SERVICE SCIENCEVol. 10, No. 4, December 2018, pp. 1–18

http://pubsonline.informs.org/journal/serv/ ISSN 2164-3962 (print), ISSN 2164-3970 (online)

Transformation Through Unbundling: Visualizing the GlobalFinTech EcosystemRahul C. Basole,a Shiv S. Patelaa School of Interactive Computing, Georgia Institute of Technology, Atlanta, Georgia 30332Contact: basole@gatech.edu, http://orcid.org/0000-0002-7328-5276 (RCB); shivpatel@gatech.edu (SSP)

Received: July 24, 2017Revised: December 13, 2017Accepted: February 21, 2018Published Online:

https://doi.org/10.1287/serv.2018.0210

Copyright: © 2018 INFORMS

Abstract. The infusion of digital technology into financial services, also known as Fin-Tech, is creating a massive disruption of the entire financial sector. Services traditionallyoffered by incumbents are now rapidly unbundled by a growing set of startups, leadingto new models of collaboration and a significant shift in power. This study providesinsight into the structure of the FinTech ecosystem using data-driven visualizations of6,493 global companies across 24 market segments. We focus on two types of ecosystementities, namely, (1) firms that create, offer, and acquire FinTech services and (2) investorsthat fund and enable them. Our analyses reveal a highly skewed global footprint ofFinTech activities, differential growth patterns across ecosystem segments, highly inter-dependent network structure, and a variegated nature of investments and acquisitionsled by key incumbents and venture capital firms. We conclude with theoretical andmanagerial implications and discuss opportunities for future service ecosystem research.

Keywords: service ecosystem • FinTech • transformation • network analysis • visualization

1. IntroductionMany industries are experiencing massive transformational business model changes because of increased digi-tization (Maglio et al. 2015, Rogers 2016). Energy, healthcare, education, and hospitality are just a few promi-nent examples. Perhaps one of the most profound transformations is occurring in the financial sector (Altand Puschmann 2016, Puschmann 2017). The infusion of digital technology into financial services, commonlyreferred to as financial technology or FinTech, has led to an explosive growth in new startups and unprece-dented change in the finance sector (Chishti and Barberis 2016). It has been argued that every banking service isbeing targeted by FinTech companies globally, either to reduce costs or serve customers better, while ultimatelydisrupting the financial incumbents (Mackenzie 2015).Traditionally, consumers accessed financial services through one or more large institutions. In this “univer-

sal” model, incumbents typically offer a broad product portfolio including retail, private, commercial, invest-ment, and transaction banking, along with wealth, asset management, and insurance. In today’s platformand app-centric world, consumers are less concerned about receiving all their services from a single serviceprovider (Smedlund 2012). Instead, consumers expect a seamless experience across various services, responsiveand personalized to their expectations, and accessible anywhere and at anytime (Maglio and Spohrer 2013,de Reuver et al. 2017). FinTech startups are therefore focusing on improving specific parts of the “univer-sal” model; they design, build, and execute individual parts of the traditional value chain, while being better,cheaper, and faster than existing incumbent offerings (Alt and Ehrenberg 2016). With this strategy, startups areoften able to establish a niche market position.

Enabled by the proliferation of cloud, mobile, and social computing, startups are realizing this new valueexpectation and have started to “unbundle” many of the traditional financial offerings (Christensen et al. 2016).Overall, this would not be an issue to the large incumbents who have continually dealt with technologicalchanges (Chishti and Barberis 2016). However, given the sheer number of FinTech players, the pace of inno-vations is incredibly high and exposes incumbents to a massive scale and scope of disruption. Since a singleorganization cannot match this rapid rate of disruption, incumbents must shift their strategies accordingly.New collaboration and competition models must be embraced (Nienaber 2016). The emergence of applica-tion programming interfaces (APIs)—digital control points that enable broader access to data and services—are enabling such novel organizational arrangements (Evans and Basole 2016, Parker et al. 2016, de Reuveret al. 2017).

In a very short time, the FinTech ecosystem has grown remarkably. To call FinTech a technology fad wouldbe dismissive of the tectonic activities shaping the landscape. According to a recent industry report, 13.8 billion

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USD were invested into organizations by both venture capitalists and corporate banks in 2016 alone (KPMG2017). Large financial institutions, such as Citi, BBVA, and JP Morgan Chase, are recognizing this disrup-tive potential and are either partnering or acquiring nimble and innovative FinTech organizations to enrichand widen their digital business portfolio and stay relevant in the ever-evolving FinTech landscape. SomeFinTech startups have even gone public with initial public offerings (IPOs); many others are assumed to fol-low (Mackenzie 2015).Unlike some technology industries, FinTech embraces a truly global footprint. In a recent study released by

Ernst and Young, the United Kingdom was named the FinTech capital of the world beating out traditionalinnovation ecosystem leaders Silicon Valley and New York (EY 2017). According to other reports, there are over20 FinTech focused accelerators found globally in emerging financial centers such as Singapore and Mumbai.Additionally, it is not unusual to see financial platforms developed in one region deployed across the world. Theglobal nature of FinTech has wide ranging implications for cross-border financial transactions, data residencylitigations, and institutional policies.

At the core of today’s financialization is the concept of shifting financial decisions traditionally made byprofessionals to consumers via easy-to-use and convenient applications and tools (Alt and Puschmann 2016,Sironi 2016, Zavolokina et al. 2016). The new way enabled by innovative FinTech applications allows individualsto search for mortgages, check investments, find insurance, and even plan their kids’ education without everleaving the convenience of their house. Therefore, the emergence of the FinTech sector has and will likelycontinue to have a profound impact on consumers in the foreseeable future.

