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What do FinTechs actually do? A Taxonomy of FinTech Business Models

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FinTechs are companies that combine technological and financial attributes in their business models. In recent years, the rise of FinTechs has attracted much attention since they challenge incumbent financial service companies including the traditional banking model. In this paper, we aim to contribute to a better understanding of this phenomenon. Therefore, we develop a taxonomy of FinTech business models following a theoretically grounded and empirically validated approach for identifying and defining underlying business model elements. After developing our taxonomy, we use a clustering-based approach to identify business model archetypes on which to showcase our results, reexamine the assumptions made during taxonomy development, and validate the presented findings. Based on the gained insights, we discuss implications for research, practice and policy makers, as well as directions for future research.
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What do FinTechs actually do? A Taxonomy of FinTech
Business Models
Journal:
International Conference on Information Systems 2017
Manuscript ID
ICIS-1058-2017.R1
Track:
04. E-business and E-Government
Keywords:
Taxonomy, E-finance, FinTech, Digital business model, Digital
Transformation, Financial Technology
Abstract:
FinTechs are companies that combine technological and financial attributes
in their business models. In recent years, the rise of FinTechs has attracted
much attention since they challenge incumbent financial service companies
including the traditional banking model. In this paper, we aim to contribute
to a better understanding of this phenomenon. Therefore, we develop a
taxonomy of FinTech business models following a theoretically grounded
and empirically validated approach for identifying and defining underlying
business model elements. After developing our taxonomy, we use a
clustering-based approach to identify business model archetypes on which
to showcase our results, re-examine the assumptions made during
taxonomy development, and validate the presented findings. Based on the
gained insights, we discuss implications for research, practice and policy
makers, as well as directions for future research.
Taxonomy of FinTech Business Models
Thirty Eighth International Conference on Information Systems, South Korea 2017 1
What do FinTechs actually do?
A Taxonomy of FinTech Business Models
Completed Research Paper
1
Matthias Eickhoff
University of Goettingen
Platz der Goettinger Sieben 5
37073 Goettingen, Germany
meickho@uni-goettingen.de
Jan Muntermann
University of Goettingen
Platz der Goettinger Sieben 5
37073 Goettingen, Germany
muntermann@wiwi.uni-goettingen.de
Abstract
FinTechs are companies that combine technological and financial attributes in their
business models. In recent years, the rise of FinTechs has attracted much attention since
they challenge incumbent financial service companies including the traditional banking
model. In this paper, we aim to contribute to a better understanding of this phenomenon.
Therefore, we develop a taxonomy of FinTech business models following a theoretically
grounded and empirically validated approach for identifying and defining underlying
business model elements. After developing our taxonomy, we use a clustering-based
approach to identify business model archetypes on which to showcase our results, re-
examine the assumptions made during taxonomy development, and validate the
presented findings. Based on the gained insights, we discuss implications for research,
practice and policy makers, as well as directions for future research.
Keywords: Taxonomy, E-finance, FinTech, digital business model, digital
transformation, financial technology
Introduction
The financial services industry has always been characterized by a high affinity towards the use of
information technology (IT). Eventually, this has led to an inextricable interlocking of the financial services
industry and IT. However, in the past, IT was primarily a driver for cost-effectiveness and efficiency gains,
like the automation of processes. Exemplarily, financial transactions are completed without any physical
interaction (Puschmann 2017). More recently, the role of IT in general is undergoing a fundamental shift.
Digital transformation of whole industries is brought about by pervasive digital technologies (El Sawy and
Pereira 2013; Lucas Jr. et al. 2013). According to this new understanding of IT, companies create and
capture “[…] business value that is embodied in or enabled by IT” (Fichman et al. 2014). This
transformational impact can also be witnessed in the financial services industry via the emergence of new
1
All authors contributed equally to this work.
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Thirty Eighth International Conference on Information Systems, South Korea 2017 2
business models such as “robo-advisors”, and an increasing cross-industry competition with formerly pure
technology companies entering the financial market, such as Apple (Puschmann 2017). In sum, the
emergence of pervasive digital technology (collectively referring to mobile technologies, cloud computing,
big data analytics and social media) (Bharadwaj et al. 2013) triggered a shift in the role of technology,
moving beyond process automation towards the enabling role of new innovative (digital) business models
(Fichman et al. 2014; Teece 2010).
