4.1 Production outputs and performance analysis
Table 2 shows the database search results: 1,792 articles for the period from 1999 to 2023 and the corresponding quantitative analysis of the main information. Even though the data extraction was conducted on Dec 31, 2022, there is one article is for 2023 since WoS accepted advance online publication. The total number of authors within the collections was 1,099. These articles were published by 738 sources for all document types (journal articles, book chapters, etc.). In total, the collection cited 80,507 references. The table also illustrates a large number of international co-authorships, calculated as 32.7%. For the last 23 years, the annual growth rate of the number of publications is 2.93%. The document average age is 1.56 years, which indicates that most of the publications are recent; thus, the dataset can represent the latest development of the FinTech domain. The table also indicates that 4,427 author keywords were found and 1,946 keywords plus were generated from titles and keywords.
Table 2
Description of the dataset
Description | Results | Description | Results |
MAIN INFORMATION ABOUT DATA | | AUTHORS | |
Timespan | 1999:2023 | Authors | 4,272 |
Sources (Journals, Books, etc) | 738 | Authors of single-authored docs | 308 |
Documents | 1,792 | AUTHORS COLLABORATION | |
Annual Growth Rate % | 2.93 | Single-authored docs | 350 |
Document Average Age | 1.56 | Co-Authors per Doc | 2.87 |
Average citations per doc | 9.99 | International co-authorships % | 32.7 |
References | 80,507 | DOCUMENT TYPES | |
DOCUMENT CONTENTS | | Article | 1,512 |
Keywords Plus (ID) | 1,946 | Article; Early Access | 173 |
Author's Keywords (DE) | 4,427 | | |
| | Article; Proceedings Paper | 9 |
Article; Book Chapter | 6 | Review | 79 |
Article; Data Paper | 2 | Review; Early Access | 11 |
Figure 3 shows the progress of the 1,792 (1685) articles found through the WoS search in the dataset. It also shows the yearly citation numbers for the period of 1999–2022. During this period of more than 30 years, scientific production in FinTech research increased at a constant rate in view of historiographic mapping of knowledge domains (Garfield, 2004). Based on Kuhn’s (2012) theory of scientific evolution, the scientific production (blue line) indicates there are three phases: a slow growing period (1999–2016) in which the number of publications increased from two in 1999 to eight in 2016, thus named as the “initiation” period; a medium growing period (2017–2019), named the “development” period, in which the number of publications increased from 48 in 2017 to 147 in 2019; and a fast-growing period (2020-now) in which the publication grew from 290 in 2020 to 556 in 2022, named accordingly as “fast-growing”. This classification is used later in field evolution analysis. Since the period of 2020–2022 covers the COVID-19 period, future research can use these results for the study of the pandemic crisis.
The number of citations increased exponentially for articles published between 2016 and 2018, yearly citation numbers grew from 167 (2016) to 3736 (2018), which equals 1864 citations per year, 11.4 citations per year per article. The number of citations peaked in 2018. Starting from 2019, the number of citations flattened out. On average, between 2019 and 2022, the number of citations received were at 2955, 3729, 3077, and 1257. The average number of citations is 2754 per year, which 1.9 times per article per year. The citable years were not considered in regard to these calculations. Further analysis will be conducted in subsequent sections of the research.
The first article published in the research target was “Business as Usual and Rare Events: The odd couple of risk management coming together” published in the Journal of Portfolio Management (Focardi, 1999). Here, the author indicated that derivatives had shown a surprising level of success in eliminating risk in business-as-usual conditions with failures in rare events. Financial technology plays a key role in the success of risk management. By applying financial technology to risk management, the author argued, it is more robust than previously thought.
Even though a number of scholars (J. Wang et al., 2022) believe that the FinTech research started in the early 1990s, this research found that it began in the late 1990s or, as Fig. 2 shows more specifically, in 1999. This result is also inconsistent with Tepe et al.’s (2022, p1) the assertion from other scholars, who indicated that FinTech only “entered the literature five years ago” (Tepe et al., 2022).
4.2 The analysis of field evolution
To predict future research directions, the research created three time slices to represent three phases of FinTech studies (refer to the section 4.2 of production performance). Figure 8 illustrates the evolution of FinTech research between 1999 and 2022. The figure shows that, in the initiation phase between 1999 and 2016, FinTech research was focused on three conversations: financial markets, FinTech, and finance. In the development phase between 2017 and 2019, the topics become heterogeneous, with many new popular conversations, such as systemic risk, digitalization, artificial intelligence, innovation, financial regulation, big data, cryptocurrency, China, smart contracts, financial services, and India. Two trending topics in the initiation phase evolved from financial markets to big data and from finance to financial inclusion; the FinTech topics remained the same in the development phase.