Despite the prominence of this emerging industry, there is limited understanding in the converging structure,dynamics, and evolution of the FinTech ecosystem. Most prior work on FinTech is predominantly practitioner ori-ented, focusing on understanding individual segments or countries or providing anecdotal qualitative evidence.Rigorous empirical studies providing systemic insight into the entire global ecosystem are lacking. Building onprior work of service ecosystems and digital disruption, the overarching objective of this study is to investi-gate the topological characteristics of the FinTech ecosystem. To achieve this objective, we must examine theunbundling of services that is driving and shaping the FinTech ecosystem. Specifically, we identify and describesegmental and geographic differences in the structural evolution of the FinTech ecosystem. We take a largelydescriptive approach to provide a foundation for future predictive or prescriptive analyses. In doing so, wefurther our overall understanding of digital innovations made by financial services and infrastructures.

Advocated by earlier work (Basole et al. 2015), we pursue this objective by using a data-driven analysis andvisualization approach. We create a unique data set by integrating socially curated data sources. We focus ouranalysis on two salient types of ecosystem entities, namely, (1) organizations that create, offer, and acquireFinTech products and services; and (2) investors that fund and enable these organizations. This particularbipartite network context allows us to explore how different startups emerge across categories, who is investingin them, and the synergistic behaviors between them (Chishti and Barberis 2016, Anderson 2016, Lien andWilliams 2016).

Our contribution is multifold. First, our study provides a rich data-driven analysis of an emerging, convergingservice ecosystem that is financializing consumers’ daily life, transforming the distribution of ecosystem power,and requiring incumbents to rethink their competitive strategy. Second, by creating various visual representa-tions of the structure and evolutions of the FinTech ecosystem, we reveal the underlying interconnectivity andcomplexity of investment relationships while providing important triangulated insights into segmental differ-ences. In doing so, we address the call for studies examining ecosystem as structure (Adner 2017). Third, byexplicitly considering ecosystem investors and acquirers we identify how business strategies differ across theservice ecosystem. This allows us to explore where the market is headed and what possible corporate direc-tions are pursued. Lastly, given the diverse nature of the FinTech ecosystem, our study ultimately contributesto the call of cross-disciplinary research at the intersection of service science, information systems, finance, andstrategy (Lusch et al. 2016, Mackenzie 2015).

The remainder of the paper is structured as follows. Section 2 provides a background on related work. Sec-tion 3 describes our research methodology, data curation process, and analysis/visualization approach. In Sec-tion 4 we present our analysis and discussion of the results. We conclude with implications and suggest futureresearch opportunities in Section 5.

2. BackgroundThe study of digital market transformation is not confined to a single discipline. To guide our exploratorywork on the digital transformation of the financial industry, we build on three core research streams: businessecosystems, digital disruption, and financialization and FinTech. These three areas reflect key underpinnings

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that motivate our study. The study of business ecosystems examines the dynamic, collaborative, and coopeti-tive nature of industries, identifying different stakeholders and their complex interactions. Our second researchstream is the study of digital disruption. Enterprises, industries, and entire economies are fundamentally trans-formed through digital technology. The study of digital disruption examines the nature and extent of disruptionin different contexts. Our third research stream examines the evolution of the financial industry, providing ahistorical perspective of different contingencies that in part contributed to the emergence of the FinTech indus-try. In doing so, we gain an appreciation of the wide range of transformation dynamics that have shaped theindustry for decades and allow us to differentiate what may be different in FinTech.

2.1. Business EcosystemsThe conceptualization of industries and markets as business ecosystems is an increasingly established perspec-tive in the management, strategy, and information systems literature (Moore 1996, Iyer et al. 2006, Basole andKarla 2011). Service science scholars in particular have called for an increased adoption of an ecosystem lensto understand complex service contexts (Vargo and Akaka 2012, Lusch et al. 2016). Adapted from the naturalsciences, the ecosystem perspective is based on the tenet that markets consist of a heterogeneous, intercon-nected, and continuously evolving set of “species” (or stakeholders) that assume specific roles, cocreate value,and depend on each other for survival (Iansiti and Levien 2004, Vargo and Lusch 2008).Typical business ecosystems are characterized by a few prominent players (keystones) and many smaller ones

(complementors and niche) (Moore 1996, Thomas and Autio 2015). With increasing complexity of products andservices, value creation is disintegrated both vertically and horizontally, raising the necessity and opportunityfor interfirm relations (Iansiti and Levien 2004, Basole and Rouse 2008). Network formation has been found tobe particularly valuable in high clock speed industries as it has allowed firms to share risks in development andhave access to synergistic knowledge (Eisenhardt and Schoonhoven 1996). Indeed, interfirm networks have beenshown to be an effective organizational form to improve firm performance, innovation speed, and organizationallearning (Ahuja 2000, Gulati et al. 2000).

Ecosystem stakeholders come from a variety of market segments, providing both complementary and com-peting service offerings. Successful value creation and delivery requires a careful orchestration between firmsacross these segments (Basole and Karla 2011, Dhanaraj and Parkhe 2006). As we discuss later, the FinTechecosystem is unique in the sense that an established industry with large players is being transformed by theentry of small players across a variety of market segments. These new entrants are focusing on offering existingand novel services using emerging technologies, consequently changing the collaborative and competitive fabricof the overall ecosystem (Kashyap and Weber 2016, Lien and Williams 2016).

While there is an increasing set of studies focused on the structure and dynamics of business ecosys-tems (Ahuja et al. 2012, Padgett and Powell 2012), our understanding of the FinTech ecosystem is still in itsinfancy. Drawing on the core ideas of ecosystemic thinking—multiple stakeholders, competing and complemen-tary market segments, and value cocreation—we seek to explore what makes up the FinTech ecosystem, howincumbents are reacting, and who is enabling the growth through funding, investment, and acquisition.