This development collectively refers to the movement of FinTechs. The term FinTech stems from the words
financial and technology and clearly indicates the markets in which these companies do their business. Yet,
due to the relatively recent emergence of FinTechs, there is no distinct agreement on or definition of what
a FinTech actually is. Recent contributions describe FinTechs broadly as an entrepreneurial phenomenon
in the financial services industry that leverages digital technologies. For example, Arner et al. (2015, p. 3)
define FinTechs as companies that use “[…] technology to deliver financial solutions,” and they are
similarly described by Lee and Teo (2015, p. 2) as companies offering[…] innovative financial services or
products delivered via technology. FinTechs are also accounted for challenging established roles, business
models and service offerings in the financial sector, which is particularly caused by the introduction of
technology-based innovations (Gomber et al. 2017). These aspects are covered by the definition of Sia et al.
(2016, p. 105) who define FinTechs as “a new generation of financial technology start-ups that are
revolutionizing the financial industry” and by Puschmann (2017, p. 74), who define them as “[…]
incremental or disruptive innovations in or in the context of the financial services industry induced by IT
developments resulting in new intra- or inter-organizational business models, products and services,
organizations, processes and systems. Against this background, we use the following definition in this
paper: FinTechs are companies that operate at the intersection of (i) financial products and services and
(ii) information technology, they are usually (iii) relatively new companies (often startups) with (iv) their
own innovative product or service offerings.
As digital technologies impact society at large and customers become increasingly technology-savvy, they
can easily draw on ubiquitous, readily available information. As a result, customers are more informed,
demand a higher level of transparency related to products and services, and are shifting their expectations
towards more diverse yet personalized offerings (Alt and Puschmann 2012; Granados and Gupta 2013;
Hansen and Sia 2015; Hedley et al. 2006). This development is a major driver of FinTech success and it
explains why FinTechs hold the potential to disrupt whole branches of the financial services industry:
FinTechs are often able to understand their customers better than incumbents and thus address their needs
more effectively (Mackenzie 2015).
Incumbents’ actions are often constrained by legacy systems, resulting in tension and the need to transform
and adapt to digital technologies (Gregory et al. 2015) while also meeting institutional expectations from,
e.g., regulators and analysts (Benner and Ranganathan 2012; Benner and Ranganathan 2013). In addition
to a decline in customers’ trust, many traditional financial services companies are affected by stricter
regulations as a consequence of the financial and EURO crises (Alt and Puschmann 2012). In contrast,
FinTechs are apparently less affected by these developments and the opposite seems to be the case:
regulators seem to struggle to keep up with the ongoing increase in the diffusion and adoption of digital
technologies alongside the creation of new innovative businesses (McGrath 2013; Rycroft 2006), resulting
in a “pacing problem (Marchant et al. 2011).
However, we also see that incumbents started to cooperate with FinTechs for value creation, leading to new
ecosystem setups. In sum, the rise of FinTechs is an important and relatively new phenomenon, which
addresses the changing role of IT, changing customer behavior, changing ecosystems, and changing
regulation in the financial services industry (Puschmann 2017). Given this new enabling role of IT for
business value creation in the financial industry, it is important to understand the similarities and
differences among different business models in the FinTech field. The business model concept is useful for
developing such an understanding as it provides “[…] a conceptual tool that contains a set of elements and
their relationships and allows expressing the business logic of a specific firm” and “[…] a description of
the value a company offers to one or several segments of customers and of the architecture of the firm and
its network of partners for creating marketing, and delivering this value and relationship capital, to
generate profitable and sustainable revenue streamsOsterwalder et al. (2005, p. 17). Against this
background, we aim at providing a rigorous overview of FinTech business models. Thereby, this paper
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Thirty Eighth International Conference on Information Systems, South Korea 2017 3
contributes to a better understanding of FinTech business models by answering the following research
question:
RQ1: What are the theoretically grounded and empirically validated elements of financial technology
companies’ (FinTech) business models?
RQ2: Which FinTech business model archetypes can be identified by an empirical examination of these
elements?
To answer these questions, we first develop a taxonomy of FinTech business models (RQ1), before applying
this taxonomy to our sample of FinTech companies using cluster analysis (RQ2), which yields a sample of
companies, for which we investigate whether typical patterns (archetypes) of business model elements can
be identified.
