnerships, and the advancement of artificial intelligence know-how.
The UAE's commitment to artificial intelligence is further demonstrated by a number of initiatives that have been specifically designed to achieve targeted goals. The UAE Artificial Intelligence Camp offers extensive training and workshops to develop local artificial intelligence talent, while the Artificial Intelligence and Blockchain Council proposes policies to support artificial intelligence adoption. Furthermore, the UAE has launched an artificial intelligence and coding license initiative to attract global artificial intelligence companies and coders, facilitating their establishment in the country and granting them opportunities such as the UAE Golden Visa.
Internationally, the UAE has formed partnerships with leading tech companies and institutions, notably those from the United States. These collaborations include
ventures with Microsoft, IBM, and OpenAI with the goal of advancing artificial intelligence research and applications.
Artificial intelligence is being increasingly integrated into the financial sector in the UAE, reflecting broader global trends. The primary applications and impacts of artificial intelligence include enhancing service delivery, improving operational efficiency, fraud detection, and financial inclusion. For instance, the study conducted by M. Sharma highlights the transformative effect of artificial intelligence-powered electronic devices and internet-based applications on banking, providing personalized services and improving customer experiences.21 Similarly, the research carried out by H. Mohamed discusses the application of artificial intelligence in Islamic asset and wealth management, emphasizing better predictability and consistency in financial services.22 W. Alhosani explores the role of financial intelligence units in anti-money laundering within the UAE, demonstrating the importance of artificial intelligence in analyzing suspicious activity reports and suspicious transaction reports.23 Furthermore, the study conducted by M. Ashfaq and U. Ayub provides insights into the knowledge and attitudes of financial professionals towards artificial intelligence, highlighting the significant business opportunities it presents despite concerns about ethics and security.24
In summary, the UAE has established a robust framework of laws, strategies, and initiatives to foster artificial intelligence development and integration across various sectors, positioning itself as a prominent leader in the global artificial intelligence landscape.
2.2.6. Ethiopia
Ethiopian financial establishments are increasingly integrating artificial intelligence to enhance their operations. One exemplary example is the Ethiopian Commercial Bank, which employs AI-powered customer service chatbots to assist clients with inquiries and transactions. This application of synthetic intelligence improves patron engagement and service provider performance.
In addition to the financial sector, the Ethiopian Artificial Intelligence Institute collaborates with numerous other sectors to put into effect synthetic intelligence solutions aimed at boosting productivity and protecting data. Their projects encompass artificial intelligence-pushed revenue guarantee structures in tax series
Manoj Sharma, A Study: How Artificial Intelligence Is Incorporated in the Middle East Banking, 2(3) J. for Res. in Applied Sci. & Biotechnology 202 (2023).
Hazik Mohamed, I-FinTech and its Value Proposition for Islamic Asset and Wealth Management, in Mohd M. Billah (ed.), Islamic FinTech: Insights and Solutions 249 (2021).
Waleed Alhosani, Financial Intelligence Units in the UK and UAE to Date, in Anti-Money Laundering: A Comparative and Critical Analysis of the UK and UAE's Financial Intelligence Units 17 (2016).
Muhammad Ashfaq & Usman Ayub, Knowledge, Attitude, and Perceptions of Financial Industry Employees Towards AI in the GCCRegion, in Artificial Intelligence in the Gulf 95 (2021).
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and fraud detection in monetary transactions. These implementations help economic establishments enhance their accuracy in monetary reporting and decrease risks related to monetary crimes.
All of these advancements are part of Ethiopia's broader strategy to harness virtual technology to transform the country into an inclusive, prosperous society, as mentioned in the "Digital Ethiopia 2025" strategy. This initiative's objectives are to create an environment that is conducive for synthetic intelligence adoption through a wide variety of sectors, including finance, healthcare, and governance.
