“Modeling of FinTech market development (on the example of Ukraine)”

FinTech startups and services are one of the most dynamic segments of the modern economy. New financial technologies have already attracted many investors and form millions of budgets. Changing the traditional financial services concept, FinTech companies formed a new niche within the financial services market, the dynamic development of which determines the relevance of the development and implementation of an effective regulatory and oversight system. The purpose of the article is to develop an economic and mathematical model for forecasting the development of the FinTech market on the example of Ukraine. In order to study the development of the FinTech industry, a multiple regression model was presented. The model describes the dependence of the total investment value of FinTech from venture investments in financial technology, venture investments in other technologies and venture investments in online lending. Based on this model, the effect of attracting investments with new FinTech projects on the total volume of investments in the industry was clarified. According to the model, with a change in investments in FinTech by 1%, the total rate of venture investments decreases by 0.03, funds in new projects of other companies grow by 0.05, and venture investments in online lending increase by 0.89. According to the analysis of regulatory legislation in the foreign countries of the FinTech services sphere, it was found that the regulation of most of the risks associated with the development of FinTech services falls within the competence of different supervisory authorities, requiring cross-sectoral cooperation between public institutions.


INTRODUCTION
The current market offer should be based on the preferences of the consumer for which the product should be adapted.Meanwhile, in the sphere of FinTech innovations, there is an increase in consumer interest in the very fact of FinTech implementations, which provide a certain, but shallow detuning from competitive supply in the market.One of the most innovative areas of the financial market is financial technology.Starting its development in 2008 based on the global crisis, FinTech has grown rapidly and increased attention from investors.The availability of fast and wireless Internet, the development of social networks and the evolution of smartphones from ordinary means of communication to personal assistants with a personal set of special applications have contributed to the expansion of demand for modern financial services and products.At the same time, classical financial institutions, primarily banks, due to regulatory and legislative constraints introduced to overcome the effects of the crisis, as well as through certain conservatism, failed to respond quickly and adequately to the needs of consumers.Technology companies have used the new niche by presenting their financial products, which by their very nature were simpler, while more flexible, adaptive and accessible to customers at any time and in any place.The active development of FinTech companies and the growing competition with classical financial intermediaries have determined the relevance of the analysis of trends in the development of FinTech services in the financial services market.

LITERATURE REVIEW
Modern marketing identification of basic needs of clients is formed in the conditions of the unpredictable crisis transformation of the markets and their transition to the evolutionary phase of accelerated innovative transformation of the product-service offer.
In the context of high-tech products, the analysis of the development of financial technologies is undertaken by reputable global companies from different spheres of the market, such as Consumers International, KPMG, Kofax, Capgemini and others.However, most of the information provided in their reports is only grouped by statistical data or expert assessments of company employees.
Based on marketing aspect of the innovation communications development, relationship marketing acts as a tool for practical implementation of the concept of joint creation of value in modern institutional forms of interaction in the innovation system (innovation networks, virtual, international clusters, alliances, etc.).Some authors considered the empirical features of modern evolution of market of payment services through the lens of FinTech marketing that allowed to rethink the basic needs of user and existing customer experience in wider key marketing, as well as generate additional arguments to justify the perspective of resulting trajectory of development of the market.That in future will be characterized by increasing competitive technology sector operators and weakening position of functional market positioning of banks, which will maintain its market inertia of status-quo in conjunction with traditional architecture of payment and ability to reduce industry risk of the entry for new operators (Kyznetsov & Shalahova, 2016).
Modern marketing identification of basic needs in the context of rapidly developing user experience in the markets of new high-tech products is also presented in publications (Kotler & Trias de Bes, 2015), reflecting the empirical features of building strategies for developing customer-oriented service, especially in conditions of unpredictable crisis transformation of markets or their entry into the evolutionary phase of accelerated innovative transformation of the product-service proposal (Kim & Moborn, 2016;Revo, 2016).
At the same time, a model that would allow estimating the further development of the sphere of FinTech is not included in any of the publications.This is a significant disadvantage as experts' forecasts are subjective and cannot reliably assess the market situation, and a large amount of information accumulated on the FinTech industry makes it difficult to distinguish the most significant indicators.Therefore, a mathematical model was developed that allows us to determine the further development of the FinTech market in Ukraine based on factors with the greatest influence on the volume of investments in FinTech market.