Theoretical Background
Classification Systems and Taxonomies
Maybe one of the earliest and best known publications of a classification system goes back to the botanist,
physician, and zoologist Carl Linnaeus who, amongst other important classification schemes, published the
“Systema Naturae” in 1758 providing a comprehensive classification of species of animals and plants
(Linnæus 1735). Since then, the need for ordering or classification of objects and phenomenon of interest
has been recognized as a fundamental form of science in most scientific disciplines as it aims at organizing
concepts of knowledge (Carper and Snizek 1980). Classification systems put structure to a field of
knowledge and can help researchers in further theory developing when hypothesizing and studying
relationships among described objects. They are useful to e.g., explain differences and similarities of
objects, as well as uncovering and classifying non-existent objects (Glass and Vessey 1995; Varshney et al.
2015). In the IS field, classification systems and taxonomies have themselves been classified as theory for
analyzing describing characteristics of objects or phenomenon and relationships between them (Gregor
2006).
As reported by Nickerson et al. (2013), in IS research the term “taxonomy” is widespread, and the authors
define it as a “set of dimensions each consisting of a set of mutually exclusive and collective exhaustive
characteristics” (Nickerson et al. 2013, p. 340), or more formally as follows:
T = {Di, i = 1, …, n | Di = {Cij, j = 1, …, ki, ki ≥ 2}}
Di (i=1, …, n) defines the n dimensions and Cij (j=1, …, ki) ki (ki≥2) the mutually exclusive and collectively
exhaustive characteristics Cij (j=1, …, ki) each dimension consists of. Here, mutually exclusive refers to the
property that no object has two different characteristics in a dimension, while collectively exhaustive is
used when each object has at least one characteristic in each dimension. Together, these two properties
assure that each object has exactly one characteristic in each single dimension. We use this definition in the
formal presentation of the developed taxonomy.
Conceptualizations of Business Models
In a recent review of the business model literature, Zott et al. (2011) found that the scholarly discourse is
very heterogeneous in regard to the question of “what is a business model?”. Generally, articles on business
models refer to them as presentations of building blocks. However, they often lack a clear definition of the
business model concept. Yet, Zott et al. (2011) show that the existing literature on business models can be
classified according to three generic themes: 1) e-business models where organizations make use of
information technology; 2) strategic issues, which address competitive advantage, value creation, and firm
performance; and 3) the management of innovation and technology (Zott et al. 2011). For logic reasons, we
focus on 1) e-business models, which suits our taxonomy development of FinTech business models and
includes the following contributions (Alt and Zimmermann 2001; Osterwalder et al. 2005).
Another extensive review of the business model literature is presented by Alt and Zimmermann (2001),
who find six common elements that business models consist of: mission, structure, processes, revenues,
legal issues, and technology. The mission is described as one of the more important elements of a business
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model. It encompasses an understanding ranging from corporate strategy down to products and services,
including the value proposition. In addition, a convincing business model is often led by a vision and not
just by the technology behind it. Structure highlights the actors and governance a company is engaging, i.e.,
its value network. Furthermore, it also describes the company’s geographic and industry focus. Processes
can be viewed as a more granular look at a business model’s mission and structure, which provides detailed
insight into the activities of value creation, i.e., customer orientation as well as coordination mechanisms.
Revenues define the business’ logic and sources of its revenue. Legal issues are an element that touches all
dimensions: potentially influencing the vision, structure, value creation processes, and revenue model.
Finally, technology can be an enabler of but also a constraint on a (technological) business model. Like legal
issues, technological developments may influence the mission, structures, processes, and revenue model of
a company.
Osterwalder et al. (2005, p. 12) identify nine common business model elements: value proposition, target
customer, distribution channel, relationship, value configuration, core competency, partner network, cost
structure, and revenue model. Value propositions provide information on what products and services a
company is offering. Target customer describes to whom the company intends to offer its products and
services, i.e., the value; distribution channels are the means and ways of how a company reaches out to its
customers; and relationship refers to the links a company creates between its target customers and itself.
These three elements (target customer, distribution channel, relationship) can also be subsumed under
customer interface. Value configuration is how resources are arranged in relation to a company’s activities;
core competencies highlight the competencies that are needed to carry out the (desired) business model;
and partner networks are the company’s cooperation with other actors that are needed to create and offer
the value. Value configuration, core competency and partner network can be categorized further as
infrastructure management. Finally, the last two elements of a business model highlight financial aspects.
The cost structure describes the monetary consequences for a business model to operate, and the revenue
model is the way the company receives money from its revenue streams (Osterwalder et al. 2005).