Meanwhile, it is important to note that artificial intelligence applications are primarily seen in improving accounting information systems and enhancing the efficiency and productivity of financial institutions. For example, the research conducted by A. Abate highlights the low level, limited implementation of accounting information systems implementation among SMEs in the city of Bahir Dar, thus emphasizing the importance of government support, employee IT competence, and organizational readiness for successful adoption.25 Similarly, S. Yuvaraj and K.B. Assefa evaluate the design and implementation of accounting information systems in Ethiopian manufacturing industries, noting that financial report quality and asset control could be further improved by the implementation of effective accounting information systems, although performance evaluation remains a challenge.26 Additionally, the study by G.G. Gebremichael examines the efficiency and productivity of microfinance institutions, identifying inefficiencies compared to industry averages and suggesting that artificial intelligence could address these issues through improved HRM and cost management.27
Many other research specialists have also investigated internal audit practices and noted the importance of aligning with international standards to enhance audit quality, where artificial intelligence could play a pivotal role. The integration of artificial intelligence in the financial sector in Ethiopia is currently still in its nascent stages; however, it shows significant potential for transformation.
3. Challenges in the Legal Implementation of Artificial Intelligence
The integration of artificial intelligence into diverse sectors, ranging from law, healthcare, and finance, has been transformative, offering numerous advantages such as accelerated efficiency, improved accuracy, and value savings. Nevertheless,
Abera Abate, Implementation of Accounting Information Systems in Ethiopia: Evidence from Small and Medium Enterprises in Bahir Dar City, 6(6) Sci. J. Bus. Mgmt. 107 (2019).
Sambasivam Yuvaraj & Kibret B. Assefa, Evaluating the Design of Accounting Information System and its Implementation in Ethiopian Manufacturing Industries, 2(7) TIJ's Res. J. Sci. & IT Mgmt. 16 (2013).
Giday G. Gebremichael, Efficiency and Productivity of Microfinance Institutions of Ethiopia: A Case Study on Specialized Financial and Promotional Institution (SFPI), 32 J. Poverty Inv. & Dev. 83 (2017).
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the implementation of artificial intelligence still presents sizable legal challenges that should be resolved in order to ensure that these technologies are used ethically and effectively. This study also explored some of the main challenges associated with the implementation of artificial intelligence, specializing in troubles related to liability, data privacy, and regulatory frameworks.
One of the most pressing legal challenges in artificial intelligence implementation is determining liability. Artificial intelligence systems, especially those that are self-sustaining, can make decisions without human intervention, elevating questions about who's responsible when those selections result in harm or loss. The conventional criminal frameworks, which normally hold humans or organizations liable for their actions, conflict when it comes to accommodating situations in which artificial intelligence systems function independently.
According to scholars, the current legal systems need to evolve in order to be able to address the unique challenges posed by artificial intelligence. This may involve developing new legal categories or doctrines that are specifically designed to address artificial intelligence liability.28
Another major challenge is the privacy of records, i.e. the protection of personal and confidential data. Artificial intelligence structures, especially the ones based on device learning, require large amounts of statistics to function effectively. Considering that this fact often consists of sensitive non-public information, artificial intelligence groups have voiced concerns about the adequate protection of sensitive data and statistics. The General Data Protection Regulation (GDPR) within the European Union specifies strict guidelines for the collection and processing of personal data with the goal of safeguarding personal privacy. However, compliance with such regulations can also be challenging for artificial intelligence systems that continuously collect and analyze massive amounts of data.29 Moreover, the dynamic nature of artificial intelligence, which evolves by means of acquiring knowledge from new data, further complicates the undertaking of ensuring ongoing compliance with privacy legal guidelines.
The existing regulatory frameworks governing artificial intelligence are still in their nascent stages of development, which adds an additional layer of complexity to its legal implementation. Different countries are at varying stages of developing and enforcing synthetic intelligence guidelines, resulting in a fragmented worldwide landscape of legal regulation. For instance, the European Union has proposed comprehensive artificial intelligence regulations that emphasize ethical use and accountability, while other regions, such as the United States, have adopted a more
28 Sabine Gless et al., If Robots Cause Harm, Who Is to Blame? Self-Driving Cars and Criminal Liability, 19(3)
New C. L. Rev. 412 (2016).
Paul Voigt & A Guide (2017).
29 Paul Voigt & Axel von dem Bussche, The EU General Data Protection Regulation (GDPR): A Practical
sector-specific approach, focusing on guidelines rather than binding regulations.30 Such disparities create challenges for multinational companies that need to navigate different regulatory environments.
