The Experience of Using Digital Technologies in the Financial Sector in Order to Ensure Economic Security in Russia and Abroad

Afanasyeva Lyubov Viktorovna ORCID: 0000-0003-2880-8872 PhD in Economics Associate Professor, Department of Economic Security and Taxation, Southwest State University 305040, Russia, Kursk region, Kursk, ul. 50 Let Oktyabrya, 94 lv_af@mail.ru

Evloeva Alina Borisovna Student, Department of Economic Security and Taxation, Southwest State University. 305040, Russia, Kursk region, Kursk, ul. 50 Let Oktyabrya, 94 alinkursk@gmail.com Other publications by this author

Introduction The rapid development of artificial intelligence technologies has led to their wider use in various fields, including finance.

Artificial intelligence-based solutions are used in various areas of finance, such as risk management, fraud detection, customer service and investment analysis. In this article we will look at the current state and potential of the use of artificial intelligence in the financial sector, the advantages and disadvantages of using such technologies, as well as the latest trends and developments in this area. We will also consider the role of artificial intelligence in order to ensure economic security and the possibility of its use in tax administration.

Analysis of the current state and potential of digital technologies in the financial sector in Russia and in the worldAccording to a report by McKinsey & Company, artificial intelligence technologies can potentially bring from $1.0 trillion to $1.5 trillion in annual economic profit in the global banking industry alone ^[1].

Moreover, a study conducted by PwC showed that 77% of financial institutions believe that artificial intelligence will have a significant impact on their business over the next three years ^[2].

In the field of risk management, artificial intelligence algorithms can analyze huge amounts of data to identify potential risks and predict future trends. This can help banks and other financial institutions manage their portfolios more effectively and minimize their exposure to potential losses.

N.E. Zhiyanova and S.H. Mavlonov believe that in recent years the topic of digitalization of the financial environment and the use of artificial intelligence in solving important tasks has become especially relevant. However, developing countries may be less prepared to adopt the latest technologies, which may limit the impact of digitalization on these States.

Scientists identify 3 areas in which digital technologies and artificial intelligence have the greatest potential for use: personal finance planning; fraud detection and anti-money laundering; automation of processes, including customer transactions. The use of artificial intelligence makes it possible to obtain more individual and comprehensive solutions that can stimulate consumer activity (for example, direct the saved funds into investments) and adapt to the needs of customers ^[3].

However, A.A. Nikonov and E.V. Stelmashonok in their research say that financial technologies do not replace traditional banking models, but rather fills in certain gaps. Moreover, the banks themselves are actively introducing innovative technologies, such as Internet banking, which indicates that a complete transformation of the financial industry has not yet occurred. In the future, the most common business model will probably be the integration of traditional and artificial intelligence-oriented approaches in activities. Traditional banks will retain their importance, and digital structures will also have their place in the industry ^[4].

In all financial services sectors - capital markets, investment banking, retail banking - more than 75% of companies use at least one of the main types of high-performance computing, machine learning and deep learning.

According to Nvidia's research “State of AI in financial services. 2022 Trends” the most used type of artificial intelligence among financial sector companies is machine learning (Figure 1).

Figure 1 - The most used types of artificial intelligence among financial sector companies ^[5]

The data in Figure 1 indicate that about 80% of companies and organizations in the financial sector use some kind of artificial intelligence in their work. The most popular technologies are machine learning, that is, 58% of companies on the market use various process automation programs, image recognition, voice or text. High-performance computing (combining and increasing computer power) and deep learning (a less standardized area of neural network training without the use of specific algorithms) are less common than machine learning, but also occupy a share of more than 50%. In general, it is noticeable that artificial intelligence and neural network technologies are widespread among financial sector organizations, and this trend is sure to continue.

Another area where artificial intelligence has a significant impact is fraud detection. By analyzing large amounts of data in real time, artificial intelligence systems can detect suspicious patterns and anomalies that may indicate fraudulent activities. This can help banks prevent financial crimes and protect their clients' assets.

In the field of customer service, chatbots and virtual assistants based on artificial intelligence can provide support around the clock, responding to customer requests and solving problems without human intervention. This can help financial institutions reduce costs and increase customer satisfaction.

As for trade and investment analysis, artificial intelligence algorithms can analyze huge amounts of financial data and identify trends and patterns that may be overlooked by analysts. This can help investors make more informed decisions and increase their profitability ^[5].

Table 1 shows data on organizations using special software tools and using various types of digital technologies ^[6].