In the fast-growing phase (2020–2022) of Fintech research, there was a smaller number of hot topics in comparison to the development phase, as shown in Fig. 8. However, it can also be seen that FinTech itself (higher bar in right column) became a hotter topic in comparison to the two previous phases. Blockchain was the second hottest conversation, and the third one was artificial intelligence. In addition to these three main topics, three small topics—innovation, trust, and digitalization—appeared after classifying financial technology/technologies as FinTech. The figure shows the evolution of each single topic from phase two to phase three. For example, trust, as one of the latest topics, evolved from cryptocurrency; blockchain evolved from several topics of phase two, such as cryptocurrency and smart contracts. Conversations in phase three have the potential to be future research directions.
4.3 The thematic analysis by three phases
Following Schneider’s (2009) evolutionary theory of field development, stage one (initiation) involves the introduction of new objects and phenomena as the subject matter for a new research area. The second stage (development) entails the development of a toolbox of methods and techniques that can be applied to the field. This development in methodology has resulted in a greater understanding of the range of objects and phenomena that fall within the jurisdiction of the new field at this point in time. In the third stage (fast-growing), most of the specific knowledge is generated, as well as the majority of original research publications. In the third stage, new research methods are applied to objects or phenomena in order to obtain a deeper understanding of them. This section will engage in thematic analysis for each phase established in section 4.1. For each cluster in each phase, this section provides: (1) the top impact keywords (i.e., the terms that received the highest citation counts), (2) the top trending keywords (i.e., the most recent used terms that appeared in the articles), and (3) the indicative disciplines. The section also delivers key measures for each cluster including callon centrality, callon density, ranked centrality, ranked density, and cluster frequency (refer to sunsection 3.3 for calculation).
4.3.1 Thematic analysis for initiation phase (1999–2016)
By using the strategy map, researchers are able to identify and visualize conceptual subdomains. In a two-dimensional strategy diagram, co-keywords and h-index indicators are used to generate a thematic map (Mühl & de Oliveira, 2022). A set of research themes is therefore displayed on the strategy map. Networks of words are formed when keywords appear together the most. Whenever the keywords appear together many times, a high-density network is formed. A network of words is then related to another network of keywords based on the relationships established by the software. As a result, keywords are grouped according to their subject fields, and themes are distributed based on their centrality and density. By measuring centrality, researchers can examine how closely a keyword network interacts with others. By measuring density, researchers can determine how closely related the words are within a network. By examining the centrality and density of each theme, researchers can classify them into four groups (Duan, 2023b; López-Robles et al., 2019).
This study deployed the Louvain algorithm for clustering authors’ keywords. The Louvain method detects communities in large networks. This algorithm maximizes a modularity score for each community, where modularity quantifies how well nodes are assigned to communities. This is determined by comparing how densely connected nodes are within a community with how densely connected they would be if they were randomly distributed. The application of this algorithm has been growing rapidly and has been recommended by scholars (Cai et al., 2019; Wang & Koopman, 2017)
Figure 9 shows three topics/themes between 1999 and 2016 on a strategy map. The theme “finance technology” is classified as a niche theme, which indicates the topic is well developed but requires future research on its relationship with the field. The financial markets topic is a basic theme, which requires further self-development despite having a close association with the FinTech field. The topic FinTech is categorized as an emerging theme, which, on a strategic map, indicates the topic needs both self-development and relationship development with the FinTech field. The reason for that is because the new era of “FinTech”, as a commonly accepted abbreviation of the research field, was only just emerging at the beginning of the time period (Arner et al., 2015; Schueffel, 2016; Schindler, 2017).
Table 6 shows the characteristics of those three topics: FinTech, finance technology, and financial markets. From a centrality (degree of relevance) perspective for the FinTech field, FinTech and finance technology have the same score and rank, while the frequencies are different (three for FinTech and four for finance technology). The frequencies are different (three for FinTech and four for finance technology). This indicates that, in this period, scholars were using “finance technology” more frequently than FinTech, even though they had an identical meaning. The density (degree of development) perspective can further confirm the above point since the theme of “finance technology” is more developed (62.5 vs 33.3 in density column). “Financial markets”, as a research topic during this period, was more relevant to FinTech studies than the other two topics (0.167 vs 0.00, and 3 vs 1.5 in density and ranked density columns). Table 6 also shows that the frequency of clusters is between 3 (financial markets) and 4 (FinTech and finance).