Ethical concerns also play a vital role within the legal implementation of artificial intelligence. Issues that involve bias and equity are distinguished. Synthetic intelligence systems can inadvertently perpetuate or even exacerbate current biases present in the training data of an AI model, leading to unfair results. For example, facial reputation systems have been proven to showcase racial and gender biases, which could result in discriminatory practices. Addressing these ethical issues calls for sturdy criminal frameworks that ensure transparency, accountability, and fairness in artificial intelligence applications.
The challenge of comprehending ability, or the "black box" problem, is another critical issue. Artificial intelligence systems, especially deep learning models, can be highly complex and opaque, making it difficult to understand how these systems arrive at certain decisions. This lack of transparency poses challenges in terms of accountability and trust. Legal frameworks need to mandate the comprehension of the complex concept of ability in artificial intelligence systems, thereby ensuring that the decision-making processes are transparent and understandable to humans.31
3.1. Technological Limitations and Ethical Considerations
The primary technological limitations and ethical considerations associated with the implementation of artificial intelligence focus on issues such as data dependency, bias, transparency, and accountability.
One of the most common technological limitations of artificial intelligence is its dependency on data quality and quantity. Artificial intelligence systems require large datasets to function effectively, and the quality of these datasets directly impacts the performance of the artificial intelligence models. Inaccurate or biased data can lead to flawed decision-making processes, which can have significant consequences, particularly in critical areas such as law enforcement and healthcare. A number of authors have highlighted the fact that biases present in training data can result in artificial intelligence systems that perpetuate or even exacerbate existing societal biases, leading to unfair outcomes.32
Another technological challenge is the complexity and opacity of artificial intelligence models, often referred to as the "black box" problem. Many artificial intelligence systems, particularly those based on deep learning, operate in ways that
30 Reuben Binns, Fairness in Machine Learning: Lessons from Political Philosophy, in Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency 149 (2018).
31 Finale Doshi-Velez & Been Kim, Towards a Rigorous Science of Interpretable Machine Learning (2017) (June 10, 2024), available at https://arxiv.org/abs/1702.08608.
32 Solon Barocas & Andrew D. Selbst, Big Data's Disparate Impact, 104 Cal. L. Rev. 671 (2016) (June 10, 2024), also available at https://ssrn.com/abstract=2477899.
are not easily interpretable by humans. This lack of transparency makes it difficult to understand how artificial intelligence systems arrive at their decisions, posing significant challenges for accountability and trust. Several authors have emphasized the need for developing interpretable artificial intelligence models that can provide explanations for their decisions, which they consider crucial for ensuring that these systems are used ethically and responsibly.33
The issue of bias is not only a technological limitation but also a profound ethical consideration. When artificial intelligence systems are trained on data, they can inherit and amplify biases that may be present in the training data, which can lead to discriminatory outcomes. This is particularly concerning in applications such as hiring, loan approval, and criminal justice, where biased artificial intelligence systems can have a significant impact on individuals' lives. For instance, scientists J. Buolamwini and T. Gebru conducted a study on commercial artificial intelligence systems and found significant disparities in the accuracy of gender classification across different demographic groups, highlighting the urgent need to address bias in artificial intelligence.34
Data security is another critical ethical consideration in the implementation of artificial intelligence. Artificial intelligence systems often require access to large amounts of personal data, which has raised concerns about how this data is collected, stored, and used. The General Data Protection Regulation (GDPR) in the European Union sets stringent requirements for data protection, emphasizing the importance of obtaining informed consent and ensuring data security. Some scientists have, however, debated the challenges of ensuring compliance with these regulations, particularly given the dynamic nature of artificial intelligence systems that are continuously learning and evolving.35
The ethical implications of artificial intelligence extend beyond data privacy and bias. The potential for artificial intelligence to replace human workers raises concerns about job displacement and economic inequality. As artificial intelligence systems become more capable of performing tasks traditionally done by humans, there is a growing risk that many jobs will become obsolete, which could lead to significant economic and social disruptions. Some scientists, such as Brynjolfsson and McAfee, have discussed the impact of artificial intelligence on the future of work, highlighting the need for policies that would provide support to workers displaced by automation.36
Doshi-Velez & Kim, supra note 31.
