Ecosystems as an Innovative Tool for the Development of the Financial Sector in the Digital Economy

Abstract

The purpose of this article is to analyze the trends in the development of the financial sector, as well as the digital technologies used in this area, to identify the fundamental drivers for improving the ecosystem of the financial sector of the economy. Achieving sustainable business growth is one of the urgent tasks of management, both at the level of individual enterprises and organizations and the national economic system as a whole. This issue is of the highest relevance in the context of the high dynamism of the external environment and the growing level of uncertainty. When writing the article, the following research methods were used: trend analysis, visual graphical analysis, descriptive statistics, correlation-regression analysis, and cross-tabulation. Based on the results of the analysis, it can be concluded that the following indicators have the greatest impact on the ecosystem of the financial sector: the share of financial organizations that had special software for managing the procurement of services; the share of financial organizations that had special software for managing the sales of services. With regards to the Russian financial sector, there is a weakness in the development of the ecosystem, which is partly due to the insufficient use of complex digital solutions in managing financial flows, for example, the use of ERP systems (enterprise resource planning), CRM systems (customer relationship management), and SCM systems (supply chain management). We believe that the conclusions and results presented in this article can be used as methodological tools for developing strategies for improving the ecosystem of the financial sector in the context of the transition to a digital economy.

1. Introduction

Achieving sustainable business growth is one of the urgent tasks of management, both at the level of individual enterprises and organizations and the national economic system as a whole. This issue is of the highest relevance in the context of the high dynamism of the external environment and the growing level of uncertainty. When determining the directions of strategic development, the focus of modern business entities is increasingly shifting towards the search for new tools and ways of doing business, innovative technologies, and business models that are adequate for the digital economy. One of the key components of the digital economy is the widespread development of digital platforms a way of doing business, on the basis of which an ecosystem is formed. Although ecosystems have become more widespread in the field of trade, there are other areas of their application, among which the financial sector can be distinguished.

Differences in the architecture of business ecosystems and the behaviors of their participants are based on a fundamental difference in complementary products that determine multilateral relations among participants and thereby set directions for strategic design decisions in business ecosystems (Jacobides et al. 2018). Complementary services or goods in an ecosystem can be unique and/or supermodular. Ready-to-use products are unique, as are the services of modular producers that are able to adapt them to different ecosystems, such as payment using PayPal (Wail and Warner 2019). Supermodularity is a characteristic of complementary services or goods, the presence of which contributes to the attractiveness of the ecosystem and increases the tendency to collaborate.

Ecosystem analysis of digital platforms in the global economy is also one of the most important directions for studying this topic (Kim and Mudambi 2020; Hilbolling et al. 2021; Ganco et al. 2020; Fan et al. 2021). The formation of an entrepreneurial ecosystem as the most important component of the development of the economic system as a whole is also the object of close analysis in the works of the authors (Abootorabi et al. 2021; Howard et al. 2019; Hakala et al. 2020). However, it should be noted that in these studies, the study of the ecosystem is considered to mainly pertain to the real sector of the economy or trade. Other sectors of the economy related to the service sector, for example, the financial sector, are practically not studied from the standpoint of ecosystem formation.

The problem of studying the trends and patterns of development of the financial sector, especially in times of crisis, should be reflected in modern research (Yiu et al. 2019; DesJardine et al. 2019; Dimmock et al. 2021). In addition, such debatable issues are raised as embedding the financial sector in the development of other sectors of the economy (Baghai et al. 2021; Shue and Townsend 2021; Guest 2021). However, these studies do not take into account the prospects and possibilities of the formation of ecosystems.

In the context of the development of the digital economy, a large array of publications is being formed on this topic, in particular, the risks of the digital economy, prospects for further development, the digitalization of industries, and a number of others (Skare et al. 2021; Ballestar et al. 2021; Saura 2021; Goldfarb and Tucker 2019). The issues of determining the key factors of digitalization in different sectors of the economy are not ignored (Adhikary et al. 2021; Kudryavtseva et al. 2020; Lim et al. 2021).

However, one should point out the lack of research on the formation of ecosystems in the financial sector in the context of the development of the digital economy, which predetermined the choice of the research topic and set the goal and working hypothesis.

Thus, we can conclude that the ecosystem, which is the object of analysis in this article, is an innovative tool for doing business in the financial sector that is aimed at maximizing the satisfaction of consumers of financial services on a “one-stop shop” basis using innovative digital technologies.