Table 3
Clusters of the thematic map of FinTech research classified by author keywords between 1999 and 2016 (ranked by centrality and density)
Label (first keyword of the cluster) | Callon Centrality | Callon Density | Ranked Centrality | Ranked Density | Cluster Frequency |
FinTech | 0.083 | 25.000 | 2 | 1 | 4 |
Financial Markets | 0.167 | 33.333 | 3 | 2 | 3 |
Finance technology | 0.000 | 62.500 | 1 | 3 | 4 |
4.3.2 Thematic analysis for development phase (2017–2019)
By setting the minimum appearance to 5 and applying the Louvain clustering algorithm to calculate the top 500 author keywords, the thematic clustering detected thirteen clusters, as depicted in Fig. 10. Each cluster is labelled with the top three most dominant keywords in the cluster when the cluster contains more than three keywords.
-
Three niche themes: “accountability, digital platforms, and financial crisis”; “credit risk, credit scoring, and systemic risk”; and “data analytics and e-commerce”. These themes, positioned in the upper-left quadrant, are well-developed, standalone themes that insignificantly influence the other themes.
-
Three motor themes: “banking, financial market, and FinTechs”; “innovation, financial inclusion, and finance”; and “big data, regulation, and cloud computing”. These themes, positioned in the upper-right quadrant, are both well,developed and important to the other themes.
-
Four basic themes: “China, P2P lending, and shadow banking”, “banks, cryptocurrencies, and mobile banking”, “fintech, crowdfunding, and artificial intelligence”, and “blockchain, cryptocurrency, and bitcoin”. These themes, positioned in the lower-right quadrant, are basic or transversal themes and of importance to the FinTech field but not well developed.
-
Three emerging themes: “internationalization”, “distributed ledger, smart contracts, and corporate governance”, and “entrepreneurial finance, economic growth, and entrepreneurship”. These themes are positioned in the lower-left quadrant which are weakly developed and marginal important to FinTech field.
Table 7, using the first words of the clusters as labels with thematic classification results, illustrates the characteristics of these thirteen clusters. From strong to weak, the order of ranked relevance degree (centrality) to the field for the clusters is: Blockchain, Innovation, Fintech, Banks, Banking, Big data, China, Data analytics, Credit risk, Entrepreneurial finance, Accountability, Distributed ledger, and Internationalization. The order, from strong to weak, of ranked self-development degree (density) is: Accountability, Credit Risk, Data analytics, Banking, Innovation, Big data, Entrepreneurial finance, Distributed ledger, Banks, China, Internationalization, and FinTech. The cluster frequency column illustrates the number of times the keyword occurred, and the order is FinTech(179), Blockchain(130), Innovation(111), China(52), Big data(40), Banks(32), Banking(25), Credit risk(18), Accountability(13), Entrepreneurial finance(12), Distributed ledger(11), Data analytics(4), and Internationalization(2). A keyword's frequency represents the number of articles that address a particular subject.
Table 4
Clusters of the thematic map between 2017 and 2019 of top 500 of author keywords (ranked by centrality and density)
Cluster No. | Label (theme) | Callon Centrality | Callon Density | Ranked Centrality | Ranked Density | Cluster Frequency |
1 | FinTech (basic) | 2.196 | 49.566 | 11 | 2 | 179 |
2 | Blockchain (basic) | 3.725 | 48.004 | 13 | 1 | 130 |
3 | Innovation (motor) | 3.031 | 59.847 | 12 | 9 | 111 |
4 | Big Data (motor) | 0.919 | 59.394 | 8 | 8 | 40 |
5 | China (emerging) | 0.764 | 53.508 | 7 | 4 | 52 |
6 | Banking (motor) | 1.073 | 61.905 | 9 | 10 | 25 |
7 | Banks (basic) | 1.153 | 55.177 | 10 | 5 | 32 |
8 | Entrepreneurial Finance (emerging) | 0.408 | 58.333 | 4 | 7 | 12 |
9 | Credit Risk (niche) | 0.486 | 69.643 | 5 | 12 | 18 |
10 | Distributed Ledger (emerging) | 0.083 | 55.208 | 2 | 6 | 11 |
11 | Accountability (niche) | 0.250 | 76.389 | 3 | 13 | 13 |
12 | Data Analytics (niche) | 0.500 | 62.500 | 6 | 11 | 4 |
13 | Internationalization (emerging) | 0.000 | 50.000 | 1 | 3 | 2 |
| Average | 1.122 | 58.421 | | | 48 |
Table 8 shows author keywords and their appearance in each cluster. The focus of each cluster can be conceptualized based on the content of keywords and their frequency. Cluster 1 (FinTech) pertains to the generic FinTech literature. With 19 keywords, it covers almost every aspect of the FinTech field from crowdfunding, artificial intelligence, financial regulation, peer-to-peer lending, machine learning, digital currency, equity crowdfunding, financial intermediation, text mining, continuance intention, convolutional neural networks, financial innovation, herding, sharing economy, social media, technical analysis, and more. Containing 20 keywords, cluster 2 concentrates on the application of blockchain. Its related topics are cryptocurrency, bitcoin, digitalization, competition, smart contract, digital economy, Ethereum, initial coin offering, regulations, trust, banking sector, and more. Cluster 3, 25 keywords, is about FinTech innovation in relation to financial inclusion, finance, regtech, financial services, business models, mobile payment, digital banking, efficiency, financialization, governance, lending, and more. Cluster 4, consisting of 13 keywords, is related to the application of big data. Key components include regulation, cloud computing, financial institutions, risk management, and more.