Practically oriented contributions already capture the categorization schemes of FinTechs (Bajorat 2016;
Levy 2015). However, they regularly lack a rigorous methodological foundation and fall short of describing
more than one dimension (usually limited to the product/service offering). But also scientific literature on
FinTechs in general and especially related to their business models are still scarce (Puschmann 2017).
Methodological Approach to Taxonomy Development
To address our first research question RQ1, we follow the method presented by Nickerson et al. (2013),
which has also been adopted by a number of other IS studies, such as Prat et al. (2015) and Tan et al. (2016).
The chosen method provides a structured process for developing taxonomies on the basis of existing
theoretical foundations (deduction), as well as empirical evidence (induction) in an iterative manner. In so
doing, we build upon the rich business model literature and conceptually derive the taxonomy’s dimensions.
Then, related characteristics are subsequently developed by empirically examining a large number of
globally diverse FinTech companies. The development of taxonomies usually focusses on a specific
phenomenon of interest, i.e., a meta characteristic, which is determined at the beginning of the process.
All dimensions and characteristics are based on the meta characteristic.
As Nickerson et al. (2013) explain, a taxonomy can be viewed as useful when it meets the following five
criteria, representing ending conditions during the iterative process of taxonomy development: (1) the
number of dimensions and characteristics should be limited to obtain a concise taxonomy that is easy to
apply and comprehend. (2) Yet, to make objects distinguishable from each other, there should be a sufficient
number of dimensions and characteristics, making the taxonomy robust. (3) If all relevant dimensions of
an object are identified, i.e., if all (or a random sample) can be classified, the taxonomy is comprehensive.
(4) The taxonomy’s dimensions and characteristics should also be extendable to account for possible new
objects in the future that may not fit in the existing taxonomy. (5) And finally, to understand the objects,
the taxonomy should be explanatory and not just descriptive.
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Thirty Eighth International Conference on Information Systems, South Korea 2017 5
Figure 1. Taxonomy development method (Nickerson et al. 2013, p. 345)
These five attributes are also known as subjective ending conditions of a taxonomy development process.
Objective ending conditions are as follows: there is no variation (merge, split or new additions) of objects,
dimensions or characteristics in the last iteration; all objects (or a representative sample) are analyzed;
every dimension, characteristic within the dimensions and combination of characteristics are unique; there
is at least one object categorized for each characteristic under its dimension.
The final taxonomy should satisfy both subjective and objective ending conditions as well as the initial given
definition of a taxonomy. During taxonomy development and after each iteration of revising dimensions
and/or characteristics of the taxonomy, the satisfaction of all ending conditions is checked. Only if all
ending conditions are satisfied, the process of taxonomy development is completed. Following and
documenting this structured approach helps to cope with the complexity inherent to taxonomy
development and to communicate the resulting taxonomy in a reproducible manner. During each iteration,
dimensions and/or characteristics of the taxonomy are revised on the basis of either deductive (conceptual-
to-empirical) or inductive (empirical-to-conceptual) reasoning.
Doing so allows to build upon existing theoretical foundations or, alternatively, empirical evidence. An
overview of all steps of the method suggested by Nickerson et al. (2013) is depicted in Figure 1. In our
process of taxonomy development presented in the following section it took four iterations (one conceptual-
to-empirical and three empirical-to-conceptual) to arrive at a final taxonomy fulfilling the objective and
subjective ending conditions.
start
determine meta-characteristic
determine ending condition
approach
identify (new) sub set of objects
identify common characteristics and
group objects
group characteristics into dimensions to
create (revise) taxonomy create (revise) taxonomy
examine objects for thes e characteristics
conceptualize (new) characteristics an d
dimensions of objects
ending
condition met?
end
no
yes
empirical-to-conceptual conceptual-to-empirical
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Thirty Eighth International Conference on Information Systems, South Korea 2017 6
Taxonomy Development
Dataset Description
During empirical-to-conceptual development iterations, we made use of the Crunchbase database
(Crunchbase 2016). Crunchbase is a company information database with a focus on the start-up
community. The database offers profiles of companies, investors and incubators, individuals, and events,
as well as the relationships between these entities. There are two ways to browse the information available
on Crunchbase. First, a web interface can be used to view information interactively. Second, an application
programming interface (API) is available to perform structured requests against the database. We use the
latter as our primary source of data.