2.2. Digital DisruptionDigital disruption is not new and has been a topic of interest for many years (Venkatraman 1994, Basole andDeMillo 2006, Bradley et al. 2015, Rogers 2016). A comprehensive review is beyond the scope of this paper, butinterested readers are referred to Andal-Ancion et al. (2003) and Westerman et al. (2014) for excellent overviews.

In one of the early waves of digital disruption, the Internet fundamentally shifted the traditional trade-offbetween richness and reach, reducing transactions costs substantially and changing how products and serviceswere made and sold (Evans and Forth 2015). Firms had to make hard choices on which parts of the business tokeep and which to abandon, thereby deconstructing value chains. This was followed by Web 2.0, which broadlyconsidered replacing traditional economies of scale and scope with collaborative activities, self-organizing com-munities, and “long tails,” leading to fundamentally new business architectures (El Sawy et al. 2010, Bharadwajet al. 2013, Weill and Woerner 2015). Today, the advent of mobile, social, cloud, and analytics is disruptingindustries even further, generating hyper-scaled business environments, with massive data, complex collabora-tions, dynamic customer expectations, and entirely new business models (Loebbecke and Picot 2015, Matt et al.2015, de Reuver et al. 2017).

Prior studies have shown the broad disruptive impact of digital technologies at the process, supply chain,and enterprise level across a variety of domains, including healthcare, energy, and manufacturing (Agarwalet al. 2010, Lucas Jr. et al. 2013, Evans and Forth 2015). Digital transformation has been a topic of interestto the information systems community for many years (Lyytinen and Rose 2003, Bharadwaj et al. 2013, Matt

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et al. 2015). Existing work, for instance, has examined how digital disruption is redefining industries (Bradleyet al. 2015), how digital ecosystems should be designed (El Sawy et al. 2010), what impact it has on businessmodels and societies (Loebbecke and Picot 2015, Majchrzak et al. 2016), and how to adapt and thrive in suchenvironments (Weill and Woerner 2015).Building on this work, our study examines how digital technologies are creating entirely new companies in

the financial services sector. We also discuss how digital technologies are reshaping existing industry boundariesand leading to new market segments.

2.3. Financialization and FinTechThe mainstream perception of FinTech is that it is the latest mobile app that lets consumers pay without touch-ing currency or swiping a credit card. While technically not incorrect, the reality is that FinTech is significantlybroader. To understand the current state of FinTech, it is important to analyze a historical perspective. Tech-nology has played an important role in the finance sector for a long time. Credit cards were introduced inthe 1950s to ease the burden of carrying cash, followed by the introduction of ATMs to replace tellers andbranches in the 1960s. In the 1970s and 1980s, electronic stock trading began on exchange floors and mainframecomputers, while banks started to adopt new data record keeping systems. With the emergence of the Internet,e-commerce and online stock brokerage websites emerged in the 1990s, replacing traditional retail and stockbrokering models. While these are primarily consumer-facing examples, these technological transformationswere also accompanied by many institutional innovations, including more sophisticated risk management, tradeprocessing, treasury management, and data analysis tools (Mackenzie 2015). These innovations helped banksand financial services firms become significantly cost-effective.

What is remarkable over these six decades of technological innovations is that the finance and banking sectorwas never really threatened, but in fact grew significantly. With the emergence of mobile, social, and cloudcomputing, however, the industry has been shaken to its core (Alt and Ehrenberg 2016, Sironi 2016). Financialservices are increasingly digitized through mobile wallets, payment apps, automated wealth and retirementplanning advisors, and crowdfunding and online lending platforms (Parker et al. 2016, Fleming and Sorenson2016, Sironi 2016). These services are not just enhancements, but rather replacements for banking services. Whileconsumers in the past used a single financial institution to access all these services, they increasingly use adiverse set of apps offered by various FinTech startups (Alt and Ehrenberg 2016, Kashyap and Weber 2016).

The convergence of digital technology and financial services is creating entirely new market segments andshifting the power from traditional institutions to startups. Unquestionably, the industry is being unbundled (seeFigure 1). However, what market segments really make up FinTech is not clear. In fact, the boundaries betweenmarket segments appear to be increasingly blurring and, despite some notable recent studies (Zavolokina et al.2016, Puschmann 2017), no single overarching definition of the FinTech industry exists.

Indeed, a systematic search of how industry experts have categorized the FinTech industry offers some over-lapping but also divergent perspectives on key market segments, as shown in Table 1. The results show thatmost experts use 6 (minimum) to 14 (maximum) categories to organize the ecosystem. We also observe somecategory consistency across classifications (e.g., Payments), but some unique categories as well (e.g., DigitalCurrency, financial exchanges (FX)). Based on our analysis, each of these studies use different classificationapproaches, either starting with a subset of key firms and expanding their listing using a snowball method orapplying existing classification schemes from other domains. It is evident that there is no consistent approachacross these studies suggesting that there still is no clear consensus what the ecosystem is and how segmentsand boundaries should be defined.

A reasonable explanation for this gap is that the industry is rather nascent, and relevant segments are stillemerging. While we fully acknowledge the value of these prior market studies and recent overview studies, webelieve that there is an opportunity to further clarify the boundaries and structure of the FinTech ecosystemand more organically identify the underlying market segments. To pursue this question, we use a bottom-up,data-driven approach to define the key categories that define the FinTech ecosystem.

3. Research Methodology3.1. DataThe availability of a tsunami of digital business data facilitates comprehensive business ecosystem analysis morethan ever (Basole et al. 2013). Given that we are interested in the unbundling and structure of the financialsector, identification of key entities and relationships between them is key. Our study uses Crunchbase,1 asocially curated (wiki style) directory of global technology companies, people, and investors. While relativelynovel, it is increasingly used in the innovation and entrepreneurship as well as electronic commerce research

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Figure 1. Conceptualizing the Unbundling of Financial Services

Incumbent

...