Joy Buolamwini & Timnit Gebru, Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification, in Proceedings of the 1st Conference on Fairness, Accountability, and Transparency 77 (2018) (June 10, 2024), also available at https://www.media.mit.edu/publications/gender-shades-intersectional-accuracy-disparities-in-commercial-gender-classification/.
Voigt & Bussche 2017.
Erik Brynjolfsson & Andrew McAfee, The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies (2017) (June 10, 2024), also available at https://ilp.mit.edu/node/25076.
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Ensuring accountability in artificial intelligence systems is another major ethical challenge. When artificial intelligence systems make decisions that result in harm or loss, it can be difficult to determine who is responsible. Traditional legal frameworks, which typically hold humans or corporations accountable for their actions, may not be well-suited to address scenarios where artificial intelligence systems operate independently. Scientists Gless, Richter, and Wahl argue that the legal system must evolve to accommodate the unique challenges posed by artificial intelligence, potentially by developing new legal categories or doctrines that specifically address artificial intelligence liability.37
In conclusion, while artificial intelligence offers significant benefits, its implementation presents substantial technological limitations and ethical challenges. These include dependency on data quality, lack of transparency, data privacy concerns, job displacement, and accountability issues. Addressing these challenges requires comprehensive and adaptive legal frameworks that can keep pace with rapid advancements in artificial intelligence technology. Such frameworks can ensure that artificial intelligence systems are used responsibly and ethically while at the same time also maintain a balance between innovation and protection. By doing so, society can harness the full potential of artificial intelligence while safeguarding against IT-risks.
3.2. Regulatory Harmonization
As artificial intelligence integrates more deeply into sectors such as healthcare, finance, and transportation, there is a growing need for harmonized legislation that not only protects individuals but also fosters innovation.
In the context of such a dynamic development of technologies, which is confirmed by both state programs and many scientists, for example, P.H. Winston,38 legislators need to determine the risks of introducing such technologies and consider potential problems that may consequently arise, such as ethical, social, and legal problems. As a result, special attention must be paid to the issues of the legal regulation of such processes.
The fact that artificial intelligence technologies transcend national borders results in a mosaic of regulatory standards that can be confusing for companies and potentially stifling for innovation. Different countries have varying degrees of privacy laws, data protection standards, and ethical guidelines, which can create significant barriers to the deployment of artificial intelligence systems on a global scale. Harmonization of the regulatory laws would aim to minimize all of these discrepancies, facilitating the smoother integration of artificial intelligence technologies worldwide.
37 Gless et al. 2016.
38 Patrick H. Winston & Richard H. Brown, Artificial Intelligence: An MIT Perspective 24 (1980).
One of the primary challenges in harmonizing artificial intelligence legislation is the diversity of legal systems and cultural norms. Different countries view ethics and morality differently. In other words, what is considered ethical or acceptable in one country might be viewed quite differently in another. For example, the European Union's General Data Protection Regulation (GDPR) places strict limits on data usage that are not present in other jurisdictions. Reconciling these differences requires careful negotiation and compromise.
Furthermore, artificial intelligence technology evolves at a breakneck pace, making it difficult for legislation to keep up. Laws drafted today may become obsolete tomorrow as new artificial intelligence applications emerge. This dynamic poses another unique challenge: legislating in a way that is both specific enough to be effective and flexible enough to remain relevant.
Another significant challenge is balancing the need to protect consumers and the public with the need to encourage technological and business innovation. Overly stringent regulations might hamper the growth of artificial intelligence startups and stifle innovation, while too lax a regulatory framework could lead to unethical use of artificial intelligence technologies.
Even so, given the global nature of artificial intelligence, there is a strong push for international cooperation in formulating artificial intelligence legislation. Bodies like the European Commission and the United Nations have started to play more active roles in fostering dialogue among nations. These discussions are crucial in setting international standards that can serve as guidelines for national legislation.