With 15 keywords, cluster 5 is associated with FinTech development in China. Topics include P2P lending, shadow banking, startups, marketplace lending, regulatory sandbox, and more. Cluster 6 (nine keywords) centres on FinTech in banking and is linked to financial market, business model, digital transformation, and more. Cluster 7 (11 keywords) is about FinTech in banks. The associated topics include cryptocurrencies, mobile banking, India, mobile money, adoption, and more. With four keywords, cluster 8 is related to entrepreneurial finance, entrepreneurship, economic growth, and more. Cluster 9, containing six keywords, relates to credit risk, credit scoring, systemic risk, contagion, and more. Cluster 10 (four keywords) incorporates distributed ledger, smart contracts, corporate governance, and regulatory arbitrage. Cluster 11 contains six keywords concerning institutional/organizational responsibility, accountability, digital platforms, financial crisis, governmentality, international development, and transaction costs. Two keywords in cluster 12: Data Analytics, and E-Commerce. The last cluster contains keyword “Internationalization” only.
Table 5
Keywords and occurrences in each cluster in development phase
Cluster | Keywords and occurrences |
1 | FinTech(108), Crowdfunding(15), Artificial Intelligence(9), Financial Regulation(6), Peer-To-Peer Lending(6), Machine Learning(5), Digital Currency(3), Equity Crowdfunding(3), Financial Intermediation(3), Text Mining(3), Continuance Intention(2), Convolutional Neural Networks(2), Financial Innovation(2), Herding(2), Insurtech(2), Sharing Economy(2), Social Media(2), Technical Analysis(2), Virtual Currency(2) |
2 | Blockchain(47), Cryptocurrency(20), Bitcoin(13), Digitalization(7), Competition(4), Smart Contract(4), Digital Economy(3), Ethereum(3), Initial Coin Offering(3), Regulations(3), Trust(3), Banking Sector(2), Digital Money(2), FinTech Innovation(2), Ico(2), Initial Coin Offerings(2), Insurance(2), Islamic FinTech(2), Money Laundering(2), Startup(2), Systematic Literature Review(2) |
3 | Innovation(17), Financial Inclusion(11), Finance(10), Regtech(9), Financial Services(7), Technology(6), Business Models(5), Disruption(4), Mobile Payment(4), Ai(3), Digital Banking(3), Efficiency(3), Financialization(3), Governance(3), Lending(3), Markets(3), Payments(3), Cluster Analysis(2), Infrastructure(2), Payment Networks(2), Robots(2), Stock Returns(2), Technology Ecosystems(2), Value Propositions(2) |
4 | Big Data(9), Regulation(8), Cloud Computing(3), Financial Institutions(3), Risk Management(3), Algorithmic Trading(2), Cyber Security(2), Data Security(2), Financial Markets(2), Financial Stability(2), Privacy(2), Security(2) |
5 | China(9), P2P Lending(9), Shadow Banking(5), Startups(4), Marketplace Lending(3), Regulatory Sandbox(3), Supply Chain Finance(3), Case Study(2), Competitive Advantage(2), Event Study(2), Experimentation(2), Fin-Tech(2), Information Asymmetry(2), Internet Finance(2), Lending club(2) |
6 | Banking(7), Financial Market(3), FinTechs(3), Business Model(2), Digital Transformation(2), FinTech Company(2), Risk(2), Service(2), Taxonomy(2) |
7 | Banks(6), Cryptocurrencies(4), Mobile Banking(4), India(3), Mobile Money(3), Adoption(2), Disruptive Innovation(2), Emerging Markets(2), Information Technologies(2), Mobile Payments(2), Technology Acceptance Model(2) |
8 | Entrepreneurial Finance(6), Economic Growth(2), Entrepreneurship(2), Peer-To-Peer(2) |
9 | Credit Risk(4), Credit Scoring(3), Systemic Risk(3), Contagion(2), Factor Models(2), Network Models(2) |
10 | Distributed Ledger(4), Smart Contracts(3), Corporate Governance(2), Regulatory Arbitrage(2) |
11 | Accountability(3), Digital Platforms(2), Financial Crisis(2), Governmentality(2), International Development(2), Transaction Costs(2) |
12 | Data Analytics(2), E-Commerce(2) |
13 | Internationalization(1) |
Some clusters overlap, as shown in 10, due to the identical centrality and density presented in Table 9. The clusters of drivers, prediction, systems, energy-consumption, and future overlap, as does pair of air-pollution and community.