AIM
Modeling of an economic-mathematical model for forecasting the development of the FinTech market on the example of Ukraine.

METHODS
The article uses methods of economical and mathematical modeling, namely, regression analysis had allowed to determine the dependence of the main indicators that influence the development of the FinTech market in Ukraine.Methods of statistical synthesis and statistical analysis were used to synthesize the research material.Also, analysis and synthesis were used to select financial market valuation indices.

RESULTS
Modern retail is undergoing a FinTech revolution.Business is actively using the revolution in the field of financial technologies, which forms a new technological breakthrough, moving from lending to payment services, and in the future insurance and savings of the population.
There is a convergence of banks and retail, which have not previously been integrated in customer service, and today actively interact, forming a higher level of customer loyalty and expanding the consumer audience.At the same time, the most relevant component of the effect from the use of FinTech marketing is the expansion of the consumer audience.
Figure 1 shows the forecasting of the number of users of the FinTech products for the period 2016-2022.
After analyzing the number of users of FinTech products, we can conclude that their number is dynamically increasing from year to year.Thus, according to forecast data, by the beginning of 2022, it will amount to more than 4.3 billion people, and the to-tal value of transactions with the use of FinTech will reach the level of 3 trillion US dollars (Figure 2).
According to the Statistical Committee of the Republic of Armenia, the financial services and insurance market has recorded a steady growth over the past five years (CAGR 4.14%).In Armenia, several FinTech instruments are widely used such as: • payment systems (Easypay, Idram, Telcell); • banking online services and mobile applications (Inecobank, Evocabank, Acbabank); • credit online services (varks.am).The overall structure and dynamics of the number of FinTech companies in Ukraine is presented in Table 1.Thus, as of January 1, 2018, FinTech's ecosystem of financial services market in Ukraine was 51 FinTech company, which is 2 times more than in 2013.