To address the rapid evolution of artificial intelligence, some legal experts propose the idea of adaptive legislation, which includes mechanisms for regular updates and revisions based on technological advancements. Additionally, enacting proactive legislation that anticipates future developments could help manage the transition as artificial intelligence becomes more pervasive.
Furthermore, incorporating a wide range of perspectives, such as the expert opinions of artificial intelligence researchers and developers as well as those of the general public and ethicists, can also help ensure that legislation is both comprehensive and practicable. Involving these stakeholders in the legislative process could go a long way towards understanding the practical implications of laws and gaining broader acceptance.
The necessity of legal regulation of artificial intelligence in BRICS Plus countries is driven by several key factors, including the need to manage social, economic, and legal challenges posed by artificial intelligence technologies. Artificial intelligence systems, while offering significant benefits such as solving complex societal issues and improving efficiency, also introduce legal complexities that require clear regulatory frameworks to ensure accountability and protect human rights. For instance, B.A. Shakhnazarov highlights the importance of harmonizing acts like the Model Convention on Robotics and AI to formulate unified approaches for regulating
AI, which would include issues such as legal personality and responsibility.39 Similarly, I.A. Zyryanov argues for constitutional and legal regulations that treat AI as an object rather than a quasi-subject of law, emphasizing state licensing, activity restrictions, and liability frameworks to protect social rights.40
The task of harmonizing artificial intelligence legislation is a complex yet crucial endeavor. As this technology advances, the legal frameworks need to evolve accordingly to address the emerging challenges and harness the full potential of artificial intelligence. The path forward involves a delicate balance of protecting individual rights and promoting technological progress, which requires a concerted effort from policymakers, technologists, and society at large. Through international collaboration and innovative legislative practices, it is possible to develop laws that are both effective and adaptable to the changing landscape of artificial intelligence.
Conclusion
The integration of artificial intelligence into financial systems across the BRICS Plus countries, especially with regard to combating anti-money laundering and counter-terrorism financing regimes, signifies a transformative step in the direction of more effective, robust monetary tracking systems.
Artificial intelligence's ability to analyze enormous amounts of data in real-time has proven crucial in identifying suspicious transactions that human oversight may omit. This technological development not only effectively enhances the performance and effectiveness of AML and CTF operations, but it also aligns with global requirements and practices that incorporate artificial intelligence technologies to safeguard economic systems against illicit activities. However, as those countries forge ahead, the need for a harmonized criminal framework will become more apparent. Such a framework might need to ensure that artificial intelligence integration no longer simply adheres to worldwide norms but also respects people's rights and privacy, addressing moral issues such as information bias and transparency.
Furthermore, the journey of the BRICS Plus group of countries in integrating artificial intelligence into their financial tracking systems highlights the balance between innovation and regulation. As these countries continue to enhance and refine their artificial intelligence abilities, the collaboration among criminal scholars, technologists, and policymakers might be crucial. This collaborative effort is critical to creating adaptive criminal systems that are capable of handling the dynamic nature of artificial intelligence technology while at the same time safeguarding the monetary system against both present-day and future threats.
39 Benjamin A. Shakhnazarov, Legal Regulation of Relations Using Artificial Intelligence, 17(9) Actual Probs. of Rus. L. 63 (2022).
40 Igor A. Zyryanov, Problems of Constitutional and Legal Regulation of Artificial Intelligence 531 (2020).
At the same time, despite the complex regulatory challenges that the field of synthetic intelligence is currently confronting, the proactive stance of the BRICS Plus nations in the direction of artificial intelligence integration in monetary monitoring guarantees a future in which monetary protection is notably superior as a result of advanced technology.
There is also a growing awareness of the challenges associated with regulating artificial intelligence. In the future, ensuring the stability of the financial system, public administration, and the monetary system of the nation will become the primary goals in the application of artificial intelligence. One of the most crucial regimes in the area of national security, which has undergone significant improvements as a result of technological changes, is the regime for countering cash laundering and terrorism financing.
Countering money laundering and terrorism financing is one of the most critical responsibilities of the countries today. This requires extensive costs and the adoption of a prevention-oriented approach in its response. Furthermore, there is a strong need for a timely response to the risks faced by national bodies tasked with enforcing state policies against money laundering and terrorism financing, thereby protecting the state's economic system.