4.3.3 Thematic analysis for fast-growing phase (2020-now)
By setting the minimum appearance to 5 and applying the Louvain clustering algorithm to calculate the top 500 author keywords between 2020 and 2022, the thematic clustering is presented on a strategy map (Fig. 9).
-
Three niche themes: “trust, digital economy, and e-commerce”; “regulation, banks, and privacy”; and “FinTech innovation, sustainability, and digitization”.
-
One motor theme: “financial inclusion, mobile money, digital finance”.
-
three basic themes: “FinTech, COVID-19, and China”, “innovation, financial stability, and technology,” and “artificial intelligence, machine learning, and banking”.
-
Two emerging themes: “digitalization, digital transformation, and financial sector”; and “blockchain, cryptocurrency, and crowdfunding”.
Table 9, using the first words of the clusters as labels with thematic classification results, illustrates the characteristics of these nine clusters. From strong to weak, the order of ranked relevance degree (centrality) to the field for the clusters is artificial intelligence, FinTech, blockchain, financial inclusion, innovation, blockchain, digitalization, regulation, and trust. The order, from strong to weak, of ranked self-development degree (density) is regulation, trust, FinTech innovation, financial inclusion, blockchain, innovation, digitalization, FinTech, and artificial intelligence. The cluster frequency column illustrates the number of times the keyword occurred, and the order is FinTech(744), blockchain(317), financial inclusion(291), Artificial Intelligence(285), innovation(154), trust(149), regulation(90), FinTech innovation(70) and digitalization(49). Generally, the more frequently a keyword appears, the stronger the topic.
Table 6
Cluster description of the thematic map between 2020 and 2022
Cluster No. | Cluster label (theme) | Callon Centrality | Callon Density | Ranked Centrality | Ranked Density | Cluster Frequency |
1 | FinTech (basic) | 0.604 | 10.653 | 8 | 2 | 744 |
2 | Blockchain (emerging) | 0.390 | 11.979 | 5 | 5 | 317 |
3 | Financial Inclusion (motor) | 0.601 | 13.286 | 7 | 6 | 291 |
4 | Artificial Intelligence (basic) | 0.643 | 9.965 | 9 | 1 | 285 |
5 | Innovation (basic) | 0.427 | 11.397 | 6 | 4 | 154 |
6 | Trust (niche) | 0.178 | 14.911 | 1 | 8 | 149 |
7 | Digitalization (emerging) | 0.285 | 11.206 | 4 | 3 | 49 |
8 | Regulation (niche) | 0.215 | 17.785 | 2 | 9 | 90 |
9 | FinTech Innovation (niche) | 0.260 | 13.621 | 3 | 7 | 70 |
| Average | 0.400 | 12.756 | | | 238 |
Table 10 shows author keywords and their appearance in each cluster in the fast-growing phase between 2020 and 2022. It is identical with the development phase, with cluster 1 (FinTech) in this phase concerning the generic FinTech literature. With 18 keywords, it covers various aspects of the FinTech field: COVID-19, China, P2P lending, financial regulation, entrepreneurship, credit scoring, microfinance, financial development, systemic risk, commercial banks, case study, financial intermediation, emerging markets, credit risk, social media, and more. Some individual clusters in the development phase have been combined and evolved into this one cluster such as China, entrepreneurship, and credit risk. One special topic in this cluster is COVID-19, which, since the outbreak of the pandemic, is believed to have dramatically impacted FinTech, positively and negatively (Abdul-Rahim et al., 2022). Containing 16 keywords, cluster 2 still concentrates on the application of blockchain; its related topics are similar to the those in the development phase. Cluster 3, 16 keywords, is about financial inclusion and its related topics such as mobile money, digital finance, financial literacy, mobile payment, financial innovation, Africa, digital financial inclusion, and more. This cluster evolved from the cluster of innovation in the development phase. Cluster 4, consisting of 13 keywords, is related to the application of artificial intelligence. Key components in the cluster include machine learning, banking, finance, financial services, and more. The cluster evolved from the cluster of big data of the previous FinTech phase.