Based on the analysis of existing approaches to the concept of a business ecosystem, the following characteristic features can be distinguished:

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the presence of the core of the ecosystem, represented, as a rule, by a digital platform around which an ecosystem-based business community is formed, the composition of the participants of which is different and can constantly change, i.e., is flexible and mobile;

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a multilateral number of interactions between ecosystem participants, which are based on a system of formal and informal institutions (rules, norms, behaviors, etc.), offering the client a value proposition on the principle of using a “single window”;

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continuous development by the participants of the ecosystem, the introduction of innovations;

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generation of data flows and their analysis and management based on the use of digital technologies and digital competencies;

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the main assets of ecosystems are multilateral relations among participants.

Thus, the purpose of this article is to analyze the trends in the development of the financial sector, as well as the digital technologies used in this area, to identify the fundamental drivers in improving the ecosystem of the financial sector of the economy.

The scientific hypothesis of the article is that an assessment of the financial sector ecosystem, carried out using indicators of the introduction of digital technologies, will demonstrate their positive impact on the growth of gross value added, investments, and innovative services in this area of activity.

The main contribution of the article to the development of scientific thought is to test the tools of economic and mathematical modeling in assessing the contribution of digitalization tools to the formation of an ecosystem of innovations in the field of finance; in conducting a comparative analysis of trends in the development of the digital financial sector in Russia and countries around the world and developing recommendations for expanding the competencies of the Russian economy in terms of improving the ecosystem of innovations in the financial sector in the wake of digitalization.

The main results of the article are as follows: regression models of the development of the financial sector of the Russian and European economies are obtained; control variables have been identified to activate the ecosystem of financial sector innovations in the Russian economy in the context of digitalization.

2. Materials and Methods

When writing the article, aggregated statistical data for the European Union were used as the basis of the study in terms of describing trends in the development of e-commerce in the financial sector of the economy. The data source in this case was Eurostat. The following indicators were used for the analysis:

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enterprises having received orders online (at least 1%)—% of enterprises;

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share of enterprises’ turnover on e-commerce—%.

The analysis involved aggregated data from 27 countries in the European Union.

Official statistics, including time series by indicators, are available on the Eurostat website at the following link: https://ec.europa.eu.

To analyze the trends in the development of the ecosystem in the financial sector in relation to the Russian economy, the official statistics of Rosstat were used as an information base, which are available at the following link: https://rosstat.gov.ru.

To identify the relationship between ecosystem indicators in the digital economy, it is advisable to use regression analysis, which allows you to identify the influence of independent indicators on the resulting factor. In this case, we believe that the use of regression analysis can be a key research method for this article.

In accordance with the monitoring of the digital society in the Russian Federation, which monitors various contours of the business ecosystem—the interaction of its participants on the principles of comprehensive cooperation, including the sectors of science, entrepreneurship, and the state; in relation to the ecosystem of the financial sector, the following indicators were selected for analysis, and for them the symbols were introduced:

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the share of innovative services in the total volume of shipped goods, performed works, and services—Y1;

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index of physical volume of gross value added, in % of the previous year—Y2;

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the investment volume index of investments in fixed assets aimed at the acquisition of information, computer, and telecommunications (ICT) equipment—Y3;

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share of people employed in the ICT sector in the total number of employed population—X1;

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the share of financial organizations that had special software for managing the procurement of services in the total number of surveyed organizations—X2;

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the share of financial organizations that had special software for managing sales of services in the total number of surveyed organizations—X3;

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the share of financial organizations that used ERP systems (enterprise resource planning) in the total number of surveyed organizations—X4;

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the share of financial organizations that used CRM systems (customer relationship management) in the total number of surveyed organizations—X5;

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the share of financial organizations that used SCM systems (supply chain management) in the total number of surveyed organizations—X6.

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The dynamic series of indicators included data from 2010 to 2021.

The initial data for analysis and modeling of the financial sector of the Russian economy are presented in

Table 1. Indicators of the development of the financial sector ecosystem in the context of the digital economy (data are presented for the Russian Federation in percentages).