Within the Crunchbase database, each company is assigned a number of attributes (tags), which help users
to assess companies or find firms with specific characteristics. For our purposes, we use this tag attribute
to request all firms in the database that have the “FinTech” tag. This results in a preliminary list of 2,340
companies. For each company, the database contains information such as name, country and city of origin,
a hyperlink to the company website, social media links, a founding, date, and a textual description of the
company.
We drop all companies for which no URL or textual description is available to exclude companies for which
no meaningful information is readily available, resulting in 2,040 companies as the basis for our analysis.
During the course of our analysis, more companies are dropped for similar reasons. As expected when
looking at an industry dominated by startups, many FinTechs are quickly bought, or they cease operations,
resulting in unreachable websites or redirects to new parent companies. When this is the case, the company
is dropped from the sample. Table 1 provides an overview of the regional distribution of our initial sample.
As shown, most companies are located in the United States or the European Union; however, we do not
limit the analysis to these centers of activity.
Per Country FinTech No. in Dataset (Countries with at least 4 FinTechs)
United States
942
Spain
15
Canada
28
Czech Republic
7
United Kingdom
192
Ireland
12
Mexico
28
Italy
7
Germany
50
Japan
12
Switzerland
25
Ukraine
7
India
40
South Africa
11
Netherlands
23
Thailand
6
Australia
38
Belgium
10
China
22
Latvia
5
Singapore
38
South Korea
10
Hong Kong
21
Luxembourg
5
France
33
Poland
9
Brazil
18
Philippines
6
Israel
29
Indonesia
8
Russia
16
Malta
4
Table 1. Companies coded by country of origin. Only countries with >3 companies in the
sample are reported in the table to save space, along with their color-coded (by country)
global distribution (all observations).
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Meta Characteristic
For our taxonomy, we are interested in the business models implemented by FinTechs. In line with
(Nickerson et al. 2013, p. 343) the selection of our meta characteristic was guided by the purpose of the
taxonomy and it was also based on existing (business model) theory. Consequently, we specify elements of
FinTech business models as our meta-characteristic.
1st Iteration
Building upon the rich amount of literature on business models, our first iteration involved following the
conceptual-to-empirical path of the applied method and, consequently, reviewing the existing knowledge
and identifying relevant key concepts from the literature. In doing so, we purposefully selected dimensions
that are useful for taxonomy development. We drop possible dimensions, in which many FinTechs are
similar or regarding which information about individual companies can be obtained. Specifically, we draw
on Zott et al. (2011) and, for the first iteration, we purposefully select D1=Dominant Technology Component
and D5=Revenue from Alt and Zimmermann (2001) and D2=Value Proposition, D3=Delivery Channel,
D4=Customer Segments, and D5=Revenue Stream from Osterwalder et al. (2005), which led to a
preliminary taxonomy with the following formal notation:
T = {
D1 Dominant
Technology
Component
| D1 =
{empty}
D2 Value Proposition
| D2 =
{empty}
D3 Delivery Channel
| D3 =
{empty}
D4 Customers
| D4 =
{empty}
D5 Revenue Stream
| D5 =
{empty}}
Due to the purely conceptual nature of the first iteration, several ending conditions were not met, e.g., all
objects (or a representative sample) are analyzed, as displayed in Table 2: Summary of the iterations and
ending.
2nd Iteration
For our second iteration, we followed an empirical-to-conceptual approach and analyzed the data on
FinTechs described in the previous section on Dataset Description. We started by drawing a random
sample of 150 companies that were labeled as FinTechs by the Crunchbase database. This sample was split,
and each of the authors was assigned to analyze 50 companies. Thus, we were able to derive suitable
characteristics for the dimensions obtained by the first iteration. The results of each author were discussed
and integrated into a single taxonomy.
For example, characteristics with a very similar meaning but different names were summarized as a single
characteristic, e.g., matching and intermediation to C2,5 Matching/Intermediation, or unification and
consolidation to C2,10 Unification/Consolidation. Furthermore, during this empirical iteration, we
identified the need for an additional dimension, D6=Product/Service Offering, and added it to our
taxonomy, which we did not include in the deductive first iteration. We added it as a new dimension in
addition to the existing value proposition dimension. We did this because when looking at the FinTech
companies in our sample, it becomes apparent that for many companies there is a clear distinction between
what is being delivered to the customer and the use the customer is expected to gain from the service or
product.