With 11 keywords, cluster 5 is about innovation, which is inherited from the cluster with the same name in the previous phase. Topics include financial stability, technology, risk, bank, regulatory sandbox, and more. Cluster 6 (16 keywords) centres on trust in FinTech and in association with Digital Economy, E-Commerce, Indonesia, Technology Adoption, Islamic Finance, Islamic FinTech, and more. Indonesia and Islamic FinTech are new topics, appearing only in this fast-growing phase of FinTech research. Cluster 7 (four keywords) is about digitalization, digital transformation, financial sector, and the COVID-19 pandemic. With nine keywords, Cluster 8 is about regulation. The associated topics include SMEs, financial markets, household finance, lending, banks, privacy, competition, platforms, payments, and more. Finally, the cluster of FinTech Innovation-related topics include sustainability, digitization, green finance, sustainable development, ecosystem, insurtech, and financial geography. Sustainability, digitization, green finance, sustainable development, and ecosystem first appeared in the last three years. This implies that FinTech research and practice are moving toward an era of focusing on sustainable development.
Table 7
Keywords and occurrences in each cluster in fast-growing phase
Cluster | Keywords and occurrences |
1 | FinTech(485), COVID-19(48), China(36), P2P Lending(25), Peer-To-Peer Lending(23), Financial Regulation(18), Entrepreneurship(13), Credit Scoring(12), Microfinance(12), Financial Development(10), Systemic Risk(10), Commercial Banks(9), Case Study(8), Financial Intermediation(8), FinTechs(8), Emerging Markets(7), Credit Risk(6), Social Media(6) |
2 | Blockchain(92), Cryptocurrency(39), Crowdfunding(37), Bitcoin(33), Cryptocurrencies(19), Blockchain Technology(12), Security(11), Entrepreneurial Finance(11), Information Asymmetry(11), Venture Capital(9), Initial Coin Offering(8), Smart Contracts(8), Bibliometric Analysis(7), Digital Currency(7), Distributed Ledger Technology(7), Internet Of Things(6) |
3 | Financial Inclusion(89), Mobile Money(28), Digital Finance(26), Financial Literacy(22), Mobile Payment(17), Financial Innovation(16), Africa(11), Digital Financial Inclusion(11), G21(10), Internet Finance(9), Debt(7), FinTech Adoption(7), G28(7), Sustainable Development Goals(7), Credit(6), Digital Financial Services(6), Financial Education(6), G20(6) |
4 | Artificial Intelligence(49), Machine Learning(46), Banking(34), Finance(24), Financial Services(23), Big Data(22), Regtech(15), Deep Learning(13), Robo-Advisor(11), Digital Banking(10), Open Banking(10), Risk Management(10), Ai(6), Bibliometric(6), Insurance(6) |
5 | Innovation(30), Financial Stability(22), Technology(21), Financial Technology (FinTech)(17), Risk(14), Bank(13), Regulatory Sandbox(10), Financial Institutions(9), Financial Market(6), Financial Risk(6), Investment(6) |
6 | Trust(26), Digital Economy(14), E-Commerce(10), Indonesia(10), Technology Adoption(10), Islamic Finance(9), Islamic FinTech(9), Malaysia(9), Tam(8), Perceived Risk(7), Utaut(7), Banking Sector(6), Data Envelopment Analysis(6), Disruptive Innovation(6), Intention To Use(6), Vietnam(6) |
7 | Digitalization(22), Digital Transformation(12), Financial Sector(9), COVID-19 Pandemic(6) |
8 | Regulation(20), Banks(13), Privacy(9), Competition(8), Platforms(8), Payments(7), SMEs(7), Financial Markets(6), Household Finance(6), Lending(6) |
9 | FinTech Innovation(11), Sustainability(11), Digitization(10), Green Finance(10), Sustainable Development(8), Ecosystem(7), Insurtech(7), Financial Geography(6) |
Key themes in the four clusters.