Table 1 - Distribution of organizations' costs for the introduction and use of digital technologies by type (as a percentage of the total)

Source: compiled by the author on the basis of Rosstat data (https://rosstat.gov.ru/statistics/science )

The data in Table 1 indicate that in 2020 there was a significant decrease in the use of special digital programs in business activities by organizations. Thus, the overall growth rate decreased to 76.14% from 2020, and for some types of special computer programs, you can notice a decrease of more than 2 times (automated production management programs). Such indicators may be the result of an economic downturn and a decline in production and trade in 2020 due to the COVID-19 pandemic and the subsequent economic crisis, which resulted in the closure and bankruptcy of many enterprises and organizations.  Thus, the overall reduction in the costs of companies, as a result of a drop in their income, led to negative growth rates in 2020. However, with the recovery of the economy and the normalization of the market situation in 2021, the distribution of organizations' costs for the introduction and use of digital technologies has increased in most indicators, or decreased slightly in some types of use. This indicates that companies and organizations are interested in the development and implementation of digital technologies in their activities, therefore, they are increasing their expenses in this area.

In Russia, digital technologies and artificial intelligence are also becoming increasingly important tools in the financial industry. According to the Accenture report, Russian banks are implementing artificial intelligence technologies at a faster pace than their global counterparts. The report shows that 82% of Russian banks already use or plan to use artificial intelligence in their operations, compared with the global average of 77% ^[7].

It is also worth noting that the introduction of ICT in Russian organizations is not only related to their internal needs, but is also part of the state strategy for the development of the digital economy, the main goal of which is to create conditions for the formation of a knowledge-based society ^[8]. To achieve this goal, it is necessary to make extensive use of modern information technologies in various fields. Thus, the use of ICT in Russian organizations is an integral part of the information society development strategy and ensures the competitiveness of the Russian economy on a global scale. Table 2 shows the shares of organizations' costs for the introduction and use of various digital technologies for the period from 2019 to 2021.

Table 2 - Distribution of organizations' costs for the introduction and use of digital technologies by type (as a percentage of the total)

Source: compiled by the author on the basis of Rosstat data (https://rosstat.gov.ru/statistics/science )

Based on the statistics given in Table 2, it can be concluded how and in what proportions the costs of enterprises and organizations for digital technologies and their implementation are distributed. Thus, internal costs are the largest item of expenditure and in 2019 account for 78.9 percent of all costs, but in 2020 and 2021 their share decreased, and the share of external costs for the introduction and use of digital technologies on the contrary increased. The costs of products and services in the field of information security decreased from 5.8% to 5.3% for the period from 2019 to 2021. This may indicate that organizations are directly most interested in using their own funds for the introduction and use of digital technologies. However, lower costs for information security products and services may also indicate the impact of the COVID-19 pandemic crisis.

In Russia, the use of artificial intelligence is also supported by the government's strategic plan for the development of digital technologies in the country. The Digital Economy program, launched in 2017, includes an emphasis on the development of artificial intelligence and other advanced technologies in order to increase productivity, competitiveness and innovation in various sectors of the economy.

The program includes initiatives to support the development of startups in the field of artificial intelligence and research projects, as well as measures to improve the regulatory framework in the field of artificial intelligence in the country. For example, the Ministry of Communications and Mass Media has created a working group on AI, which is responsible for the development of regulations and guidelines on the responsible use of AI in various industries, including finance.

The Central Bank of Russia has launched a project called RegTech, aimed at studying the use of artificial intelligence to comply with regulatory requirements. The project is aimed at developing artificial intelligence-based solutions that can automate regulatory reporting and improve the accuracy and timeliness of compliance processes ^[9].

In addition, several Russian banks have launched their own initiatives in the field of artificial intelligence. Sberbank, one of the largest banks in the country, has created a special artificial intelligence unit called SberAI, which is responsible for developing artificial intelligence solutions for various areas of the bank's activities. The bank has also launched a digital assistant called SberPortal, which uses natural language processing and machine learning algorithms to provide customers with personalized financial advice.

Another example is Alfa-Bank, which has developed a virtual assistant based on artificial intelligence called Alfa. The assistant can help clients with various tasks, such as checking their account balances, transferring funds and making payments, through interaction in natural language ^[10].

Table 3 - Dynamics of investments by financial sector companies in the development of various scenarios for the use of artificial intelligence.

Source: compiled by the author on the basis of Nvidia research “State of AI in financial services. 2022 Trends”

Based on the data in Table 3, we can conclude that investments in all scenarios for the use of artificial intelligence in the financial sector showed a noticeable increase in 2022 compared to 2021. The largest amount of investment in 2022 goes into the development of AI applications in the field of fraud detection, conversational artificial intelligence and trading algorithms. Also, the largest increase in investments over 1 year was shown by compliance scenarios, conversational AI and portfolio optimization. In the future, priority areas of investment policy in the field of innovation are: building technological potential and creating high-tech developments; using scientific and technological potential to create new technologies and innovative products ^[11].

Problems and prospects for the development of digital technologies in the financial sector in order to ensure economic security

Despite the growing adoption of artificial intelligence in the financial industry, there are also concerns about privacy and data security. Financial institutions process confidential customer data, and the use of artificial intelligence raises questions about how this data is collected, stored and used. There are also concerns about potential cyber attacks on systems using artificial intelligence, which could have significant consequences for financial institutions and their customers.