The newly added dimension and characteristics also indicated that our taxonomy has not yet reached all
ending conditions and is still changing significantly. In sum, we developed the following taxonomy for the
second iteration:
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Thirty Eighth International Conference on Information Systems, South Korea 2017 8
T = {
D1 Dominant Technology
Component
| D1 =
{C1,1 Advisor System, C1,2 Analytics, C1,3 Payment System, C1,4
Personal Assistant, C1,5 Recommender System, C1,6 Wallet, C1,7
Blockchain,
C1,8 Digital Platform}
D2 Value Proposition
| D2 =
{C2,1 Automation, C2,2 Collaboration, C2,3 Customization, C2,4
Insight,
C2,5 Matching/Intermediation, C2,6 Monetary, C2,7 Financial Risk,
C2,8 Transparency, C2,9 Trust, C2,10 Unification/Consolidation,
C2,11 Usability, C2,12 Convenience}
D3 Delivery Channel
| D3 =
{C3,1 API, C3,2 App, C3,3 Physical, C3,4 WWW, C3,5 WWW+App}
D4 Customers
| D4 =
{C4,1 B2B, C4,2 B2C, C4,3 B2B, B2C, C4,4 B2B2C, C4,5 B2C2B, C4,6
B2CB}
D5 Revenue Stream
| D5 =
{C5,1 Kickback, C5,2 Pay Per Use, C5,3 Revenue Share, C5,4 Sales,
C5,5 Subscription, C5,6 Unknown}
D6 Product/Service Offering
| D6 =
{C6,1 Comparison, C6,2 Data, C6,3 Information, C6,4 Lending}}
3rd Iteration
Next, we draw on a larger random sample of 600 companies, i.e., 200 per author, to test whether the
dimensions and characteristics developed during iteration two are stable enough. During this iteration, we
merged the characteristics C1,1 Advisor System, C1,2 Analytics, C1,4 Personal Assistant and C1,5
Recommender System with the newly added characteristic C1,9 Decision Support System.
The reason was that C1,1 Advisor System, C1,4 Personal Assistant and C1,5 Recommender System are very
similar function-wise and they all encompass C1,2 Analytics to some extent, which we subsumed to C1,9
Decision Support System. In addition, we merged the characteristics C1,3 Payment System and C1,6 Wallet
to the newly added overarching characteristic C1,6 Transaction Processing System. Further changes within
the Dimension D1 Dominant Technology Component were the addition of the characteristics C1,10
Marketplace and C1,11 Database. Within D4 Customers we condensed our taxonomy down to three
characteristics, C4,1 B2B, C4,2 B2C, C4,3 B2B, B2C, which makes the taxonomy more concise.
The most significant changes were in the dimension D6 Product/Service Offering. The first three
characteristics, C6,1 Comparison, C6,2 Data and C6,3 Information were merged to C6,3 Information
Aggregation. In addition, we identified ten new characteristics, namely C6,6 Brokerage, C6,7 Currency
Exchange, C6,8 Current Account, C6,9 Device, C6,10 Financial Education, C6,11 Financing, C6,12 Investments,
C6,13 Payment Service, C6,14 Personal Assistant and C6,15 Credit. Similar to the 2nd iteration, our taxonomy
still requires significant changes, indicating that the ending conditions have not been met. The taxonomy
at the end of iteration three is notated as follows:
T = {
D1 Dominant Technology
Component
| D1 =
{C1,7 Blockchain, C1,8 Digital Platform, C1,9 Decision Support
System,
C1,10 Marketplace, C1,11 Database, C1,12 Transaction Processing
System}
D2 Value Proposition
| D2 =
{C2,1 Automation, C2,2 Collaboration, C2,3 Customization, C2,4
Insight,
C2,5 Matching/Intermediation, C2,6 Monetary, C2,7 Financial Risk,
C2,8 Transparency, C2,10 Unification/Consolidation, C2,13 Security,
C2,14 Usability/Convenience}
D3 Delivery Channel
| D3 =
{C3,1 API, C3,2 App, C3,3 Physical, C3,4 WWW, C3,5 WWW+App,
C3,6 Instant Message}
D4 Customers
| D4 =
{C4,1 B2B, C4,2 B2C, C4,3 B2B, B2C}
D5 Revenue Stream
| D5 =
{C5,1 Kickback, C5,2 Pay Per Use, C5,3 Revenue Share, C5,4 Sales,
C5,5 Subscription, C5,6 Unknown}
D6 Product/Service Offering
| D6 =
{C6,4 Lending, C6,5 Information Aggregation, C6,6 Brokerage,
C6,7 Currency Exchange, C6,8 Current Account, C6,9 Device,
C6,10 Financial Education, C6,11 Financing, C6,12 Investments,
C6,13 Payment Service, C6,14 Personal Assistant, C6,15 Credit}}