Cluster No. and key themes | Keywords and occurrences | Indicative fields |
1: Freelane works and implications | FinTech(485), COVID-19(48), China(36), P2P Lending(25), Peer-To-Peer Lending(23), Financial Regulation(18), Entrepreneurship(13), Credit Scoring(12), Microfinance(12), Financial Development(10), Systemic Risk(10), Commercial Banks(9), Case Study(8), Financial Intermediation(8), FinTechs(8), Emerging Markets(7), Credit Risk(6), Social Media(6) | Finance, Economics; Politics; Law; commerce, Industrial relations; Management |
2:Blockchain and implication | Blockchain(92), Cryptocurrency(39), Crowdfunding(37), Bitcoin(33), Cryptocurrencies(19), Blockchain Technology(12), Security(11), Entrepreneurial Finance(11), Information Asymmetry(11), Venture Capital(9), Initial Coin Offering(8), Smart Contracts(8), Bibliometric Analysis(7), Digital Currency(7), Distributed Ledger Technology(7), Internet Of Things(6) | Finance, ICT |
3: Financial inclusion and implications | Financial Inclusion(89), Mobile Money(28), Digital Finance(26), Financial Literacy(22), Mobile Payment(17), Financial Innovation(16), Africa(11), Digital Financial Inclusion(11), G21(10), Internet Finance(9), Debt(7), FinTech Adoption(7), G28(7), Sustainable Development Goals(7), Credit(6), Digital Financial Services(6), Financial Education(6), G20(6) | Finance, Commence, ICT, Management, Law |
4: Artificial Intelligence and implications | Artificial Intelligence(49), Machine Learning(46), Banking(34), Finance(24), Financial Services(23), Big Data(22), Regtech(15), Deep Learning(13), Robo-Advisor(11), Digital Banking(10), Open Banking(10), Risk Management(10), Ai(6), Bibliometric(6), Insurance(6) | ICT, management, law |
5: Innovation and implications | Innovation(30), Financial Stability(22), Technology(21), Financial Technology (FinTech)(17), Risk(14), Bank(13), Regulatory Sandbox(10), Financial Institutions(9), Financial Market(6), Financial Risk(6), Investment(6) | Management, law, Banking, markrting |
6: FinTech adoption and imploications | Trust(26), Digital Economy(14), E-Commerce(10), Indonesia(10), Technology Adoption(10), Islamic Finance(9), Islamic FinTech(9), Malaysia(9), Tam(8), Perceived Risk(7), Utaut(7), Banking Sector(6), Data Envelopment Analysis(6), Disruptive Innovation(6), Intention To Use(6), Vietnam(6) | Management, law, Banking, markrting |
7: Digitalization and implications | Digitalization(22), Digital Transformation(12), Financial Sector(9), COVID-19 Pandemic(6) | ICT, Finance, Crisis Management |
8: Regulation and implications | Regulation(20), Banks(13), Privacy(9), Competition(8), Platforms(8), Payments(7), SMEs(7), Financial Markets(6), Household Finance(6), Lending(6) | Law, Banking, Commerce, |
9: Sustainability and FinTech | Sustainability(11), FinTech Innovation(11), Digitization(10), Green Finance(10), Sustainable Development(8), Ecosystem(7), Insurtech(7), Financial Geography(6) | Sustainable development |
4.4 Future research directions and exploring areas
A number of current thematic trends were detected through the keyword and thematic mapping detailed in the previous two sections (4.4 and 4.5). This section provides a holistic view in regard to future research directions by combining keyword evolution and thematic map analyses. First, the field evolution analysis revealed that, between 2020 and 2022, there were eight central topics: FinTech, blockchain, financial inclusion, artificial intelligence, innovation, trust, digitalization, and regulation. These became the key trending topics in this latest, fast-growing phase, and most likely, these topics and their associated conversations will be a research direction for the next few years. These eight topics suggest that generic research on FinTech, which has been a talking point for the last 23 years—from the initial phase through to the fast-growing phase—will continue to be a research subject.
In addition to these eight topics, the thematic analysis for the field’s fast-growing phase (Table 9) revealed one more topic—“FinTech Innovation”—which is classified as a niche theme. An important feature of “FinTech innovation” is its focus on sustainable development. The conversations associated with this topic include sustainability, green finance, sustainable development, ecosystem, InsurTech, and financial geography (see Table 10. Keywords and occurrences in each cluster in fast-growing phase). This result signals that the sustainable development of FinTech and FinTech for sustainability together represent one of most current research directions and will be trending in the future.