Year	Y1	Y2	Y3	X1	X2	X3	X4	X5	X6
2010	4.8	104.5	115.6	1.7	35.2	22	5.1	4.1	4.4
2011	6.3	104.3	146.0	1.7	36.1	24.3	6.2	4.6	3.7
2012	8.0	104.0	118.1	1.7	36.2	22.8	6.5	5.0	2.5
2013	9.2	101.8	96.5	1.7	38.6	22.9	7.5	5.7	2.6
2014	8.7	100.7	103.1	1.7	36.3	20.3	10.1	7.2	4.1
2015	8.4	98.0	104.4	1.7	38.4	21.9	9.3	9.9	4.3
2016	8.5	100.2	93.3	1.7	37.8	21.8	10.7	9.4	4.4
2017	7.2	101.8	136.9	1.7	36.2	22.0	12.2	10.3	4.7
2018	6.5	102.8	124.3	1.6	38.3	25.9	13.8	13.2	6.4
2019	5.3	102.2	127.6	1.7	39.0	26.0	14.8	13.9	6.6
2020	5.7	97.3	117.9	1.8	23.7	16.0	11.5	10.8	4.3
2021	5.0	104.7	104.0	1.7	26.9	18.6	13.8	13.4	4.8

.

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the share of innovative services in the total volume of shipped goods, performed works, and services—Y1;

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index of physical volume of gross value added in % of the previous year—Y2;

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the investment volume index of investments in fixed assets aimed at the acquisition of information, computer, and telecommunications (ICT) equipment—Y3;

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share of people employed in the ICT sector in the total number of employed population—X1;

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the share of financial organizations that had special software for managing the procurement of services in the total number of surveyed organizations—X2;

-

the share of financial organizations that had special software for managing sales of services in the total number of surveyed organizations—X3;

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the share of financial organizations that used ERP systems (enterprise resource planning) in the total number of surveyed organizations—X4;

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the share of financial organizations that used CRM systems (customer relationship management) in the total number of surveyed organizations—X5;

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the share of financial organizations that used SCM systems (supply chain management) in the total number of surveyed organizations—X6.

For the aggregated presentation of data, descriptive statistics were calculated, in particular: mean value, minimum value, maximum value, range, standard deviation, and coefficient of variation. The results of the descriptive statistics calculations are summarized in

Table 2. Descriptive statistics (in percentages).

Variable	Mean	Minimum	Maximum	Range	Std.Dev.	Coef.Var.
Y1	7.0	4.8	9.2	4.4	1.6	22.5
Y2	101.9	97.3	104.7	7.4	2.5	2.4
Y3	115.6	93.3	146.0	52.7	16.2	14.0
X1	1.7	1.6	1.8	0.1	0.0	1.9
X2	35.2	23.7	39.0	15.3	4.8	13.7
X3	22.0	16.0	26.0	10.0	2.8	12.8
X4	10.1	5.1	14.8	9.7	3.3	32.2
X5	9.0	4.1	13.9	9.8	3.6	39.9
X6	4.4	2.5	6.6	4.1	1.2	27.9

.

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the share of innovative services in the total volume of shipped goods, performed works, and services—Y1;

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index of physical volume of gross value added in % of the previous year—Y2;

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the investment volume index of investments in fixed assets aimed at the acquisition of information, computer, and telecommunication (ICT) equipment—Y3;

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share of people employed in the ICT sector in the total number of the employed population—X1;

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the share of financial organizations that had special software for managing the procurement of services in the total number of surveyed organizations—X2;

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the share of financial organizations that had special software for managing sales of services in the total number of surveyed organizations—X3;

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the share of financial organizations that used ERP systems (enterprise resource planning) in the total number of surveyed organizations—X4;

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the share of financial organizations that used CRM systems (customer relationship management) in the total number of surveyed organizations—X5;

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the share of financial organizations that used SCM systems (supply chain management) in the total number of surveyed organizations—X6.

Therefore, according to the table, referring to the value of the coefficient of variation, we can conclude that the greatest volatility in dynamics was characteristic of the following indicators with the maximum coefficient of variation:

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the share of financial organizations that used ERP systems in the total number of surveyed organizations—X4;

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the share of financial organizations that used CRM systems in the total number of surveyed organizations—X5;

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the share of financial organizations that used SCM systems in the total number of surveyed organizations—X6.

All of them are digital tools for integrated management in the ecosystem.

The tools for analysis used the construction of linear regression models that describe the relationship and mutual influence of dependent variables, in our case, the share of innovative services in the total volume of shipped goods, performed works, and services—Y1; index of physical volume of gross value added, in % of the previous year—Y2; the investment volume index of investments in fixed assets aimed at the acquisition of information, computer and telecommunications (ICT) equipment—Y3; from predictors (X1–X6).

