An Assessment of Lithuania’s Financial Technology Development

Abstract

The Lithuanian financial technology (referred to as FinTech) sector is one of the fastest-growing financial technology centers in Europe; however, this sector faces economic, regulatory, and technological challenges that hinder its development. This article aims to assess the state of development of Lithuania’s FinTech sector, identify the main challenges, and provide recommendations to promote the development of the sector. This study uses quantitative indicators, inter-criteria correlation, multi-criteria evaluation methods, and SWOT analysis. This article’s results will help identify the key factors that influence the growth of the FinTech sector in Lithuania and will be useful in shaping the sector’s further development strategy. The results of this study revealed that factors such as favorable regulation influence the FinTech sector in Lithuania the most, strengthening the innovation ecosystem and attracting international investments. However, the sector still faces challenges such as a lack of skilled labor, ensuring cybersecurity, and constant regulatory adaptation to new technologies. Based on the results of this study, it is recommended to pay more attention to educational programs aimed at training technology specialists, to promote cooperation between the public and private sectors, and to further improve the regulatory environment to ensure the sustainable and safe development of FinTech.

1. Introduction

The digital revolution has driven technological advances over the past few decades that have fundamentally transformed financial markets and services [1,2]. The result of these changes is a significant change in traditional financial processes, such as mobile payments, money transfers, borrowing, raising financing, and asset and investment management [3]. These changes lead to the rapid development of financial technology and significant changes in traditional financial systems, making financial transactions more efficient, easier, and cheaper. FinTech companies, taking advantage of these innovations, offer consumers more convenient and technology-based financial services. Globally, the FinTech sector is expanding rapidly, becoming an essential element of the financial system, and opening new opportunities for both businesses and consumers [4]. A financial services revolution is underway [5], and innovations such as artificial intelligence, blockchain technologies, and open banking continue to transform the sector, providing the opportunity to manage data more efficiently, ensuring greater security, and creating new financial service models. However, in a rapidly changing world of technology, any innovation can unexpectedly change market dynamics and influence the future of the sector. Nevertheless, the development of the FinTech sector in Lithuania is limited by certain economic, regulatory, and technological factors that prevent the country from maintaining its competitiveness in the international market [6]. Therefore, Lithuania needs to adapt to these rapidly changing circumstances and ensure that the FinTech sector can continue to grow and exploit the opportunities provided by innovation. This article fills the knowledge gap about FinTech development in Lithuania. A few companies are presented that implement the principles of FinTech, but it is also important to present the main challenges that the FinTech sector in Lithuania faces.

This article examines the following question: what are the main challenges that the FinTech sector in Lithuania faces, and how can they be overcome to promote the development of the sector?

The subject of this article is Lithuania’s financial technology sector.

This article aims to assess the development of the Lithuanian FinTech sector and identify the main challenges and opportunities to implement national development guidelines.

The following are tasks for achieving this goal:

• Analyze theoretical models of financial technology development to understand their impact on the country’s economy and the development of the financial sector.
• Develop a research methodology that would allow for a systematic assessment of the factors of the development of the FinTech sector in Lithuania, based on quantitative indicators.
• Conduct an empirical study in order to identify the main factors that promote or limit the development of the FinTech sector in Lithuania and assess their impact on the growth of this sector.

The methods used in this article to answer the above question include an analysis of the scientific literature and theoretical statements, the collection of secondary material, and information processing. Comparative data analysis, inter-criteria correlation (CRITIC method), the multi-criteria complex proportional assessment (COPRAS) method, and SWOT analysis are performed, and a priority order of sectors is formed.

2. The Concept and Main Factors of Financial Technologies

The digital transformation highlighted by the Fourth Industrial Revolution has led to the emergence of sophisticated technology-based financial services, known as FinTech, which has rapidly transformed the traditional financial services space. The Fourth Industrial Revolution has created an emerging environment in which more disruptive and digitally transformative technologies such as the Internet of Things, augmented reality, and artificial intelligence are changing the way we live [7,8]. This revolution has also permeated the financial industry, leading to the emergence of FinTech, which is mainly characterized by the emergence of technological innovations that help develop new profitable business ideas related to financial services [9,10]. FinTech can be interpreted as the application of information technology in the fields of finance, financial innovation, and digital innovation. Essentially, FinTech is an abbreviation for financial technology, which has emerged because of the application of innovative technologies. The global adoption of FinTech is growing rapidly due to its nature, as FinTech principles are mostly applied by those who want to change the essence of traditional financial services and promote the emergence of a digital revolution. FinTech is one of the most important innovations in the financial services industry, driven by the sharing economy, regulation, policy, and information technology [11,12].

The development of FinTech benefits from general advances in many areas, such as blockchain, big data, machine learning, artificial intelligence, and the digital economy [13], which have particularly influenced the overall economic growth of many countries. A new generation of investment banking and retail companies has perfectly combined the power of the Internet and convenient smartphones [14]. Banking applications have allowed customers to perform digital technological transactions and weakened bank protocols, making banks easier to access online than using traditional methods [15]. The higher the level of the development of financial technology services, the greater the challenges for businesses.

The FinTech business model also focuses on payment and lending services. In addition, it includes personal financial advisory services, crowdfunding, virtual currencies, and security (e.g., cybersecurity) [11]. Online lending services have caused controversy in communities, including moral hazard, loan default, and information asymmetry. As a result, regulators are encouraging innovation in the financial sector and applying consumer protection and risk management principles to ensure that every consumer receives safe and appropriate financial services.

The development of FinTech has not replaced traditional finance but has solved many complex problems that have prevented the poor from accessing financial products. The synergy of FinTech power and traditional financing methods has improved cash flow management, and this has been especially evident during the COVID-19 pandemic [16]. The author of [17] investigated the importance of FinTech advancements in helping people to recover faster from the economic shocks caused by the COVID-19 pandemic, so over the past few years, FinTech innovations have enabled financial stability and social responsibility in a pandemic-stricken world.