......

Transformationthrough unbundling

BUNDLEDSERVICES

Org 1

Org 2Org n

communities (e.g., Shi et al. 2016, Hallen et al. 2014, Gupta et al. 2015). All additions and edits in this data setundergo an approval process before they are released online. The data set provides detailed information oncompanies (e.g., founding date, executive team, office locations, etc.), funding entities (i.e., business angels andventure capitalist firms), funding rounds (e.g., date, amounts, and type), and exit status of portfolio companies(i.e., acquired, IPO). We used the Crunchbase API to specify relevant queries and extract the data.We corroborated the completeness of our data set by comparing the entities to the companies identified by

industry experts in the aforementioned studies. Our data set is by far the most comprehensive and includesall companies listed in these previous studies. Moreover, we identify additional companies not traditionallyconsidered given their size or global location. To further ensure the quality of our data, we consulted with twoindustry experts with over 20+ years of experience in the payment and finance industry in North America,Europe, and Asia. Our informal interviews provided sufficient support that our data set captures not only keyestablished companies but also companies that are emerging.

3.2. ApproachFollowing Basole et al. (2015), we propose a five-phase process for analyzing the structure of the FinTech ecosys-tem, consisting of (1) boundary specification, (2) network construction, (3) metrics computation, (4) visualizing,and (5) sensemaking. Our process is shown in Figure 2. The “human-in-the-loop” approach is based on thewidely used information visualization reference model (Card et al. 1999), which advocates a careful balance

Table 1. Industry Perspectives on Key FinTech Segments

Source No. of segments Segments

Accenture 6 Insurance, Risk and Security, Markets, Wealth Management, Payments, LendingCB Insightsa 7 Lending, Payments/Billing Technology, Personal Finance, Money Transfer, Digital

Currency, Institutional Tools, Equity CrowdfundingBI Intelligenceb 6 Payments and Transfers, Lending and Financing, Retail Banking, Financial

Management, Insurance, Markets and ExchangesVenture Scannerc 14 Lending, Personal Finance, Payments, Equity Financing, Remittances, Retail

Investments, Institutional Investments, Security/Authorization/Fraud, BankingInfrastructure, Business Tools, Crowdfunding, Consumer Banking, FinancialResearch, FinTech Investors

World Economic Forumd 6 Payments, Insurance, Deposits and Lending, Capital Raising, InvestmentManagement, Marketing Provisioning

Deloittee 9 Asset Management, Payments and Transactions, Mobile Banking, FinancialAdvisory, P2P Lending and Crowdfunding, Risk and Compliance, Security andPrivacy, FX, Trading

ahttp://www.cbinsights.com/blog/fin-tech-periodic-table.bhttp://www.businessinsider.com/the-fintech-ecosystem-explained-measuring-the-effects-of-technology-on-the-entire-financial-services

-industry-2015-12.chttp://insights.venturescanner.com/venture-scanner-sector-maps/.dhttp://www3.weforum.org/docs/WEF-The-future-of-financial-services.pdf.ehttp://www2.deloitte.com/be/en/pages/financial-services/articles/banking-disrupted.html.

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Figure 2. Multistage FinTech Ecosystem Analysis and Visualization (Adapted from Basole et al. 2015)

Step 1Boundary

Specification

Step 2Network

Construction

Step 4Visualization

Step 5Sense Making

FinTech ecosystemdata

Determines

Feeds

1 2

n

Identify,extract, and

curate

Step 3Metrics

Computation

Tasks

of data management, visual encodings, and interaction techniques for the purpose of creating a flexible andreusable ecosystem visualization approach.3.2.1. Step 1: Boundary Specification. All segments and firms are to a certain extent related to each other. Whileit would be desirable to include everything that is “relevant,” this would easily lead to scope creep. A criticalfirst step in emerging business ecosystem analysis is the specification of boundaries. The challenge in definingboundaries is that business ecosystems are evolving systems, with firms continuously entering and leaving.Rather than taking a firm-level selection approach, an alternate view is to select broad market segments thatmake up the ecosystem. Here, too, we face the issue which ones to include, as segments are often related toeach other. Ultimately, the choice of what to include is driven by the nature and intent of the problem, thequestions being asked, and the costs involved (Basole et al. 2015).In our study context, boundary specification involves determining the primitives of the FinTech ecosystem

architecture (Ahuja et al. 2012), including nodes, node types, relationship types, and specification of the desiredanalysis timeframe. We applied a bottom-up approach to selecting the overall boundary of the FinTech ecosys-tem. As all companies have one or more category tags associated with them, we began our selection by firstchoosing a set of well-known FinTech companies and identifying all corresponding category tags. Out of the 857unique tag categories, only 120 made sense to be included in further analysis. We then used multiple FinTechexperts to evaluate the applicability of the tags and rate them as highly, somewhat, or not relevant to FinTech.Using a consensus-based approach we ultimately identified 57 tag categories of relevance.