It is also important to comprehend that, in tandem with the advancement of state systems, the use of artificial intelligence in the realm of cybersecurity crimes as well as crimes involving other new technology, including artificial intelligence and blockchain, is also growing. The range of cutting-edge monetary tools and technology being used for illegal purposes is increasing.
Tracking complicated and multi-stage transactions in real-time poses an impossibly challenging task for government employees of analytical departments that monitor the movement of finances, making it exceedingly difficult to reveal and detect fraudulent schemes only by using traditional procedures.
Money laundering and terrorism financing pose a significant threat to economic establishments around the world, and with the introduction of technology such as artificial intelligence, the opportunities for unlawful activities are increasing, as are the devastating consequences of such crimes.
For this reason, it has become extremely necessary to continuously enhance the strategies and methods of conducting financial tracking of transactions and expand the technological competencies of monitoring systems in present day realities.
Even at the current rate, money laundering and terrorism financing are among the main destabilizers of countries' economies today and pose a global threat to monetary institutions worldwide. Ensuring financial safety is an essential element of the BRICS countries' monetary operations. As a result, using a risk-based approach in implementing mechanisms to defend a country's economic structure becomes a concerning way to mitigate and prevent potential problems.
The fundamental tool for countering money laundering and terrorism financing is monetary tracking. And financial intelligence devices play a crucial role in facilitating monetary tracking across nations.
In order to be able to effectively execute their functions, financial intelligence systems must be able to obtain the records from accountable agencies required for the effective overall performance of their capabilities as well as possess their own, albeit limited access, to financial and law enforcement records.
The function of figuring out new schemes for cash laundering and financing terrorism falls on analysts, i.e. personnel of departments that work closely with software merchandise and technologies such as artificial intelligence.
Financial intelligence devices use a variety of equipment to conduct behavior evaluations and investigations. Some of this equipment comprises off-the-shelf products that are tailored to meet the needs of specialty departments and financial investigations, a few are developed specifically for financial intelligence devices, and others have been developed on request directly by the departments themselves.
Analysts in financial intelligence units use a huge amount of information in their work, accrued over several years of monitoring. This is necessary for schematizing transaction flows and relationships among transactions and objects.
Every year, the number of records increases, and new registries and databases are created. It is imperative that monetary institutions, regulators, and regulation enforcement agencies seek to work together, using new technology to analyze and share intelligence. In this regard, artificial intelligence can significantly simplify and enhance the quality of incoming information.
The widespread use of artificial intelligence is resulting in major adjustments to the current structure of the global economic marketplace. In addition to financial intelligence devices, banks are now also actively developing artificial intelligence to cope with challenges in the fight against cash laundering and terrorism financing.
The use of artificial intelligence in analytics has vastly improved the evaluation and detection of connections, as well as boosted the capabilities of government personnel, law enforcement corporations, and banks.
In the coming years, the work of an analyst will be reduced to monitoring the results of the activities carried out by artificial intelligence systems, which can be capable of carrying out complex analytics, data processing, statistical evaluation, and a variety of tasks that would typically be performed by several human employees and transactions. This will notably change the function of an analyst.
The regime for countering cash laundering and terrorism financing is undergoing a period of transition. Instead of relying solely on entities (such as country authorities) and individuals (such as government officers), the new regime will incorporate artificial intelligence, and this trend will continue.
If the development of artificial intelligence continues at the same rapid pace, then new bodies with embedded artificial intelligence may appear in the country, taking
over many of the functions currently performed by employees. Artificial intelligence has the potential to replace the standard capabilities of government organizations and, in the future, also enhance several aspects of their operations.
In this context, the increasing integration of artificial intelligence technologies into the financial structures of the BRICS Plus countries offers both opportunities and challenges in combating economic crimes, which include money laundering and terrorism financing.
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Information about the author
Marina Aksenova (Moscow, Russian Federation) - Senior Lecturer, Department of Financial Monitoring, RUDN University (6 Miklukho-Maklaya St., Moscow, 117198, Russian Federation; e-mail: [email protected]).