The linear regression equation has the form:

Y_x = a + b × X

(1)

Y_x—dependent variable;

X—independent variable;

a, b—regression coefficients.

The applied software product Statistica was used for calculations.

To assess the statistical significance of the obtained regression models, the following set of evaluation tools was used:

(1)

the value of the coefficient of determination of the model, which at a high level of model adequacy should be more than 75%, with an average of 50–75%;

(2)

p-value of the regression coefficients, which should be less than 0.05, at a 5% significance level and less than 0.1 at a 10% significance level;

(3)

Fisher’s criterion, which indicates the significance of the model when its statistical adequacy is less than 0.05;

(4)

visual analysis of the calculated and actual values of the dependent variable, which should match as much as possible.

To obtain a higher quality of linear regression models, the method of stepwise inclusion of variables in the model was used.

3. Results

At the first stage of the analysis, we assessed the contribution of the digital sector to the development of individual sectors of the economy. Thus, in the Russian economy, the share of the ICT sector in the formation of gross value added was distributed as follows: trade—13%, real estate transactions—10.5%, extractive sector—9.8%, construction—5.6%, financial sector—4.9%. In the financial sector, 41.9% of organizations have electronic document management systems, 36.4% conduct financial settlements electronically, 21.6% have training programs, and 16.5% provide access to databases through global information networks.

In the structure of internal costs of organizations for the creation, distribution, and use of digital technologies and related products and services, by type of economic activity, the distribution was as follows: 26.8%—information and communication, 13.2%—financial sector, 9.6%—education, 9.1%—scientific and technical activities, 8.2%—manufacturing industry, 7.9%—logistics, and 25.2%—other types of economic activity.

A comparative analysis of the use of digital technologies in the financial sector in the Russian and European economies showed that Russia’s position on this issue is at the same level or slightly lower than in European countries and the USA. For example, 49% of Russian financial sector organizations use digital technologies on a systematic basis. For comparison, in Finland this figure is 92%, in Sweden—85%, the USA—60%, Japan—20% (Figure 1).

Trendline analysis shows the annual growth of the number of enterprises that received orders online (Figure 2).

Similar dynamics are typical for another indicator of digitalization of the financial sector—share of enterprises’ turnover on e-commerce, which was reached by 2021, 20% (Figure 3).

Using the algorithm described above for constructing a linear regression model with stepwise inclusion of independent variables in the model, the following results were obtained. When used as the dependent variable, Y1 represents the share of innovative services in the total volume of shipped goods, performed works, and services. The regression equation was:

Y1 = 3.2 − 1.1 × X6 + 0.4 × X2 + 0.3 × X4 − 0.4 × X3

(2)

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the share of innovative services in the total volume of shipped goods, performed works, and services—Y1;

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the share of financial organizations that had special software for managing the procurement of services in the total number of surveyed organizations—X2;

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the share of financial organizations that had special software for managing sales of services in the total number of surveyed organizations—X3;

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the share of financial organizations that used ERP systems (enterprise resource planning) in the total number of surveyed organizations—X4;

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the share of financial organizations that used SCM systems (supply chain management) in the total number of surveyed organizations—X6.

The results of the regression analysis are shown in

Table 3. Regression analysis results for variable Y1.

Indicator	Std.Err.	b	Std.Err.	t(7)	p-Value
Intercept		3.0	1.7	1.8	0.1
X6	0.2	−1.1	0.3	−3.9	0.0
X2	0.2	0.4	0.1	5.2	0.0
X4	0.2	0.3	0.1	3.1	0.0
X3	0.3	−0.4	0.1	−2.7	0.0

.

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the share of financial organizations that had special software for managing the procurement of services in the total number of surveyed organizations—X2;

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the share of financial organizations that had special software for managing sales of services in the total number of surveyed organizations—X3;

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the share of financial organizations that used ERP systems (enterprise resource planning) in the total number of surveyed organizations—X4;

-

the share of financial organizations that used SCM systems (supply chain management) in the total number of surveyed organizations—X6.

The coefficient of determination of the model was 90%, the significance of the Fisher criterion was 0.0014, and the p-value of all independent variables was less than 0.05. The graph of observed and predicted values of the dependent variables demonstrates a high proportion of coincidence between the calculated and actual data (Figure 4).