FinTech companies are growing rapidly around the world because their innovative services are simple and creatively use new digital technologies. This poses a significant threat to incumbents because their traditional way of providing financial services is complex and subject to strict rules set by regulatory boards. Thus, existing operators need to think about strategic alliances that could be collaborative or competitive, depending on their business objectives. Given that FinTech has found the right balance between innovation and efficiency and risk management [18,19], in most countries, authorities and central banks, especially in the last three years, have started to implement FinTech technologies in the provision of financial services. It follows that FinTech innovations have led to positive financial intermediation and economic productivity.

In summary, it can be stated that the development of FinTech has a positive effect on economic productivity and encourages improvement in the process of providing financial services and the quality of the provision of financial services themselves to make them accessible to every consumer and make traditional financial services even more qualitative. It is necessary to promote closer cooperation between traditional banks and FinTech companies for banks to become more innovative and, by applying innovative technologies, gain more loyal customers who can enjoy the fast and reliable financial services they receive.

2.1. The FinTech Ecosystem and the Situation of This Sector in Lithuania

The rapid development of FinTech has led to the emergence of new business models and products that could challenge traditional financial institutions and have an impact on financial stability [20]. The development of FinTech has led to the emergence of new business models, the application of new technologies, and the introduction of innovative products and services to the market, which has a significant impact on the financial market and the efficiency of financial service delivery. There are six FinTech business models, including insurance services, crowdfunding, payments, lending, asset management, and capital markets [11], but the impact of FinTech is particularly visible in the case of banking services [21]. Such development and application of innovative technologies in the financial sector have advantages: improving the efficiency of financial activities; reducing operating costs; facilitating strategic intermediation, which brings novelty to entrepreneurship; and democratizing access to financial services [12,22,23,24].

The area of financial technologies emerged from the merger of two sectors (Figure 1): the provision of financial services and the implementation of innovative technologies.

Figure 1 depicts the FinTech ecosystem in Lithuania, which includes innovative technologies, companies, startups, and state institutions that regulate and supervise this sector. The main strengths of the Lithuanian FinTech sector are effective regulation, technological infrastructure, and a favorable business environment. However, the growth of this sector is limited by increasing competition in international markets, the slowness of regulatory changes, and the speed of the adoption of technological innovations (Figure 2).

FinTech in Lithuania is made up of various segments (see Figure 2), including electronic money institutions (EMIs), payment service providers (PSPs), peer-to-peer lending platforms (PPPs), crowdfunding platforms (CFPs), investment and asset management companies (WealthTech), and digital banking. These segments ensure the creation and development of innovative financial services, but economic and regulatory factors influence this.

Economic and regulatory factors are key components of FinTech development. It is important to note that the Lithuanian FinTech sector, although growing rapidly, has not yet reached its full potential due to the challenges of the regulatory framework, which is often changing, causing uncertainty for both new and existing companies.

Several important factors determine the development of the FinTech sector in Lithuania:

• Economic factors: Growing consumer demand for fast and efficient financial services is driving the development of the FinTech sector. Consumers are increasingly choosing digital services due to their convenience, accessibility, and price advantages.
• Regulatory factors: The Bank of Lithuania and other regulatory authorities actively support the FinTech sector by issuing licenses and permits to new companies. The regulatory environment in Lithuania is favorable, as it allows innovation to flourish, but at the same time imposes requirements that ensure the security of the financial system and the protection of consumer rights.
• Technological factors: Technological advances, such as blockchain, artificial intelligence (AI), and data analytics, provide FinTech organizations with opportunities to create new products and services, optimize processes, and improve user experience. The implementation of technologies allows for cost reduction and increased service efficiency.

Consumer behavior in Lithuania is changing, increasingly leaning towards digital services, which is driving the growth of the FinTech sector. Consumers value speed, convenience, and competitiveness, so FinTech companies must constantly improve their offerings. Competition between FinTech companies and traditional financial institutions is also increasing, so the latter are forced to adapt to new consumer needs and introduce innovations in order to remain competitive. When assessing the entire FinTech sector, it is important to analyze how it is changing and whether this is not a short-term startup breakthrough that will soon end. This can be best demonstrated by consumer interest in the sector [27] and the groups it covers.

2.2. FinTech Development Guidelines: 2023–2028

The Lithuanian Government actively promotes the development of FinTech, recognizing its potential to create new jobs, attract foreign investment, and contribute to economic growth. Therefore, the Ministry of Finance of the Republic of Lithuania has provided guidelines for the development of the FinTech sector for 2023–2028 (see Figure 3) in order to further strengthen the country’s position as a regional FinTech center.

The active growth of FinTech companies in the past few years has encouraged the qualitative development of the FinTech sector in Lithuania. This article analyzes qualitative development through the increase in the number of FinTech companies. Licensed companies had the right to carry out the specified activities.

Long-term benefits for Lithuania include the attraction of innovative solutions, which increases the potential of companies providing high-quality financial services. This stimulates investor interest in the country’s market and increases the creation of high added value. This study analyzes the financial indicators of all FinTech companies, including their added value created in 2023, expressed in thousands of euros.

In order for Lithuania to become a center of FinTech competencies, it is necessary to increase the supply of talent and attract highly qualified specialists. This can be achieved via closer cooperation with higher education institutions that develop specialized programs for specialists needed in the FinTech sector. The Ministry of Economy and Innovation will also implement measures to attract missing specialists from third countries, especially those with experience in technology and knowledge-intensive service sectors. This study discusses Lithuania’s aspiration to become a FinTech competence center, which is closely related to the capital requirements established by the legal acts of the Republic of Lithuania regulating the initial capital of licensed companies required to carry out certain activities.