Access to the financial services in
Maintaining a trend on modern financial and technological services, domestic banks illustrate the potential desire to implement their elements in their activities.According to the study of the degree of use of the innovative vocabulary by the banks (mobility, big data, electronic money, bitcoin, cryptocurrency, block, FinTech and P2P) in the context of providing FinTech services by company Noks Fishes, it was found that the banks began to talk more about innovation in 2017 compared to the same period in 2016, although the percentage of texts in one form or another FinTech averaged only 7.98% of the total number of articles.PrivatBank was the most active in this sphere (the share of using FinTech amounted to 65%).Oschadbank which became the leader of the media space on the topic of contactless payments had 32% and OTP bank was more agile in promoting electronic money.In general, the most used words in 2017 in bank texts were: contactless payments, bitcoin, FinTech.This indicated a significant interest of banks in these technologies.
In order simulate the development of FinTech industry for the dependent variable (Y), the volume of investments invested in the FinTech industry was adopted.As the main factors influencing Y, there were selected general venture investments in Fi nTech (x 1 ), venture investments in other industries (x 2 ), venture investments in online lending (х 3 ), and income from lending of crypto exchanges (х 4 ).According to the latest reports from various analytical publications, these four factors will form the fastest pace of development of FinTech projects.
The largest part in the financial industry for 2018 is the blockchain system and the digital currency (cryptocurrency) built on this system.According to Ernst  Due to the significant growth rates of the cryptocurrency, it occupies a leading position among the FinTech subsectors and was selected as a factor to be presented in the model.In order to reflect the real development of the industry, the revenue from the crypto lending was selected.This will give an idea of the amount of investments that are annually invested in the industry.
Another sub-sector that shows a rapid growth is online lending.The Pulse of FinTech 2018 reports that the development of the InsurTech in the second and third quarters of 2018 is expected to be at the level of Artificial Intelligence, Blockchain and RPA (Robotic Process Automation).
In addition to the leading industries in the model, market conditions are also to be considered, for this purpose, the factor of venture investments has been introduced, because the interest of investors in project implementation depends not only on their profitability, but also on the state of the market, the position of the states regarding the development of FinTech stratagems.In Europe, in 2017, a 25% down venture investment dropped due to the Brexit vote, while investment in Japan has grown to 271.7 billion yen, against 63.6 billion yen in 2012.Favorable conditions for increasing venture capital in the country were created through the recognition of crypto currency as an official means of payment, as well as the adoption of a new law on currency regulation.
Consequently, all factors have a significant impact on the functioning of the financial market and its further development.
For the construction of the model, data were taken for 8 years from 2010 to 2017 (Table 2).Determination with dependent variable was calculated for each factor using the Excel data analysis package.
From the results obtained, it can be argued about the significant impact of each of the selected factors on the level of investment in FinTech, since the ratio between y and x is higher than 0.5.The following conclusions can be drawn.The value of R 2 for x 1 and x 4 is in the range from 0.5 to 0.8 and indicates the average strength of the relationship between independent variable x and the dependent variable y.The results of x 2 and x 3 are between 0.8 and 1 and indicate strong linear relationship.Factors are adequate, there is a direct relationship between independent and dependent variables.
The next stage of the calculation was the verification of indicators for multicollinearity to determine the dependence of factors among themselves.According to Student's criterion, there is a close correlation between x 1 and x 3 , but the Pearson criterion for three factors is closer to the tabular value.We apply another method for eliminating the multicollinearity in the model.We will use the method of natural normalization of variables for leveling a numerical series.In the application of the method, new indicators of the factors presented in Table 3 were obtained.According to the new index 2 , i X the Pearson cri- terion was 12.12, which is less than the value of 2 , i tabl X which is equal to 14.07.Consequently, the new model lacks multicollinearity.The next step i n verifying the reliability of the model is its verification for autocorrelation to de-tect the relationship between sequential levels of the time series.To calculate, the remnants of the model (Table 4) were calculated, which were calculated using the Excel analysis package and the subsequent application of the Durbin-Watson test.
The resulting value for the model is not within the uncertainty zone (0.368 < d < 2.287), d = 2.38 and is higher critical value of the upper limit, thus confirming the hypothesis that there is no auto-correlation in the model.The balances of time series (errors) are random, with no trends and cyclical fluctuations.
When testing the model, it is also necessary to check it for the absence of heteroscedasticity.The test will help to determine whether the variance of the variables is constant.To detect heteroscedasticity, use the Breusch-Pagan test.
Values for calculations were obtained from regression indicators that were found using the Excel toolkit.The data are given in Table 5.
Based on the data in Table 4, the regression was calculated, where R 2 = 0.33.The value of 2 i X accord- ing to the model is 2.61, while the tabular value is 2 7.81.
The model is homoscedastic; hence the variance of values is minimal, all weighty factors are considered.
After checking and correcting the initial data, we can conclude about the reliability of the model, its adequacy and the accuracy of the values obtained, so by using the least squares method, we find constant values for x-factors and write down the resulting model: Thus, it is possible to follow the effect of attracting investment in new projects of FinTech on the total volume of investments in the industry.Thus, with a change in investment in FinTech by 1%, the to- tal venture investment will decrease by 0.03, the funds for new projects in other sectors will grow by 0.05, while venture investments in online lending will increase by 0.89.The quality of the constructed model can be checked by calculating the difference between the real (Y) and the simulated value (Y m ), which is presented graphically in Figure 3.
On average, the actual values of Y do not have significant differences with the Y m -modeled ones, we can assume that the resulting multiple regression model can be used in further studies.
Since the multiple regression model constructed adequately reflects the real state of investments in the financial sphere, it is possible to make a forecast of the level of the received branch of funds in the future, considering the influence of the selected factors.Thus, we use the forecasting function and get a value of 0.96 billion UAH for 2018 (Figure 4).Thus, according to the analysis of regulatory legislation in the foreign countries of the FinTech services sphere, it was found that the regulation of most of the risks associated with the development of FinTech services falls within the competence of different supervisory bodies, requiring cross-sectoral cooperation between public institutions.In their activities, supervisors rely on innovative technologies such as: artificial intelligence; distributed registers; unstructured data analysis.Preferably, regulators use two approaches to regulating FinTech services: based on rules and based on principles.Among the analysis countries, the most widespread approach is based on open source regulation, namely the regulatory sandbox, whose main objective is to support innovation in the financial services market.
Regulatory sandbox, created in accordance with the structural recommendation model, will contribute to the achievement of sustainable results, which are part of a more comprehensive package of initiatives to stimulate innovation and address the major shortcomings that block beneficial innovations in the financial services market.These packs of initiatives may include: improving the regulatory framework; Periodic review and improvement of licensing processes for financial services and products; Measures aimed at increasing competition and stimulating innovation in the country as a whole; regular evaluation of regulatory impact; financial and legal support for FinTech accelerators and incubators.