Given the high number of tag categories, we aimed to further reduce the classification to facilitate betterinterpretation and visualization of the segments. To do this, we included all companies that had one of the57 categories associated with it. Almost all companies have two or more category tags, indicating primaryand secondary foci. We then used a tag cooccurrence network and community detection algorithm to identifysubclusters within the data. Tag categories represent nodes and edges represent cooccurrence of the tags. Thethickness of the edge corresponds to the cooccurrence. We thus had a weighted undirected network of 57 tagcategories. Next, we applied the Louvain modularity algorithm to identify subclusters (Blondel et al. 2008). Thisled to 24 unique categories, shown in Table 2. We used these 24 unique categories for all subsequent analyses. Ascan be observed, the 24 categories are a more granular superset of the categories identified by market researchfirms (shown in Table 1). Moreover, we acknowledge that some categories are overlapping (e.g., Bitcoin andVirtual Currency), but given the prevalence of both and relative distance between both, we included them asseparate categories. One striking observation is that “FinTech and Financial Technology” as well as “FinancialServices” are two tag categories that emerged from our analysis. We considered dropping these given thatwe are studying FinTech and an inclusion may confound our analysis. However, we decided not to for tworeasons. First, if we dropped these categories, a significant number of relevant companies would have beendropped from the sample. Second, category tags reflect a self-assessment by the founders and executives andindicate primary and secondary segment affiliations. To ensure further validity and confidence with our data,we corroborated the categories with expert feedback. The final data set consisted of 6,493 companies across 24categories. The sample includes companies from around the world and at different maturity stages.

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Table 2. FinTech Segment Categories Used in This Study

Accounting FinTech/Financial Tech. Point of SaleAuctions Fraud Detection Price ComparisonBanking Gift Cards Risk ManagementBilling Insurance Companies Stock ExchangesBitcoin Invest./Wealth Mgt, Hedge Funds Trading, Brokers, Fin. Exch.Credit, Credit Cards, and Consumer Lending P2P Money Transfer Transaction ProcessingCrowdfunding, Social Fundraising+ Investing Payment and Mobile Payments Venture CapitalFinancial Services Personal Finance Virtual Currency

3.2.2. Step 2: Network Construction. We constructed the FinTech ecosystem network consisting of companiesand investors using a weighted adjacency matrix approach, with cell entries marked as the total funding oracquisition value between a pair of firms and 0 otherwise. In doing so, we explicitly accounted for the differingdegree of funding flow that may exist between firms. Moreover, as investments and acquisitions are inherentlydirectional, our ecosystem network resulted in a directed bipartite graph. Given our interest in the structuralevolution, we used the founding date of a firm2 and the investment/acquisition date to create annual temporalsnapshots of the ecosystem from 2006–2015. We also coded firms as “investors” and “acquirers” to denote theirrole(s) in the emerging FinTech ecosystem.

3.2.3. Step 3: Metrics Computation. The advantage of conceptualizing business ecosystems as networks is theavailability of a wide range of metrics. There are many metrics, drawn from social network, information, andgraph theory, that are useful for understanding the structure and dynamics of a business ecosystem. Theselection of metrics is generally driven by the insight objectives and decision processes. Broadly speaking,metrics fall into two levels of analysis: the node level and the network level (Zaheer et al. 2010). Node metricsprovide insight at the individual entity level, while network metrics describe the entire ecosystem. Based onprior related work (Iyer et al. 2006, Rosenkopf and Padula 2008, Basole et al. 2015, Basole and Karla 2011), wecompute several metrics at the network level and at the node level using NetworkX, a Python-based library forgraph computations.3

One of the most commonly used graph-based ecosystem metric is node centrality (Wasserman and Faust1994). Centrality refers to the relative importance or prominence of a firm in the ecosystem, where firms withhigher levels of centrality are found to have more power and control over peripheral firms. There are manyvariants of centrality, such as those based on direct ties (degree), shortest path (closeness), geodesic distance(betweenness), or recursive importance (eigenvector). Each captures a different aspect of firm power and influ-ence in an ecosystem. In our study, we use degree, weighted degree, and betweenness centrality to understandthe importance of firms in the FinTech ecosystem. Another node-level measure of frequent interest is the clus-tering coefficient, defined as the proportion of a firm’s direct links that are also directly linked to each other.In the context of ecosystems, firm’s with dense clustering have been shown to experience greater collaboration,resource pooling, and problem solving because of increased trust among partners (Schilling and Phelps 2007).At the network level, density refers to the proportion of ties in the network over the maximum possible numberof ties.

Another common measure in understanding the structure of ecosystems is the average path length. Averagepath length measures how far (i.e., “steps”) any two firms are in an ecosystem. The shorter the path length, themore accessible and interconnected an ecosystem is. Modular communities are defined as groups of denselyinterconnected nodes that are only sparsely connected with the rest of the network. Small-world networks havecharacteristics of high clustering and small average distance between nodes.

3.2.4. Step 4: Visualization. Visualizations are a fundamental component of human learning and understandingand a key step in transforming data to knowledge (Card et al. 1999). They can be used to explore, interpret,and communicate data and aid decision makers with overcoming cognitive limitations. By mapping data tovisual encodings, visualizations of ecosystems make the “what, why, how, and who” explicit. Prior work hasprovided important novel and complementary insights into the structure, dynamics, and strategy of businessecosystems (Basole 2009, Basole et al. 2013, Iyer and Basole 2016).There are many different visual representations available, ranging from simple to complex. A comprehensive

review is beyond the scope of this paper, but interested readers are referred to Card et al. (1999) and Heeret al. (2010) for excellent overviews. Given that the structural aspect is one particular interest of this study, weleverage network visualization techniques to depict the interconnections between stakeholders in the FinTech

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ecosystem. Network visualizations require the development of appropriate types of representations, placementof graph elements on the screen, and efficient mapping of visual attributes for improved readability.There are many examples of network visualizations ranging from social, organizational, engineered, and

natural networks (Newman 2003). Visualizations of industry and market networks are also emerging and areused as complementary analyses to traditional statistical summaries (e.g., Rosenkopf and Schilling 2007). It hasalso been shown that graph visualizations are particularly valuable for understanding and analyzing businessissues, including competitive intelligence, strategy, scenario planning, and problem solving (Basole 2014).