Lithuania aims to be a safe and reliable jurisdiction, ensuring security and reliability and improving cooperation and trust. For this purpose, it is important to strengthen risk management procedures and increase the maturity of companies’ activities. The main goal is to find a balance between market security, stability, and reducing the administrative burden for companies. In 2023–2028, the focus will be on combating money laundering, preventing terrorist financing, cyber and information technology security, preventing financial crimes and fraud, and protecting consumer interests. This study is based on consumer surveys submitted to the Bank of Lithuania’s analysis of complaints about the activities of institutions, disputes with consumers, and enforcement measures applied by financial market supervision institutions. The data are obtained from the annual activity reports of the Bank of Lithuania, selecting specifically those related to the application of FinTech principles. The Bank of Lithuania, following applicable laws, applies these enforcement measures in cases where violations are identified.

Lithuania is internationally recognized as one of the European FinTech centers, but further awareness-raising is necessary. In order to strengthen this position, representatives of the Lithuanian FinTech sector should actively participate in various European Union and international events. This study also analyzes the number of users and its impact on the development of the FinTech sector.

Lithuania seeks to maintain its position as a European FinTech center by promoting a safe and reliable regulatory environment, strengthening risk management and the fight against financial crimes. The development of the FinTech sector is based on attracting talent, cooperation with higher education institutions, and attracting specialists from abroad. Capital requirements and innovative technologies stimulate the growth of the sector, and high-value-added companies increase investor interest. In recent years, Lithuania has been experiencing qualitative growth in the FinTech sector, which is helping to strengthen the country’s position in the international market.

3. Materials and Methods

Evaluating the advantages of FinTech, three main methods are used. The CRITIC method, which stands for Criteria Importance Through Intercriteria Correlation, is considered a reliable approach in multi-criteria decision-making (MCDM) due to its ability to objectively evaluate the relative importance of different criteria based on their correlation with each other. By analyzing the interrelationships between criteria, this method minimizes the impact of subjective bias, ensuring a more data-driven and robust assessment. However, the reliability of the CRITIC method can be influenced by the quality of the data and the assumption that the criteria are independent and measurable. It works well when there are sufficient data available to establish strong correlations but may struggle in situations where data are scarce or the criteria are poorly defined.

Using a combination of the COPRAS, CRITIC, and SWOT methods in this study, it is possible to attain comprehensive and reliable results, as each of these methods has its own advantages and strengths [29,30,31,32,33,34].

The COPRAS (complex proportional assessment) method is a multi-criteria assessment method that allows one to evaluate alternatives, taking into account several criteria and giving each criterion a weight according to its importance. The COPRAS method is particularly useful when it is necessary to analyze complex situations and compare various alternatives according to different criteria. The advantage of COPRAS is that it simply and effectively evaluates alternatives and allows one to make comparisons and also helps to assess how each criterion contributes to the final assessment.

The CRITIC (Criteria Importance Through Intercriteria Correlation) method is designed to distribute weights between various criteria based on their importance and mutual correlations. This allows one to objectively determine which criteria are more important, taking into account their mutual interaction and impact on other alternatives. CRITIC helps to determine objective weights for criteria, as it is based on statistical data, which provides greater reliability and accuracy for assessments.

SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis is a strategic tool that allows one to analyze both internal (strengths and weaknesses) and external (opportunities and threats) factors that may influence choices. It is especially useful when analyzing the situation of an organization or project and determining priorities and opportunities.

The application of the SWOT method is useful because it provides a broader context and allows one to assess how the organization or alternatives can take advantage of external opportunities and overcome challenges.

It follows that the use of a combination of these methods provides the following:

A comprehensive approach: The COPRAS and CRITIC methods provide the opportunity to conduct a detailed quantitative analysis, while SWOT allows one to assess both internal and external factors. This enables this study to take a comprehensive approach, including both objective and subjective factors.

More accurate assessment: The CRITIC method helps to objectively determine the importance of criteria, while COPRAS allows these weights to be applied in practical analysis. This allows for a more accurate comparison of alternatives and the selection of the best option.

Structured decision-making: SWOT analysis complements these methods, as it allows one to take into account the strategic circumstances of the organization and factors that may influence choices. The results of SWOT analysis help one select the best alternative not only based on technical indicators but also taking into account long-term opportunities and risks.

Using a combination of the COPRAS, CRITIC, and SWOT methods, this study becomes comprehensive and balanced, as each method complements the other. This allows for both quantitative and qualitative analyses, providing an objective and strategic approach to solving the problem. Limitations of studies using a combination of the COPRAS, CRITIC, and SWOT methods may arise for various reasons related to the specifics of the methods, data availability, and interpretation challenges.

First of all, subjectivity in the weights of the criteria is encountered. Although the COPRAS method allows for an objective assessment of alternatives, the weights of the criteria can be determined subjectively, especially if they are chosen based on expert opinion or incorrectly distributed priorities. This may affect the final assessments, but this article also calculated the consistency of expert opinions and found that the opinions are consistent, and the results are sufficiently reliable.

The second limitation encountered is the limited amount of data. The CRITIC method relies on statistical data to determine the importance of the criteria; therefore, if the number of data is small or they are insufficiently diversified, this method may provide inaccurate or erroneous results. This article analyzes 10 years of data; therefore, the results are reliable.

Difficulties in inter-correlations may also arise. The CRITIC method uses correlations between criteria, but for some criteria, there may not be a clear or direct correlation, so this method may not be able to reveal all important factors. This may lead to an incomplete assessment of the importance of the criteria, so the COPRAS method was chosen in addition to make the results more reliable.

Complexity of the methods: The CRITIC method requires a certain level of statistical knowledge and can be difficult to use for practical research when the data are not sufficiently detailed or their structure is not clear.

SWOT analysis is a qualitative tool, so it can be difficult to evaluate and compare facts and alternatives with quantitative indicators. This can limit decision-making when more accurate quantitative analysis is required. Therefore, in order to include information from quantitative methods, the COPRAS and CRITIC methods were chosen to be used.

When using a combination of the COPRAS, CRITIC, and SWOT methods in research, it is necessary to take these limitations into account. Only after the careful collection of data and a detailed analysis of them can accurate and comprehensive research results be presented.