To solve these problems, financial institutions in Russia and around the world are investing in cybersecurity measures and developing artificial intelligence solutions that prioritize privacy and data security. This includes the use of encryption, secure data storage and the development of artificial intelligence algorithms that are resistant to cyber attacks and other threats.

Another problem associated with the use of artificial intelligence in finance is the lack of interpretability or explainability of some models of artificial intelligence. Some artificial intelligence models, such as deep learning neural networks, can produce accurate results, but they are difficult to interpret or explain how they came to their conclusions. This is a concern in the financial industry, where there is a need for transparency and accountability in decision-making ^[12].

To solve this problem, researchers and developers are working to develop new methods of explicable artificial intelligence (XAI) that can provide greater transparency and interpretability of artificial intelligence models. For example, XAI methods such as LIME (model-independent local interpreted explanations) and SHAP (Shapley additive explanations) can provide insight into how artificial intelligence models arrive at their conclusions, even for complex models such as deep learning neural networks.

In Russia, the development of XAI methods is also in the focus of research and development. The Russian Association of Artificial Intelligence (RAAI) has created a working group on XAI, which is responsible for promoting research and development in this area ^[13].

Moreover, the use of artificial intelligence in the financial industry also creates new employment opportunities and changes the skills needed by financial professionals. Since artificial intelligence solutions automate routine tasks, financial professionals need to develop new skills, such as data analysis, programming and working with artificial intelligence technologies. This offers new areas for data processing specialists, artificial intelligence developers and other specialists with experience in advanced technologies.

Russian tax authorities are also actively implementing digital technologies to automate and optimize processes related to tax accounting and control over tax payments.

One of the main directions of using digital technologies is the development of electronic services. For example, in 2018, a unified electronic document management system (EDMS) was launched in Russia, which allows accounting and reporting in electronic form. In addition, the tax authorities are actively implementing an online sales register system that allows you to automatically transmit sales data to the tax service. The key advantages of using digital technologies in the work of tax authorities is the ability to automate the processes of tax reporting analysis. For example, the use of machine learning algorithms allows you to quickly process large amounts of data and identify potential violations of tax legislation.

In addition, digital technologies allow tax authorities to interact more effectively with taxpayers. In 2021, the tax chatbot system was launched in Russia, which allows taxpayers to ask questions and receive consultations online. Also, tax authorities actively use social networks to inform taxpayers about new rules and changes in tax legislation ^[14].

Also, many European Union countries also use artificial intelligence in their tax systems. For example, in the Netherlands, the tax authority uses artificial intelligence to analyze large amounts of data and identify potential tax fraud. The system can also help identify trends and patterns of tax evasion, allowing tax authorities to take proactive measures to prevent tax fraud.

In France, the tax authority uses artificial intelligence to improve the efficiency and accuracy of tax audits. The system uses machine learning algorithms to analyze financial data and identify potential tax violations. This allows tax auditors to focus their efforts on high-risk cases and reduce the burden on bona fide taxpayers. Similarly, in the UK, Tax and Customs (HMRC) uses artificial intelligence to automate everyday tasks such as data entry and processing. This helped to optimize tax administration processes and reduce the time and resources needed to comply with tax legislation ^[15].

The modern world requires organizations to scale applications with artificial intelligence support. To build a company based on artificial intelligence, management must invest in information and communication technologies that will allow data processing specialists, product managers to implement management's strategy in the field of artificial intelligence. Successful implementation of the artificial intelligence strategy will allow companies to achieve higher revenues, lower operating costs, greater customer satisfaction and overall competitive advantage in the industry.

Conclusions:

Digital technologies have become an integral part of the financial sector, they provide faster, more convenient and secure access to financial services. The development of digital technologies in the financial sector leads to the creation of new services that help to increase the level of economic security. For example, blockchain technologies allow you to create secure and reliable systems for storing and transmitting data, which is important for protecting financial assets and preventing fraud. The development of large-scale measures to ensure economic security in a digital society contributes to the purposeful stimulation of economic growth ^[16].

Another example is the use of artificial intelligence and data analytics to identify risks and make decisions in real time. This allows you to quickly respond to changes in the economic situation and take measures to protect financial assets. Digital technologies are also used to create financial management systems that help control expenses and income, predict future financial flows and make development plans. This helps to reduce risks and ensure stability in the financial sector. Finally, digital technologies are also used to increase the transparency of financial activities, which is important to ensure confidence in financial institutions. This is achieved through the use of open financial management systems and public reporting.

Thus, the development and use of digital technologies in the financial sector is an important tool for ensuring economic security. They allow you to create safer and more reliable financial management systems, as well as increase transparency and efficiency of financial transactions. The development of digital technologies in the financial sector has significantly improved the conditions for ensuring economic security.