Ecosystem visualization, however, is challenging and resource intensive. Complete or even comprehensiveecosystem data is generally not available. At the same time, even if the data are collected and appropriatelycurated, the amount of information can often be overwhelming to the analyst if not presented appropri-ately (Tufte and Graves-Morris 2014, North 2006). Effective visualizations must therefore ensure a careful balancebetween detail, abstraction, accuracy, efficiency, and aesthetics (Card et al. 1999).

For high-level descriptive measures of the FinTech ecosystem (e.g., growth in companies and funding; geo-graphic footprint), we use Tableau 10,4 a commercial off-the-shelf visualization software. We use Gephi 0.9,5 anopen-source software for visualizing and analyzing large network graphs, to create graphical representationsof the structure of the FinTech ecosystem (Bastian et al. 2009). Specifically, we use OpenORD, a force-directednetwork layout (Martin et al. 2011). A force-based layout is based on the idea that network entities are shapedby mechanical laws, assigning repulsive forces between nodes and attraction forces between endpoints of edges.The use of a force-based layout is particularly appealing when the motivating issue is to identify central orprominent nodes, peripheral actors, or clusters in an ecosystem. The OpenORD layout uses five stages thatleverage different physical “laws”: liquid, expansion, cooldown, crunch, and simmer. We use an initial param-eter configuration of these stages to emphasize core, periphery, and clusters. Moreover, to ensure readabilityand aesthetics, we followed several visual design principles, including no node overlap and edge crossing min-imization. In all our network visualization, node size is proportional to the firm’s importance as measuredby betweenness centrality. We use several node coloring schemes, including market segment, stakeholder type(company/investor, company/acquirer), and modularity class (i.e., what subcommunity it belongs to).3.2.5. Step 5: Sensemaking. The fundamental purpose of data-driven visualization goes beyond the renderingof aesthetically pleasing renderings and does not eliminate the need for human insight and foresight (Card et al.1999). While visualization is concerned with data transformation, representation, and interaction, ultimately itis about harnessing human visual perception capabilities to help identify trends, patterns, and outliers withcomputational capabilities (Card et al. 1999). It involves the formation of abstract visual metaphors in combina-tion with a human information discourse (interaction) that enables detection of the expected and discovery ofthe unexpected within massive, dynamically changing information spaces (Cook and Thomas 2005).Sensemaking has its roots in cognitive psychology and many different models have been developed (North

2006). The consensus across these models is that the sensemaking process is cyclic and interactive, involvingboth discovery and creation. During the generation loop an individual searches for representations. In the datacoverage loop, we instantiate these representations. Based on these insights, we shift our representation andbegin again. Together this forms a complete sensemaking loop. Visualization of ecosystems can therefore beseen to support the electronic market sensemaking process. Through visualizations we look for confirmation,inconsistencies, and possible “aha” moments. If confirmation is not achieved, we return to develop alternativevisualizations or specify new boundaries.

4. Analysis and Discussion of Results4.1. The Evolutionary Growth of FinTechWe began our analysis of the FinTech ecosystem with an exploration of the growth in both total startup com-panies founded and venture funding. Figure 3 shows the total startups in FinTech-related categories funded ineach year since 1995.6 We notice a steady growth in startups with a significant jump first in 2007 (132) and thenin 2011 (334). The maximum number of startups was founded in 2013 with 473. Interestingly, 2015 saw just 115new startups. The raw data suggests that while the FinTech ecosystem has grown tremendously over the pastdecade, the rate at which new startups are emerging is much slower, partly to a maturing of the industry aswell as a potentially more prominent role of incumbents. A similar trend can be observed by the total amountof venture funding in the FinTech ecosystem (see Figure 4). Funding has steadily increased until 2012 and thenhad a remarkable jump in 2013, increasing nearly 400% in 2014 to over $30 billion. While the graph indicates adip in 2015, more recent data indicates that funding in 2016 is continued to grow at this exponential pace.A more nuanced view into the differential growth of the FinTech ecosystem can be identified from the small

multiples visualization of market segments shown in Figure 5. Overall, there is a steady growth in the number

Basole and Patel: Visualizing the Global FinTech EcosystemService Science, 2018, vol. 10, no. 4, pp. 1–18, ©2018 INFORMS 9

Figure 3. Number of FinTech Ecosystem Companies Founded Since 1995

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of companies founded and total funding received across market segments. Some FinTech market segments (e.g.,investment management) have inherently longer timespans than others (e.g., fraud detection). Interesting is themeteoritic rise of virtual currency and Bitcoin—both in terms of companies founded and total funding—inrecent years, suggesting a more pronounced emphasis on emerging currency mechanism in the digital markets.At the same time, we observe that some segments have enjoyed greater acceleration in funding than others, ascan be seen for instance from the plateauing funding levels in, for example, billing and accounting.Together, these findings corroborate that there is a healthy evolutionary growth of the FinTech ecosystem,

however with segmental differences. More established segments are lagging behind in the growth; youngersegments are reaping most of the growth benefits both in terms of number of new companies and funding.

4.2. The Global Footprint of FinTechWe shift our attention now to the global footprint of the FinTech ecosystem. Most prior work on innova-tion ecosystems has suggested that emerging industries often concentrate regionally, leveraging and benefitingagglomeration effects. However, Figure 6, which depicts firm locations and the corresponding funding levels,clearly illustrates that the FinTech ecosystem is truly global, confirming similar findings from the footprint ofother digital ecosystems (e.g., Huhtamäki et al. 2017). All continents have FinTech players, but the distribution ishighly skewed. Only few Fin Tech players are headquartered in Asia, Australia, the Middle East, and Africa. Themajority of firms are located in the United States followed by Europe. However, notable international metropoli-tan locations do exist, including Singapore and Sydney. The greatest concentration of FinTech companies appearto be in the Bay Area as well as in the financial hubs of the world—New York, Chicago, London, and Paris.A closer examination of the U.S. market is particularly interesting, as it reveals a fairly broad coverage acrossall states and particularly dense footprints in California and the Northeast (i.e., New York, Boston). Of all loca-tions, Atlanta appears to have the highest average total funding, partly explained by the significant financialtransaction processing presence.