3.1. Cross-Criteria Assessment Method

The cross-criteria correlation (hereinafter referred to as CRITIC) method is particularly suitable for assessing multifaceted and complex systems, such as the FinTech sector. Using this method, the importance of criteria is determined based on objective statistical data [35]. The CRITIC method helps to determine objective weights for decision criteria, thus avoiding subjective assessment and accurately assessing which factors, for example, regulatory or technological, limit the development of the sector the most.

Step 1: A decision matrix is prepared, collecting alternatives and criteria against which the FinTech sector will be assessed (e.g., economic, technological, and social factors). A decision matrix x is formed, showing the performance of different alternatives concerning the selected sub-criteria:

$${\mathit{x}=\left[{\mathit{x}}_{\mathit{i}\mathit{j}}\right]}_{\mathit{m}\mathit{x}\mathit{n}}=\left[\begin{array}{cccc}{\mathit{x}}_{11}& {\mathit{x}}_{12}& \mathbf{\cdots }& {\mathit{x}}_{1\mathit{n}}\\ {\mathit{x}}_{12}& {\mathit{x}}_{22}& \mathbf{\cdots }& {\mathit{x}}_{1\mathit{n}}\\ \mathbf{⋮}& \mathbf{⋮}& \mathbf{\ddots }& \mathbf{⋮}\\ {\mathit{x}}_{\mathit{m}1}& {\mathit{x}}_{\mathit{m}2}& \mathbf{\cdots }& {\mathit{x}}_{\mathit{m}\mathit{n}}\end{array}\right]\mathit{i}\in \left\{\mathbf{1},\right.\mathbf{2},\mathbf{\dots },\left.\mathit{m}\right\},\mathit{j}\in \left\{\mathbf{1},\right.\mathbf{2},\mathbf{\dots },\left.\mathit{n}\right\}$$

(1)

where n—number of alternatives; m—number of criteria.

Step 2: The decision matrix is normalized using linear normalization relations:

$${\mathit{r}}_{\mathit{i}\mathit{j}}={\displaystyle \frac{{\mathit{x}}_{\mathit{i}\mathit{j}}-{\mathit{x}}_{\mathit{j}}^{\mathit{m}\mathit{i}\mathit{n}}}{{\mathit{x}}_{\mathit{j}}^{\mathit{m}\mathit{a}\mathit{x}}-{\mathit{x}}_{\mathit{j}}^{\mathit{m}\mathit{i}\mathit{n}}}},\mathit{i}\in \left\{\mathbf{1},\right.\mathbf{2},\mathbf{\dots },\left.\mathit{m}\right\},\mathit{j}\in \left\{\mathbf{1},\right.\mathbf{2},\mathbf{\dots },\left.\mathit{n}\right\}$$

(2)

where i—the number of alternatives; j—the number of criteria; min—the minimum criterion value; max—the maximum criterion value.

Step 3: The population’s standard deviation (hereinafter referred to as SIGMA) ${\mathit{\sigma }}_{\mathit{j}}$ is determined from the normalized decision matrix for each ${\mathit{r}}_{\mathit{j}}$:

$${\mathit{\sigma }}_{\mathit{j}}=\sqrt{{\displaystyle \frac{\sum {\left({\mathit{r}}_{\mathit{i}\mathit{j}}-{\overline{\mathit{r}}}_{\mathit{i}\mathit{j}}\right)}^2}{\mathit{n}}}}$$

(3)

where ${\overline{\mathit{r}}}_{\mathit{i}\mathit{j}}$—mean; n—number of features.

Step 4: The correlation for each pair of normalized criteria is determined, and a symmetric matrix with element ${\mathit{R}}_{\mathit{i}\mathit{j}}$ is constructed:

$${\mathit{R}}_{\mathit{i}\mathit{j}}={\displaystyle \frac{\sum \left({\mathit{r}}_{\mathit{i}}-{\overline{\mathit{r}}}_{\mathit{i}}\right)\left({\mathit{r}}_{\mathit{j}}-{\overline{\mathit{r}}}_{\mathit{j}}\right)}{\sqrt{\sum {\left({\mathit{r}}_{\mathit{i}}-{\overline{\mathit{r}}}_{\mathit{i}}\right)}^2}\sum {\left({\mathit{r}}_{\mathit{j}}-{\overline{\mathit{r}}}_{\mathit{j}}\right)}^2}}$$

(4)

Correlation measures the relationship between two variables, showing how a change in one variable is related to a change in another variable [36]. The correlation coefficient can be positive or negative, and its values can fluctuate. The value of the correlation coefficient can be interpreted as follows:

1 or −1—very strong correlation.

0.7–0.9 or −0.7–−0.9—strong correlation.

0.5–0.7 or −0.5–−0.7—medium correlation.

0.3–0.5 or −0.3–−0.5—weak correlation.

0–0.3 or −0.3–0—very weak or insignificant correlation.

Positive correlation means that both variables increase together, while negative correlation indicates that one variable increases when the other decreases.

Step 1: Determination of the difference in determination between criteria:

$${\sum }_{\mathit{j}=1}^{\mathit{n}}\left(1-{\mathit{R}}_{\mathit{i}\mathit{j}}\right)$$

(5)

Step 2: Determination of the determination sum ${\mathit{C}}_{\mathit{j}}$, the amount of difference between criterion j:

$${\mathit{C}}_{\mathit{j}}={\mathit{\sigma }}_{\mathit{j}}{\sum }_{\mathit{j}=1}^{\mathit{n}}\left(1-{\mathit{R}}_{\mathit{i}\mathit{j}}\right)$$

(6)

The higher the value of ${\mathit{C}}_{\mathit{j}}$, the greater the amount of information contained in a certain criterion; therefore, the criterion has a greater relative importance.

Step 3: Determination of the determination criterion weights ${\mathit{w}}_{\mathit{j}}$:

$${\mathit{w}}_{\mathit{j}}={\displaystyle \frac{{\mathit{c}}_{\mathit{j}}}{{\sum }_{\mathit{j}=1}^{\mathit{n}}{\mathit{C}}_{\mathit{j}}}}$$

(7)

The advantages of the CRITIC method for FinTech assessment are as follows:

Objectivity: Criterion weights are determined based on statistical data, not subjective expert assessments.