-

the share of innovative services in the total volume of shipped goods, performed works, and services—Y1.

Y1 = 159.1 + 0.9 × X3 − 0.5 × X5 − 0.4 × X2 − 32.3 × X1

(3)

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the share of innovative services in the total volume of shipped goods, performed works, and services—Y1;

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share of people employed in the ICT sector in the total number of employed population—X1;

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the share of financial organizations that had special software for managing the procurement of services in the total number of surveyed organizations—X2;

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the share of financial organizations that had special software for managing sales of services in the total number of surveyed organizations—X3;

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the share of financial organizations that used CRM systems (customer relationship management) in the total number of surveyed organizations—X5.

The results of the regression analysis are shown in

Table 4. Regression analysis results for variable Y2.

Indicator	Std.Err.	b	Std.Err.	t(7)	p-Value
Intercept		159.1	43.8	3.6	0.0
X3	0.5	0.9	0.4	2.2	0.1
X5	0.5	−0.5	0.2	−2.2	0.1
X2	0.3	−0.4	0.2	−2.2	0.1
X1	0.3	−32.3	23.2	−1.4	0.1

.

-

share of people employed in the ICT sector in the total number of employed population—X1;

-

the share of financial organizations that had special software for managing the procurement of services in the total number of surveyed organizations—X2;

-

the share of financial organizations that had special software for managing sales of services in the total number of surveyed organizations—X3;

-

the share of financial organizations that used CRM systems (customer relationship management) in the total number of surveyed organizations—X5.

The coefficient of determination of the model was 62%, the significance of the Fisher criterion was 0.1, and the p-value of all independent variables was less than 0.1. The graph of observed and predicted values of the dependent variables demonstrates a high proportion of coincidence between the calculated and actual data (Figure 5).

-

index of physical volume of gross value added in % of the previous year—Y2.

When used as the dependent variable, Y3 represents the investment volume index of investments in fixed assets aimed at the acquisition of information, computer, and telecommunication (ICT) equipment. The regression equation was:

Y1 = −252.2 + 8.6 × X3 − 3.8 × X2

(4)

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the share of innovative services in the total volume of shipped goods, performed works, and services—Y1;

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the share of financial organizations that had special software for managing the procurement of services in the total number of surveyed organizations—X2;

-

the share of financial organizations that had special software for managing sales of services in the total number of surveyed organizations—X3.

The results of the regression analysis are shown in

Table 5. Regression analysis results for variable Y3.

Indicator	Std.Err.	b	Std.Err.	t(7)	p-Value
Intercept		−252.2	262.7	−1.0	0.1
X3	0.5	8.6	2.8	3.0	0.0
X2	0.5	−3.8	1.5	−2.5	0.0
X1	0.3	181.3	142.3	1.3	0.2

.

-

share of people employed in the ICT sector in the total number of employed population—X1;

-

the share of financial organizations that had special software for managing the procurement of services in the total number of surveyed organizations—X2;

-

the share of financial organizations that had special software for managing sales of services in the total number of surveyed organizations—X3.

The coefficient of determination of the model was 53%, the significance of the Fisher criterion was 0.09, and the p-value of all independent variables was less than 0.1 (with the exception of the indicator X1, which is therefore excluded from the model). The graph of observed and predicted values of the dependent variables demonstrates a high proportion of coincidence between the calculated and actual data (Figure 6).

-

the investment volume index of investments in fixed assets aimed at the acquisition of information, computer, and telecommunication (ICT) equipment—Y3.

With regard to innovation ecosystems in countries in the European Union, based on modeling the economies of 27 European countries, the following results were averaged. The following factors that shape the development of financial and nonfinancial transactions in the innovation market have the greatest impact on the development of the innovation ecosystem, as represented by the indicator, the share of shipped innovative products:

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share of organizations using SCM systems (the regression coefficient was 0.49);

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share of organizations using CRM systems (the regression coefficient was 0.47);

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share of organizations that carry out financial settlements online when selling innovative products (the regression coefficient was 0.47).

As you can see, the presented results of modeling the innovation ecosystem for the Russian economy and for countries in the European Union differ from each other. While for the Russian economy it is not enough to use integrated solutions in the management of flows in the field of innovation—supply chain management systems—for European states, this factor is the most decisive in the context of the development of the digital economy. This allows us to talk about the need to expand the scope of integrated solutions in the development of the innovation ecosystem in relation to the Russian innovation system in the context of developing a digital economy.