Figure 4. Yearly FinTech Funding Since 1995

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Basole and Patel: Visualizing the Global FinTech Ecosystem10 Service Science, 2018, vol. 10, no. 4, pp. 1–18, ©2018 INFORMS

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Basole and Patel: Visualizing the Global FinTech EcosystemService Science, 2018, vol. 10, no. 4, pp. 1–18, ©2018 INFORMS 11

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Basole and Patel: Visualizing the Global FinTech Ecosystem12 Service Science, 2018, vol. 10, no. 4, pp. 1–18, ©2018 INFORMS

Figure 7. Network Visualization of the FinTech Ecosystem Main Component (Nodes Colored by Market Segment and Sizedby Betweenness Centrality)

Accounting

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4.3. Investments and Acquisitions in FinTechOur analysis so far has focused on the evolutionary growth of the FinTech ecosystem. Figure 7 shows a networkvisualization of the FinTech ecosystem, with nodes depicting firms color encoded by market segments andedges representing investments and acquisitions. Following prior work (e.g., Schilling and Phelps 2007), wefocus on the main component of the ecosystem. Nodes are sized by betweenness centrality, indicating theirrelative prominence in the ecosystem. The visualization reveals several interesting things. First, there appearsto be a core set of FinTech ecosystem players located at the center of the visualization and a number of moreperipheral players. Interestingly, from the color distribution across the visualization we observe that the coreis composed of firms from a variety of market segments. In fact, the entire ecosystem does not appear to beclustered by market segment, but rather by a few prominent players.A closer inspection of this network visualization color encoded by acquirer (see Figure 8) and by investor (see

Figure 9) reveals some detailed differences. Comparatively, there is significantly less acquisition going on thaninvestments. According to our data only 8% of FinTech ecosystem firms pursue acquisitions, while more than60% pursue investments. Figures 8 and 9 label some of the more prominent acquirers and investors, respectively.

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Figure 8. Network Visualization of the FinTech Ecosystem Colored by Acquirer (Red) and Companies (Grey)

Square, American Express, Bank of America, JP Morgan and Chase, and Silicon Valley Bank, for instance, are allacquirers of FinTech companies, but occupy differing structural positions in the FinTech ecosystem. Traditionalbanks tend to be more centrally located in the ecosystem, indicating both their important role in the emergingFinTech industry as well as a less differentiating position in acquisition. In fact, most banks are acquirers ratherthan investors, suggesting a clear growth strategy by incumbents players. More recently formed companies,like Square, are located more peripherally but enjoy a high prominence because of very targeted acquisitions,particularly in banking and payments (as can be seen from Figure 7).In contrast to acquisitions, investments among FinTech companies is significantly more widespread. Figure 9

for instance shows that the majority of investments comes from both traditional venture capitalists (e.g., IntelCapital, SV Angel, Greylock Partners) and accelerators (e.g., Y Combinator, 500 Startups) as well as FinTechfocused investors (e.g., Block Chain Capital).

Table 3 presents the evolution of structural characteristics of the FinTech ecosystem (2006–2015). Our resultsshow that the FinTech is highly concentrated with 72% of companies in the main component. The averagenumber of relationships has increased steadily over this time period from, 1.109 in 2006 to 1.440 in 2015. Theaverage clustering coefficient, which can be viewed as a proxy to partner collaboration, has remained relativelystable. The average path length, on the other hand, has reduced significantly, suggesting that firms are becomingincreasingly interconnected and more accessible over time.

Figure 10 provides a macroview of the FinTech ecosystem, depicting the interconnection between the 24 mar-ket segments, rather than the individual companies. Nodes are sized and colored by degree (the number of

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Figure 9. Network Visualization of the FinTech Ecosystem Colored by Investor (Red) and Companies (Grey)

Table 3. Evolution of Structural Characteristics of the FinTech Ecosystem (2006–2015)

Full Core Deg. ≥ 5(2015) (2015) (2015) 2006 2007 2008 2009 2010 2011 2012 2013 2014

Nodes 6,493 4,685 711 359 569 771 1,131 1,465 1,937 2,520 3,142 3,539(100%) (72.2%) (10.95%) (5.53%) (8.76%) (11.87%) (17.42%) (22.56%) (29.83%) (38.81%) (48.39%) (54.5%)

Edges 7,894 6,748 1,761 398 676 968 1,448 1,972 2,723 3,626 4,578 5,206(100%) (85.5%) (22.31%) (5.04%) (8.56%) (12.26%) (18.34%) (24.98%) (34.49%) (45.93%) (57.99%) (65.95%)

Avg. degree 1.216 1.44 2.477 1.109 1.188 1.256 1.28 1.346 1.409 1.439 1.457 1.471Avg. weighted degree 2.286 2.854 6.826 3.524 3.578 3.672 3.538 3.513 3.458 3.261 3.104 2.978Network diameter 21 21 13 23 18 16 23 18 16 19 18 19Modularity 0.812 0.787 0.657 0.86 0.855 0.839 0.833 0.818 0.8 0.79 0.781 0.773Avg. clustering 0.003 0.003 0.007 0.007 0.005 0.003 0.003 0.002 0.002 0.003 0.002 0.003

coefficientAvg. path length 6.737 6.737 4.415 8.058 7.295 6.804 7.084 6.622 6.442 6.38 6.476 6.464Density 0.0001 0.001 0.007 0.006 0.004 0.003 0.002 0.002 0.001 0.001 0.001 0.001