Criteria independence: Correlated criteria are given less weight, thus avoiding double impact.

Dynamics: The CRITIC method can be applied to various sectors; therefore, it can be adapted to both regional and global FinTech markets.

When all criteria have their weights, it is possible to make a final assessment of the FinTech sector, reflecting the objective impact of different criteria on the development of the sector.

3.2. Multi-Criteria Assessment Method

The multi-criteria complex proportional assessment (hereinafter referred to as COPRAS) method is used to assess various factors and their importance in order to determine the optimal directions of activity and propose strategic solutions for the development of the enterprise [37]. This method is well suited for assessing the FinTech sector, as it allows for the assessment of many different criteria and the identification of the most effective development strategies.

Step 1: Constructing a weighted normalized matrix:

$${\widehat{\mathit{x}}}_{\mathit{i}\mathit{j}}={\displaystyle \frac{{\mathit{x}}_{\mathit{i}\mathit{j}}}{{\sum }_{\mathit{i}=1}^{\mathit{m}}{\mathit{x}}_{\mathit{i}\mathit{j}}}}$$

(8)

Step 2: Calculating normalized weighted values for each criterion:

$${\tilde{\mathit{x}}}_{\mathit{i}\mathit{j}}={\widehat{\mathit{x}}}_{\mathit{i}\mathit{j}}·{\mathit{w}}_{\mathit{j}}$$

(9)

Step 3: Determining the sum of the normalized, weighted values of maximizing criteria:

$${\mathit{S}}_{+\mathit{i}}={\sum }_{\mathit{j}=1}^{\mathit{n}}{\tilde{\mathit{x}}}_{+\mathit{i}\mathit{j}}$$

(10)

Step 4: Determining the sum of the normalized, weighted values of minimizing criteria:

$${\mathit{S}}_{-\mathit{i}}={\sum }_{\mathit{j}=1}^{\mathit{n}}{\tilde{\mathit{x}}}_{-\mathit{i}\mathit{j}}$$

(11)

Step 5: Determining the relative weights of alternatives:

$${\mathit{Q}}_{\mathit{i}}={\mathit{S}}_{+\mathit{i}}+{\displaystyle \frac{{\mathit{S}}_{-\mathit{m}\mathit{i}\mathit{n}}{\sum }_{\mathit{i}=1}^{\mathit{m}}{\mathit{S}}_{-\mathit{i}}}{{\mathit{S}}_{-\mathit{i}}·{\sum }_{\mathit{i}=1}^{\mathit{m}}\frac{{\mathit{S}}_{-\mathit{m}\mathit{i}\mathit{n}}}{{\mathit{S}}_{-\mathit{i}}}}},\mathit{w}\mathit{h}\mathit{e}\mathit{r}\mathit{e} {\mathit{S}}_{-\mathit{m}\mathit{i}\mathit{n}}=\underset{\mathbf{i}}{\mathit{m}\mathit{i}\mathit{n}}{\mathit{S}}_{-\mathit{i}}$$

(12)

Step 6: Calculating the percentage value of each alternative ${\mathit{U}}_{\mathit{i}}$, which allows the priority activity curves of the sector to be determined:

$${\mathit{U}}_{\mathit{i}}={\displaystyle \frac{{\mathit{Q}}_{\mathit{i}}}{{\mathit{Q}}_{\_\mathit{m}\mathit{a}\mathit{x}}}}·\mathbf{100}\mathbf{\%}$$

(13)

Step 7: Determining the priority of FinTech sectors. The higher the U_i value, the better the sector meets all the criteria. A priority order of FinTech sectors is also formed according to compliance with the criteria.

The COPRAS method is a multi-criteria assessment tool designed to determine priority areas of activity based on various factors, such as economic and technological indicators. This method helps to distinguish the most important criteria, evaluate alternatives, and propose optimal solutions for the development of the sector. Using the COPRAS method for assessing the FinTech sector, it is possible to accurately determine which sector’s business lines best meet the established criteria, thereby promoting the sustainable growth of the sector.

3.3. SWOT Analysis Method

SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis is a strategic tool that helps assess internal and external factors that may affect operations.

The first step is to collect and analyze data about the FinTech market and the external environment. This may include the following:

• A company’s financial indicators;
• The competitive environment;
• Industry trends and innovations;
• Customer reviews, market research, and surveys.

A company’s internal processes, organizational structure, employee competencies, and technological infrastructure may also be evaluated. An analysis of internal factors (strengths and weaknesses) may also be conducted; these are factors that can be directly controlled. These can be resources, capabilities, processes, or culture (

Table 1. Main questions for SWOT analysis.

Strengths (S)	Weaknesses (W)
What does the FinTech sector do better than the competition? What are its unique advantages? What resources (human, financial, technological, etc.) help the sector operate successfully?	What does the FinTech sector do worse than its competitors? What are its organizational problems: limited resources or inefficient technology? Is it possible to lose market share due to internal weaknesses? An analysis of external factors (opportunities and threats) may be conducted; these are external forces that are less controllable but must be taken into account when planning a strategy.
Opportunities (O)	Threats (T)
What market trends, technologies, or changes might provide new opportunities? Are there new markets to expand into? What regulatory or economic trends could support growth?	What external factors can disrupt operations? How can competitors affect the market position of the FinTech sector? Is there a possibility that regulations or laws will change, which could be detrimental?

Source: compiled by the authors.

).

Based on the SWOT analysis in this study, it is possible to understand the current state of the market, make decisions based on both internal structure and external factors, and develop effective strategies to take advantage of strengths and opportunities while reducing the impact of weaknesses and threats.

4. Results

4.1. Factors Influencing the Development of FinTech

After the discussion about the factors influencing the development of the FinTech sector, these main factors (criteria) and their alternatives are presented in

Table 2. Alternatives and criteria for FinTech development—influencing factors.