To develop a strategy for the development of the financial sector in the Russian economy in the ecosystem of innovations, it is fundamentally important to use the achievements of European countries in this matter. Thus, the analysis allowed us to identify the following trends in financial technology or FinTech. Its key players are predominantly technology companies rather than traditional players in the financial sector. A large number of innovative projects, developed mainly in Silicon Valley, contributed to the creation of a favorable environment for the development of a new industry. However, today other countries, such as the UK, Singapore, South Korea, and others, are also becoming centers of financial technology. Key developments in the financial technology market in Europe are provided by: mobile technologies, big data, artificial intelligence, digital currency technologies, blockchain, virtual and augmented reality, contactless technologies, and biometric technologies. The financial sector in Europe is actively using digital technologies such as: contact centers; official websites and marketplaces, mobile applications, social media accounts; terminals, information kiosks, ATMs; mobile offices; virtual offices, etc. Tech startups are the drivers of the global FinTech industry in countries around the world, including Europe. However, in the Russian economy, technological startups, especially in the financial sector, have not yet received a high level of development. Here are examples of the most successful start-up companies in the field of FinTech in countries around the world. Among them:

Payment processing and payment channel’s support: Stripe; Klarna; Toast; Payoneer; Marqeta; Razorpay; TouchBistro; Previse; Handle; GoCardless; Upserve; Ezetap; Yapstone; Veem; Origami

Digital banking: Greenlight; Uala; Petal; BREX; MONZO; CAPITAL; CHIME; N26; NUBank; Gimi

Multiprofile credit services and marketplaces: LU.com; JUMO; LendKey; progressa; WeLab; Lendio; GoFundMe; CircleUp; TrueAccord; JD Finance; CGTZ.com

Scoring systems and analytics: Credit Carma; Nav; Nova; Ice Kredit; ID Finance; Juvo; Lendstreet; CreditMantri; InDiaLends; Wecash, etc.

Among the trends in the development of FinTech in Europe, one should point to investing in technology startups, opening APIs, and using third-party platforms. The model of neobanks is becoming popular—these are “full-fledged” banks with a traditional set of services (opening accounts, lending, deposits, etc.), but, as a rule, without physical branch networks; they sell products and provide services using special mobile applications, websites, and accounts in social networks.

4. Discussion

Thus, in relation to the Russian financial sector, the following key areas of digitalization can be identified in the ecosystem of the financial sector in the context of the development of the digital economy. Let us summarize the results of the regression analysis in a table, where we can indicate which of the local indicators of the digitalization of the financial sector have an impact on the formation of the ecosystem of this sector of the economy. To do this, we created a cross-tabulation of the set of dependent and independent variables in the regression models. The + sign indicates the presence of this independent indicator in the model for a certain dependent variable (

Table 6. Crosstab of dependent and independent variables of regression models describing the ecosystems of the Russian financial sector.

Indicator	Y1	Y2	Y3
X1		+
X2	+	+	+
X3	+	+	+
X4	+
X5		+
X6	+
Total impact on the ecosystem	−0.8	−32.3	4.9

). Additionally, based on the coefficients of the regression equations in the last line of the table, we present the total impact of indicators on the formation of the financial sector ecosystem.

-

share of people employed in the ICT sector in the total number of employed population—X1;

-

the share of financial organizations that had special software for managing the procurement of services in the total number of surveyed organizations—X2;

-

the share of financial organizations that had special software for managing sales of services in the total number of surveyed organizations—X3;

-

the share of financial organizations that used ERP systems (enterprise resource planning) in the total number of surveyed organizations—X4;

-

the share of financial organizations that used CRM systems (customer relationship management) in the total number of surveyed organizations—X5;

-

the share of financial organizations that used SCM systems (supply chain management) in the total number of surveyed organizations—X6.

Based on the results of the analysis, it can be concluded that the following indicators have the greatest impact on the ecosystem of the financial sector:

-

the share of financial organizations that had special software for managing the procurement of services in the total number of surveyed organizations—X2;

-

the share of financial organizations that had special software for managing sales of services in the total number of surveyed organizations—X3.