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Figure 10. Network Visualization of the Interconnectedness of FinTech Ecosystem Market Segments

relationships). An edge between two nodes indicates how closely “related” two segments are from an invest-ment and acquisition perspective.7 The visualization shows that there is a macrocore consisting of six marketsegments (namely, FinTech and Financial Technology, Financial Services, Investment Management, PersonalFinance, Venture Capital, and Crowdfunding). Most others are peripheral. Bridging this core and peripheryare segments such as Banking, Credit, Credits Cards, Payment and Mobile Payments, and Trading, Brokers,and Financial Exchanges. Intuitively, the relation between segments makes sense. Transaction processing, forinstance, is closely related to Payment and Point of Sale; Bitcoin is closely associated with Virtual Currency andP2P Money Transfer. More enterprise/business-oriented segments are clustered together such as Billing, RiskManagement, Fraud Detection, and Accounting. FinTech and Financial Services, by its nature and classificationin this study, is central to all. Firms within these segments are driving and enabling the transformation of theecosystem and are thus critical to the evolutionary path. Emerging market segments like Bitcoin, however, arepositioned in between these and payments, suggesting a hub role to bridging existing and emerging marketsegments.

5. Concluding RemarksThis study provides descriptive insight into the structure of the FinTech ecosystem using data-driven visualiza-tions of 6,493 global companies across 24 market segments. We focus on two types of ecosystem entities, namely,(1) firms that create, offer, and acquire FinTech services; and (2) investors that fund and enable them. Ourexploratory analyses reveal a highly skewed global footprint of FinTech activities, differential growth patternsacross ecosystem segments, highly interdependent network structure, and a variegated nature of investmentsand acquisitions led by several key venture capital investors.

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Our analysis has several important practical implications. Our results show a rising trend toward marketconsolidation through acquisitions and mergers between investors, startups, and the financial incumbents. Asconsolidation sharpens, it is critical for incumbents and startups to adopt networked business strategies. Lever-aging “open” platforms, startups should make themselves reliant and integrated with other firms, while stillproviding a differentiated product or service to the end consumer. At the same time, the growing intercon-nectivity within FinTech also presents new opportunities for growth and innovation. Successful startups arefocusing on enhancing consumer experiences by unifying and integrating services across different FinTech cat-egories and aggregating data for advanced metrics and insights. This enhanced focus on integration and dataaggregation allows startups to survive in a rapidly expanding and tight connected ecosystem. From a global per-spective, FinTech provides unique, yet challenging routes to growth. With untapped markets across the globe,existing and new FinTech stakeholders can develop locally defined products and services, serving potentiallynovel services. These niches broaden the diversity of investment and acquisition opportunities for investors andincumbents.

Our analysis also provides interesting insights for existing financial institutions. Many incumbents that wantto grow vertically (across new categories) are left in a seemingly never-ending battle to stay up-to-date hor-izontally (in their current categories). Incumbents can alleviate this tension by acquiring or integrating withagile and well performing startups in both the horizontal and vertical directions. This provides for the bestgrowth-versus-cost ratio, while eliminating competitors and consolidating the ecosystem.

Overall, our analysis confirms the significant growth opportunities for incumbents, startups, and investorsin FinTech. With venture funding continuing to grow across all market segments, the future for FinTech stake-holders is bright despite significant market consolidation. Moreover, based on our findings we postulate thatincumbents, including “big” banks, will continue to play a central role in the FinTech ecosystem as they providethe basic foundational requirements. Yet, it is in the best interest of every stakeholder to harness their relationwith the incumbents and emerging startups to find mutually beneficial opportunities.

Theoretically, our study contributes to the “ecosystem as structure” line of research stream. Ecosystems, andthe networked relationships that shape them, are an increasingly common form of economic organization. Theresults of our visualizations have led to several interesting insights and hypotheses that should be pursedfurther. This research is, to the best of our knowledge, the first study to map the complex investment andacquisition relationships between startups and incumbents in the FinTech ecosystem. While every precautionwas taken to ensure the quality and rigor of our data, we are cognizant that some issues may exist. First, wecollected our data in early 2016. Given the rapid dynamics, we fully anticipate that the structure has furtherevolved, including new players and relationships. We also acknowledge that our source may be U.S. centric.A triangulation with other data sources, perhaps those Europe and Asia focused, would be valuable. Second,and related to the first limitation, our visualizations and analyses use a static (although longitudinal) perspective.Using both “updated” data sets and interactive visualizations, we can glean more accurate insights. Futureresearch should develop a FinTech ecosystem intelligence platform that allows decision makers and analysts toprobe the data more dynamically. Third, as with most ecosystems, segments and boundaries between segmentsevolve continuously. We used a bottom-up approach to validate our segments, but believe that a text-miningapproach may provide additional insights. Examining firm descriptions and 10-K statements would allow usto gain a more granular approach into what firms are actually doing, perhaps categorizing our current marketsegments. We believe that each of these limitations presents exciting future research opportunities. Our hope isthat other electronic markets researchers will embrace our data-driven approach and extend our work in noveldirections.

AcknowledgmentsThe authors would like to thank Hyunwoo Park, Peter Evans, and Mark Schoen for their comments and feedback on earlierversions of the paper.

Endnotes1See http://www.crunchbase.com.2For instances where the founding date was not available, we used the first investment or acquisition date (whichever came first) as thecorresponding founding date.3See https://networkx.github.io/.4See http://www.tableau.com.5See http://www.gephi.org.

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6We acknowledge that firms established and funded from 1995–2007 were not initially FinTech companies, but rather evolved to provideFinTech services. We provide this date range for historical context. The actual growth of FinTech-centric companies appears to begin in2007–2008.7We treat this network as bidirectional to indicate the reciproical nature between two segments.

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