Criteria/Alternatives	Number of FinTech Companies	Financial Indicators (EUR)	Capital Requirement (Thousand EUR)	Number of Users (Thousand EUR)	Complaints Filed	Impact Measures
Minimize/maximize	Max	Max	Max	Max	Min	Min
EMI	226	401,072	350	5678	10	102
PSP	331	93,512	93	2,974,073	0	28
PPP	71	88,246	40	23,774	5	9
CFP	22	230,242	5000	591,223	195	4
WealthTech	16	30,293	123,085	6176	0	10
Digital Banking	13	609,211	1000	2520	191	34

Source: compiled by the authors, based on the report data of the Bank of Lithuania ([26,28]) and the reports of the State Data Agency in 2023 and calculation results.

.

FinTech development criteria are divided into maximizing and minimizing criteria. Maximizing criteria are those whose values should be as high as possible, such as the number of companies and financial and capital ratios. Minimizing criteria are those whose values should be as low as possible, such as complaints or enforcement actions.

This information was compiled based on 2023 data to ensure that the most recent and reliable secondary sources were used. The analyzed information is based on freely available data and the 2023–2028 FinTech sector development guidelines prepared by the team of specialists of the Ministry of Finance of the Republic of Lithuania.

4.2. Evaluation of Criteria Using CRITIC Method

Based on the data presented in Table 1, the CRITIC method was applied to determine the importance of each criterion. First, the maximum and minimum values were calculated using the Excel functions $\mathit{f}\mathit{x}=\mathit{M}\mathit{A}\mathit{X}$ and $\mathit{f}\mathit{x}=\mathit{M}\mathit{I}\mathit{N}$ (see step 1 in

Table 3. Performance and standard deviation of minimum and maximum value alternatives.

Criteria/Alternatives	Number of FinTech Companies	Financial Indicators (EUR)	Capital Requirement (Thousand EUR)	Number of Users (Thousand EUR)	Complaints Filed	Impact Measures
Step 1
MIN	13	30,293	40	2520	0	4
MAX	331	609,211	123,085	2,974,073	195	102
Step 2
EMI	0.6698	0.6405	0.0025	0.0011	0.0513	1
PSP	1	0.1092	0.0004	1	0	0.2449
PPP	0.1824	0.1001	0	0.0072	0.0256	0.0510
CFP	0.0283	0.3454	0.0403	0.1981	1	0
WealthTech	0.0094	0	1	0.0012	0	0.0612
Digital Banking	0.3061	1	0.0078	0	0.9795	0
Step 3
$\mathrm{SIGMA} {\sigma }_j$	0.4213	0.3869	0.4044	0.3991	0.5016	0.3739

Source: compiled by the authors, based on calculation results.

); then, the performance of each alternative was calculated according to the selected sub-criteria: (see step 2 in Table 3).

A criterion’s standard deviation SIGMA ${\mathit{\sigma }}_{\mathit{j}}$ (see step 3, Table 3) is calculated by the Excel function $\mathit{f}\mathit{x}=\mathit{S}\mathit{T}\mathit{D}\mathit{E}\mathit{V}$.

The correlation ${\mathit{R}}_{\mathit{i}\mathit{j}}$ is derived (see Equation (4)) using the Excel function $\mathit{f}\mathit{x}=\mathit{C}\mathit{o}\mathit{r}\mathit{r}\mathit{e}\mathit{l}\mathit{a}\mathit{t}\mathit{i}\mathit{o}\mathit{n}$ (see

Table 4. Correlation between criteria.

Criteria/Alternatives	Number of FinTech Companies	Financial Indicators (EUR)	Capital Requirement (Thousand EUR)	Number of Users (Thousand EUR)	Complaints Filed	Impact Measures
EMI	1	−0.1537	−0.3738	0.7462	−0.5493	0.5087
PSP	−0.1537	1	−0.4617	−0.3404	0.6266	0.5115
PPP	−0.3738	−0.4617	1	−0.2502	−0.3078	−0.2978
CFP	0.7462	−0.3404	−0.2502	1	−0.2153	−0.1184
WealthTech	−0.5493	0.6266	−0.3078	−0.2153	1	−0.2321
Digital Banking	0.5087	0.5115	−0.2978	−0.1184	−0.2321	1

Source: compiled by the authors, based on calculation results.

).

4.3. Correlation Analysis Between Criteria

The number of FinTech companies and the number of users (0.7462) has a positive, moderate correlation. This shows that as the number of FinTech companies grows, so does the number of users.

The number of FinTech companies and impact measures (0.5087) has a positive, weak correlation. This means that as the number of FinTech companies increases, so does the use of leverage, but this relationship is not very strong.

PSPs and filed complaints (0.6266) have a positive, moderate correlation. This shows that when the indicators of the Ministry of Internal Affairs improve, the number of reported complaints also increases.

WealthTech and financial indicators (0.6266) have a positive, moderate correlation. As WealthTech’s financial ratios increase, so do the company’s ratios, indicating a strong relationship between these ratios.

EMIs and filed complaints (−0.5493) have a medium, negative correlation. This means that the number of filed complaints decreases with an increase in the activity indicators of the EMI.

WealthTech and some users (−0.2153) have a very weak, negative correlation. This shows that when the number of users declines, the impact on WealthTech is negligible.

Digital banking and the number of users (−0.1184) have no significant correlation.

A positive correlation means that an increase in one criterion is associated with an increase in another criterion, while a negative correlation means that an increase in one criterion is associated with a decrease in the other.

For the derived correlation, for each pair of normalized criteria, a symmetric matrix (see

Table 5. Determination difference and weights.