CONCLUSION
In the last 10 years since the beginning of the global financial crisis, the financial services market has changed significantly.The availability of fast and wireless Internet, the development of social networks and the evolution of smartphones have contributed to the expansion of demand for modern financial services and products.However, the classic financial institutions, especially banks, due to regulatory and legal restrictions imposed to overcome the crisis could not quickly and adequately respond to the needs of consumers.Technology companies have used the new niche by presenting their financial products, which by their very nature were simpler, while more flexible, adaptive and accessible to customers at any time and in any place.
The active development of FinTech companies and the growing competition with classical financial intermediaries have determined the relevance of the analysis of trends in the development of FinTech services in the global financial services market.
The domestic sphere of FinTech services is at the stage of active development, which is manifested in increasing the number of FinTech companies and communities that support and ensure the development of the domestic FinTech market.In general, this trend is due to an increase in the number of Internet users and the degree of penetration of the Internet, an increase in the number of active users of smartphones, computers and tablets, the development of e-commerce, including mobile.The regression model of the dependence of the total value of investments in FinTech from venture investments in financial technology, venture investments in other technologies and venture investments in online lending in Ukraine testifies to the high financial potential of this segment of the market.The outlook predicts a deepening of FinTech's services to UAH 0.96 billion, already in the next year.

Figure 2 .
Figure 2. Cost of transactions in the FinTech Industry, billion US dollars regression model describes the dependence of the total investment value of FinTech from venture investments in financial technology, venture investments in other technologies and venture investments in online lending.

Figure 3 .Figure 4 .
Figure 3.Comparison of actual and simulated model data

Table 1 .
Dynamics of the number of registered FinTech companies in the financial services market of Ukraine during 2013-2017

Table 2 .
Output data for constructing a regression model of FinTech market developmentFor this, we applied the Farrar-Globard algorithm.It was received the Xi2 Chi-squared Pearson criterion in the size of 32.97, which is more than the tabular value of Chi-squared у (Х і tabl = 14.07).It has been determined that multicollinearity is present in the factors x 1 , x 3 and x 4 , so the variables are dependent on each other and do not allow to evaluate the influence of each individual factor on the variable and reduce the reliability of the regression estimation.Values according to Student's criteria indicate the multicollinearity between the factors x 1 and x 4 , since t 14 is greater than the tabular value.To eliminate multicollinearity, you must exclude one of the variables.Since the R 2 factor x 4 is smaller than the R 2 factor x 1 , we exclude the factor yielding from the crypto exchange from the model and rely on three factors.
We find out between what values there is multicollinearity to eliminate the factor, which reduces the reliability of the model.To do this, we will calculate the Student's t-test.The calculated Pearson criterion was 18.00, while the table value was 14.07.The Fisher's criterion was calculated to refute or confirm the hypothesis of the relationship between factors.

Table 3 .
Output data for the regression model corrected by the method of natural normalization

Table 4 .
Data for calculation of autocorrelation

Table 5 .
Output data for calculating the Breusch-Pagan test