Table 10 shows all the conversations related to the abovementioned nine topics; each topic (cluster) represents a theme which comprises a set of closely related conversations. FinTech, as a generic research field, is strongly related to COVID-19, China, P2P lending, financial regulation, and more. The blockchain topic concentrates on conversations regarding digital currencies, while those keywords related to financial inclusion feature mobile money, digital finance, and more. Artificial intelligence includes machine learning, banking, finance, financial services, and more, and innovation includes financial stability, technology, risk, bank, and more. The trust topic is associated with digital economy, e-commerce, Indonesia, technology adoption, Islamic finance, Islamic fintech, and more. Digitalization is about digital transformation in the financial sector and the impact of COVID-19 pandemic. SMEs, financial markets, household finance, lending, and more are associated with the topic of regulation, while FinTech Innovation is related to sustainable development (as previously highlighted). These associated conversations can be seen as trending sub-topics for each main topic.
The thematic analysis for the fast-growing phase of FinTech field revealed three niche themes: “Trust, Digital Economy, and E-Commerce”; “Regulation, Banks, and Privacy”; and “FinTech Innovation, Sustainability, and Digitization”. One motor theme was discovered—“Financial Inclusion, Mobile Money, Digital Finance”—and three basic themes: “FinTech, COVID-19, and China”; “Innovation, Financial Stability, and Technology;” and “Artificial Intelligence, Machine Learning, and Banking”. Two emerging themes were detected: “Digitalization, Digital Transformation, and Financial Sector”; and “Blockchain, Cryptocurrency, and Crowdfunding”.
These two emerging themes have spectacular further development potential, not only in terms of the themes themselves but also regarding their relationship with other themes in the FinTech field. Similarly, “Trust, Digital Economy, and E-Commerce”, “Regulation, Banks, and Privacy”, and “FinTech Innovation, Sustainability, and Digitization” are themes that require further development vis-ā-vis the significance of each to the FinTech field and their the relationship with other themes. The themes of “FinTech, COVID-19, and China”, “Innovation, Financial Stability, and Technology,” and “Artificial Intelligence, Machine Learning, and Banking” each require further development as themes since there are significant to the field.
In a holistic view of the results of above analyses, the study revealed that, even though the largest number of studies are from developed countries in different subject of FinTech research (refer to the section of affiliation countries), this is not the case for latest fast-growing phase of thematic analysis. Among the topmost contributing countries were several emerging countries with large populations, such as China, India, Indonesia, and Malaysia. China, as an emerging economy, was detected as the top contributor. This result illustrates that interest in FinTech research may be strongly related to the country’s population, rather than its economic development. As there are structural, economic, and cultural disparities among advanced and developing countries, future studies should be conducted in comparative contexts to determine what drives FinTech and its related topic research. Scholars could benefit from such insights afforded by investigations into the determinants that foster and hinder FinTech development research to enhance current analytical frameworks.
In relation to this point, location-specific (country, region, and culture) FinTech studies are worth further investigation. Not only are a large proportion of articles location-specific, but articles identifying non-location-fit FinTech, such as Islamic finance, also indicate the importance of location- or culture-fit. The author argues that future location- or culture-specific studies should be conducted from a geographical FinTech ecosystem perspective in addition to the current focus on the organisational business ecosystem.
Finally, in total, 66 articles used COVID-19 as one of their research keywords. During COVID-19, FinTech development slowed down, struggling to obtain funding contracts amidst recessions around the world, as some scholars pointed out (refer to 4.5.3). COVID-19 should be analysed for its positive impact on the FinTech industry in terms of mitigating the economic aggravation of the pandemic for countries. Additionally, any possible financial market crisis could be prevented by developing this research direction, and new FinTech opportunities could be explored.
Table 8
Summary of future research ditrections
Trends | Future research Directions | Indicating keywords |
Phenomion of FinTech | 1: applications of new technologies 2: innovation and digitalizations in FinTech 3: regulation 4: inclusive finance 5: trust in new FinTech (e.g., models, innovations) | Blockchain, financial inclusion, artificial intelligence, innovation, trust, digitalization, P2P, e-commerce, cryptocurrency, crowdfunding and regulation. |
Sustainable development | 6: Sustainability | sustainability, green finance, sustainable development, ecosystem, |
Gepgraphic-, cultural-oriented and inclusive studies | 7: Region, country, and multinations studies 8: Cultural, and religious studies | China, India, Africa, emerging market, developing countries, financial geography |
Crisis effects (i.e., COVID-19, GFC) | Positive and nagitive impacts | COVID-19, GFC |