Criteria/Alternatives	Number of FinTech Companies	Financial Indicators (EUR)	Capital Requirement (Thousand EUR)	Number of Users (Thousand EUR)	Complaints Filed	Impact Measures
EMI	0	1.1537	1.3738	0.2538	1.5493	0.4913
PSP	1.1537	0	1.4617	1.3404	0.3734	0.4885
PPP	1.3738	1.4617	0	1.2502	1.3078	1.2978
CFP	0.2538	13404	0.2502	0	1.2153	1.1184
WealthTech	1.5493	0.3734	1.3078	1.2153	0	1.2321
Digital banking	0.4913	0.4885	1.2978	1.1184	1.2321	0
Sum	4.8219	4.8177	6.6913	5.1782	5.6780	4.6281
SIGMA ${\sigma }_j$	0.4213	0.3869	0.4044	0.3991	0.5016	0.3739
The sum of the determination difference $C_j$	2.0315	1.8638	2.7058	2.0665	2.8479	1.7304
$w_j$	0.1534	0.1407	0.2043	0.1560	0.2150	0.1306

Source: compiled by the authors, based on calculation results.

) is created—determination difference (see Equation (5)). The resulting sum is multiplied by the previously calculated SIGMA ${\mathit{\sigma }}_{\mathit{j}}$, and the determination difference sum ${\mathit{C}}_{\mathit{j}}$ is obtained (see Equation (6)). The greater the value of the difference in determination, the greater the relative importance of the criterion—these are capital requirements and filed complaints.

From the set amount of determination (see Table 5), the weights ${\mathit{w}}_{\mathit{j}}$ of the determination criterion are derived (see Equation (7)).

The CRITIC method showed that the most important criteria for the development of the FinTech sector are the initial capital requirements and the number of reported complaints. These criteria have the greatest influence on the sector’s efficiency and further development. The weightings of the criteria calculated in this study help us to better understand how various factors affect the sector, which allows for more informed decisions to be made in terms of strategic actions.

4.4. Priority Order of FinTech Sectors

Normalized values are calculated taking into account the weight of each criterion (see Equations (8) and (9)). The normalization formula is often used in the COPRAS methodology to facilitate the comparison of different criteria, even if they are measured in different units.

The weighted value is normalized; the sum of the normalized, weighted values of the maximizing and min criteria are determined; a priority order is made; and the FinTech sector is arranged according to the best criteria (see

Table 6. Priority queue.

Criteria/Alternatives	${\mathit{S}}_{+\mathit{i}}$	${\mathit{S}}_{-\mathit{i}}$	${\mathit{S}}_{-\mathit{m}\mathit{i}\mathit{n}}/{\mathit{S}}_{-\mathit{i}}$	${\mathit{Q}}_{\mathit{i}}$	${\mathit{U}}_{\mathit{i}}$	Priority
EMI	0.0907	0.0766	0.0912	0.1041	29.912	4
PSP	0.2127	0.0196	0.3571	0.2653	76.218	2
PPP	0.0257	0.0090	0.7790	0.1404	40.323	3
CFP	0.0608	0.1073	0.0651	0.0703	20.205	6
WealthTech	0.2009	0.0070	1	0.3481	100	1
Digital Banking	0.0636	0.1262	0.0554	0.0718	20.623	5

Source: compiled by the authors, based on calculation results.

) with the help of Excel functions $\mathit{f}\mathit{x}=\mathit{R}\mathit{a}\mathit{n}\mathit{k}$ (see Equations (10)–(13)).

WealthTech (investment and wealth management companies)—This sector was rated as the best in 2023, according to all criteria. It has strong results in terms of capital requirements and ${\mathit{S}}_{-\mathit{m}\mathit{i}\mathit{n}}$, which means that it performs well in both maximization and minimization criteria.

• PSPs (payment service providers) took second place, having strong financial indicators and other reasonably good criteria.
• PPPs (peer-to-peer lending platforms) had average results but performed well in minimizing complaints and other negative criteria.
• EMIs (electronic money institutions)—${\mathit{U}}_{\mathit{i}}$ = 29,912, priority: 4. Electronic money institutions were in fourth place because they have an average position according to the maximization and minimization criteria.
• Digital banking was ranked fifth due to lower financial indicators and higher complaint values.
• CFPs (crowdfunding platforms) took the last place, because their values were lower in all criteria.

Based on the priority order, investment and wealth management companies (WealthTech) were recognized as the best FinTech sector in 2023, as they achieved the best results in terms of capital requirements, financial performance, and minimal complaints.

4.5. WealthTech SWOT Analysis

SWOT analysis was conducted for the WealthTech sector, with the following highlights in each category:

• Strengths (S):
• Technological Advances: Uses advanced technologies such as artificial intelligence (AI) and big data analytics to improve investment performance.
• Accessibility: Digital platforms make it easier for the public to access investment services.
• Lower costs: Automated solutions such as robot advisors reduce fees compared to traditional wealth management firms.
• Weaknesses (W):
• Technological dependence: Increased risk due to potential technological failures and cyber threats.
• Lack of personalization: Automated solutions cannot always offer the personalized advice that financial advisors can provide.
• Regulatory complexity: It is difficult to comply with the regulatory requirements of the financial sector in various countries.
• Opportunities (O):
• Market development: There are still few available wealth management services in emerging markets, so WealthTech has great growth potential.
• Attracting the younger generation: Millennials and Gen Z tend to use digital platforms for investment.
• The development of artificial intelligence: AI capabilities allow us to offer more personalized and efficient solutions.
• Threats (T):
• Tighter regulation: There may be stricter regulatory requirements that limit innovation.
• Cyber-attacks: Cyber threats can damage the reputation and credibility of the sector.
• Competition: High competition from both traditional banks and other FinTech companies.

WealthTech is a strong, innovative, and dynamic FinTech segment that is characterized by a wide range of services, technological advances, long-term return potential, and risk diversification opportunities. Meanwhile, EMIs, PSPs, digital banking, PPPs, and CFPs are more specialized sectors that focus on payments or loans but do not offer the deeper investment and wealth management value of WealthTech.

5. Discussion

Financial technology (FinTech) is one of the fastest-growing industries worldwide, and Europe has a major role to play in this. FinTech encompasses a range of technologies and innovations that help improve the efficiency, accessibility, and security of financial services [38]. The financial technology sector in Europe has undergone significant changes over the past decade, and these technologies have become a key factor shaping the future of the financial sector. The development of FinTech has changed the essence of traditional finance and has had a significant impact on the efficiency of financial decision-making [39].