The obtained modeling results are a logical continuation of the work of the authors involved in the study of trends in the financial sector of the economy (Jorgensen and Igel 2021) and (Mnif et al. 2020). The presented results of the article also correlate with the conclusions on the instability of digitalization trends in the sectors of the Russian economy, reflected in the works of researchers (Tirole 2021; Shinkevich et al. 2021; Tolstykh et al. 2019).

However, in aggregate, for the Russian financial sector, at the moment there is a negative state in the ecosystem in this area since the increase in gross value added and innovative modeling activities have shown a negative trend, which may be due to the high volatility of these indicators, the lack of systemic solutions to manage the digitalization of the financial system, and the instability of trends in the analyzed indicators. Thus, the proposed working hypothesis—an assessment of the financial sector ecosystem, carried out using indicators of the introduction of digital technologies, demonstrating their positive impact on the growth of gross value added, investments, and innovative services in this area of activity—was not confirmed in relation to the Russian financial sector.

In addition, according to the results of the simulation, insufficient use of integrated technologies and solutions for managing the chains of financial services was revealed—ERP systems, CRM systems, and SCM systems—which also indicates the weakness of the formation of the ecosystem of the financial sector in the Russian economy during the transition to a digital economy.

The need to use integrated solutions in business process management is also consistent with the opinions of the authors, who call for considering business from the point of view of the open innovation model (Öberg and Alexander 2019; Ferreira and Teixeira 2019; Christensen and Karlsson 2019).

Directions for further research in the field of the financial sector ecosystem in the context of the transition to a digital economy may be related to the development of tools for assessing the levels of digital maturity of the financial sector, the algorithms for readiness for the digital transformation of the financial sector, improving the mechanisms and methods of collaboration in the financial sector, and improving the ecosystem of the financial sector through the implementation of innovative approaches to management.

The proposed research methodology differs from the existing approaches to assessing the innovation ecosystem typical for the countries in the European Union in that it attempts to consider the development of the innovation ecosystem through the use of digital technologies for managing integrated solutions. Despite the fact that the methodology proposes the use of traditional methods of analysis, namely, correlation and regression analysis, the obtained modeling results allow us to identify the mutual influence of digital technologies for managing integrated solutions in the management of financial flows in the field of innovation on raising the level of the innovation system of both individual states and the European Union as a whole. Undoubtedly, each country has its own specific features in the development of the innovation ecosystem; however, the proposed set of indicators for their assessment and modeling is generally accepted both in Russia and in EU countries, which allows for comparative analysis, comparison, and the use of benchmarking technologies to develop strategies and programs for the development of the innovation ecosystems, including the use of financial flow management systems in the context of developing a digital economy.

Based on the experience of European countries, the following areas can be identified as relevant for the formation of an ecosystem of innovations in the financial sector of the Russian economy: the development of FinTech technology start-ups, the use of open platforms, and the development of neo-banking. These directions will allow the Russian economy to increase the level of digitalization in the financial sector and determine its place in the trends at the European and global levels in the market for advanced technological solutions in relation to the field of finance.

5. Conclusions

Thus, based on the results presented in the article, the following conclusions can be summarized:

First, it is shown that the digital economy of the ecosystem businesses is becoming a new tool for business growth and scaling, including in the financial sector. At present, ecosystems are becoming more widespread not only in certain sectors of the economy, but are also global, large-scale in nature, characterized by their interconnectedness and the complication of the collaboration of their participants.

Secondly, there is a steady positive trend in the digitization of the main business processes in the field of finance that has been observed in national economic systems. Digital technologies in the form of managerial innovations are increasingly being embodied as a tool for achieving the strategic goals of the financial business.

Thirdly, it may be necessary to develop and introduce into economic practice an ecosystem approach to management as an innovative tool for business development, increasing its competitiveness, and achieving sustainable development.

Fourth, with regard to the Russian financial sector, there is a weakness in the development of the ecosystem, which is partly due to the insufficient use of complex digital solutions in managing financial flows, for example, the use of ERP systems, CRM systems, and SCM systems.

Fifthly, as indicators of the development of the ecosystem of the financial sector, it is proposed to use indicators of innovative activity, growth in gross value added, and investments in conjunction with the level of mastering digital technologies in managing financial flows along the entire consumer value chain.

We believe that the conclusions and results presented in the article can be used as methodological tools for developing strategies for improving the ecosystem of the financial sector in the context of the transition to a digital economy.