The development of financial technologies in Europe began only in the 20th century. Then, the first attempts to use technology in the financial sector were recorded, focusing on online banking services and the emergence of electronic payments [40,41]. This led to a situation where traditional banks must quickly adapt to the constantly changing environment, reduce their costs, and meet customers’ needs and expectations [42]. Online banking, which allows users to perform financial transactions online, was the first step towards the development of modern FinTech.

At the beginning of the 21st century, FinTech companies began to emerge in Europe, which sought to use technology in the financial sector as effectively as possible and replace traditional financial services with newer, more advanced, and more accessible ones. It was time for the rise of cryptocurrencies, blockchain technology, robo-advisors, and peer-to-peer lending platforms, so these technologies streamlined traditional financial processes and enabled a broader spectrum for users to participate in the global economy [43]. FinTech development led to more accessible, innovative, improved, competitive, secure, inclusive, and beneficial financial services [39]. One of the first significant events was the success of PayPal [44]. This created electronic payment system allowed users to transfer money safely and quickly online, which led to an increase in the availability of financial services and a reduction in prices. This transformation from digitalization to digitization led to the use of new technologies and the restructurization of banking industries [45]. People started to place more trust in banks while they transfer their money. It was then that the evolution of services provided by traditional banks began [46].

In Europe, the FinTech sector has rapidly developed as technological innovations such as blockchain, artificial intelligence, big data, and cybersecurity have been applied to the financial sector, which are accelerating globally. Canada, the UK, Germany, France, Switzerland, and Sweden have seen the largest FinTech growth streams, making these countries the European FinTech hub, attracting both domestic and international investors [47,48].

Currently, FinTech technologies are developing rapidly. The blockchain technology used in the financial sector provides the opportunity to provide safer and more efficient financial services, reducing the number of intermediaries and transaction costs, because the most important criteria in blockchain technology are security and trust [49,50]. The authors of [50,51,52,53] argue that operations in the context of the use of blockchain can lead to a reduction in operating costs, so these operations are cheaper and more efficient. Blockchain technology can help create decentralized systems that will allow transactions to be carried out without intermediaries, which will reduce costs and improve the speed of transactions. Artificial intelligence can be used to automate financial processes, predict market trends, and improve customer service. The application of artificial intelligence in the finance sector leads to the deep transformation of the financial services industry and helps to predict bankruptcy, stock prices, and oil prices to manage one’s portfolio, and this is one of the best ways to ensure an anti-money laundering process [54,55,56]. Artificial intelligence is a good tool for financial analysis, forecasting, and risk management because by using artificial intelligence tools, people can analyze a broad range of data, find the necessary results, and make predictions more quickly and easily [57,58]. Big data allows FinTech companies to more accurately analyze consumer behavior, provide personalized services, and manage risks more effectively [59]. In the financial sector, big data is used especially for fraud detection and risk assessment [60], so the financial sector becomes more sustainable [61].

The digital economy has grown due to the impact of FinTech development, and the FinTech sector in Europe is likely to continue to grow. It is predicted that the FinTech sector will become an even more important component of the economy in the next few years, as more and more consumers choose digital financial services [62,63]. Digital finance helps improve the accessibility of financial services, ensures the security and stability of financial services, and helps avoid fraud and money laundering [64,65,66]. Companies use financial technologies to offer consumers competitive services. Traditional banks are forced to cooperate with FinTech companies in order to compete in the market and attract consumers. It follows that the FinTech sector in Europe has great potential and prospects. There are many opportunities to apply innovations in the provision of financial services. However, the successful development of FinTech will depend on how quickly the financial sector adopts and applies new technologies.

6. Conclusions

The FinTech sector is important for Lithuania’s economy, as the changing needs of society and technological progress promote digitization and innovation in the financial sector. The analyzed theoretical models showed that FinTech innovations not only simplify the provision of traditional financial services but also open new opportunities and have a positive impact on the Lithuanian economy and contribute to the creation of greater added value. Technological solutions allow financial services companies to become more efficient, reduce costs, and expand their range of services. Due to the favorable legal environment and active promotion of innovation, Lithuania has become an attractive country for FinTech companies, but challenges remain, especially related to cybersecurity and regulatory compliance.

The methodologies used in this study, including quantitative ones, were effective in evaluating the development of the Lithuanian FinTech sector. Quantitative data on the number of licenses, investments, and sector growth provided an objective picture of the sector’s development. The methods used, such as CRITIC and COPRAS, allowed us to determine objective criteria and priorities in the FinTech sector, assessing which factors (e.g., regulatory restrictions, technological infrastructure) are hindering the growth of the sector the most. These methodologies made it possible to consistently identify the main barriers to development, and the SWOT analysis highlighted strengths and weaknesses, allowing for strategic recommendations for the improvement of the sector.

The results of this study highlighted that the FinTech sector in Lithuania is primarily shaped by factors such as favorable regulation, the strengthening of the innovation ecosystem, and the attraction of international investments. These elements are essential for fostering growth and creating a competitive landscape. However, despite these positive influences, the sector still faces significant challenges, including a shortage of skilled labor, the need to enhance cybersecurity measures, and the ongoing need for regulatory frameworks to evolve alongside emerging technologies.

The key barriers to accelerated growth in the FinTech sector are closely tied to the pace of technological advancements, regulatory uncertainty, and difficulties in attracting and retaining top talent. The importance of FinTech cannot be overstated, as it is central to driving digital transformation, improving financial accessibility, and ensuring economic resilience. In light of this study’s findings, it is recommended that more focus be placed on educational programs that train technology specialists, foster stronger cooperation between the public and private sectors, and further refine the regulatory environment. These actions are critical for ensuring the sustainable and secure development of FinTech, which is increasingly indispensable for both economic innovation and global competitiveness.