4th Iteration
Last, we analyzed the remaining 1400 companies with a FinTech label. Within the dimension D6 Product/
Service Offering we merged the characteristics C6,4 Lending and C6,15 Credit to C6,16 Credit/Lending because
they were identical in their meaning. Furthermore, we added two characteristics to this dimension, namely,
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C6,17 Fraud Prevention and C6,18 User Identification. This led us to our final taxonomy. However, the last
iteration did not meet two objective ending conditions from Nickerson et al. (2013), i.e., no dimensions or
characteristics were merged or split, and no new dimensions or characteristics were added. Nevertheless,
we stopped the development process because, after this iteration, we analyzed the largest and remaining
proportion of the FinTech sample, yet our taxonomy experienced only marginal changes. The final
taxonomy is visualized in Table 3 FinTech Business Model Taxonomy with the following formal notation:
T = {
D1 Dominant Technology
Component
| D1 =
{C1,7 Blockchain, C1,8 Digital Platform, C1,9 Decision Support System,
C1,10 Marketplace, C1,11 Database, C1,12 Transaction Processing System}
D2 Value Proposition
| D2 =
{C2,1 Automation, C2,2 Collaboration, C2,3 Customization, C2,4 Insight,
C2,5 Matching/Intermediation, C2,6 Monetary, C2,7 Financial Risk,
C2,8 Transparency, C2,10 Unification/Consolidation, C2,13 Security,
C2,14 Usability/Convenience}
D3 Delivery Channel
| D3 =
{C3,1 API, C3,2 App, C3,3 Physical, C3,4 WWW, C3,5 WWW+App,
C3,6 Instant Message}
D4 Customers
| D4 =
{C4,1 B2B, C4,2 B2C, C4,3 B2B, B2C}
D5 Revenue Stream
| D5 =
{C5,1 Kickback, C5,2 Pay Per Use, C5,3 Revenue Share, C5,4 Sales,
C5,5 Subscription, C5,6 Unknown}
D6 Product/Service Offering
| D6 =
{C6,5 Information Aggregation, C6,6 Brokerage,
C6,7 Currency Exchange, C6,8 Current Account, C6,9 Device,
C6,10 Financial Education, C6,11 Financing, C6,12 Investments,
C6,13 Payment Service, C6,14 Personal Assistant, C6,16 Lending/Credit,
C6,17 Fraud Prevention, C6,18 User Identification }}
Finally, and in order to demonstrate the necessity of each iteration, Table 2 provides a summary of the four
iterations and to which extent each of them contributes to fulfilling the required ending conditions. As
shown, the first iteration (conceptual-to-empirical) only satisfied three ending conditions, while the
subsequent three iterations (empirical-to-conceptual) contributed to the satisfaction of the remaining
ending conditions. As all ending conditions are satisfied for our company sample after the four conducted
iterations, we consider the developed taxonomy finalized at this point. However, as the FinTech field keeps
evolving, which may lead to a future violation of an ending condition, the developed taxonomy may be
extended to reflect such changes by conducting additional development iterations.
Iteration
Ending Condition
1
2
3
4
conceptual
empirical
empirical
empirical
Taxonomy definition restrictions
Mutually exclusive: no object has two different characteristics in a
dimension
Collectively exhaustive: each object has at least one characteristic in each
dimension
Concise: dimensions and characteristics are limited
Robust: sufficient number of dimensions and characteristics
Comprehensive: identification of all (relevant) dimensions of an object
Extendable: possibility to easily add dimensions and characteristics in the
future
Explanatory: dimensions and characteristics sufficiently explain the object
(50)
● (600)
● (all)
All objects (or a representative sample) were analyzed
No object was merged or split
At least one object assigned to each characteristic
●*
No new dimensions or characteristics were added
●*
No dimensions or characteristics were merged or split
Every dimension is unique
Every characteristic within the dimension is unique
Every combination of characteristics is unique
Table 2. Summary of the iterations and ending conditions. * In these cases there is a minor
change, which we consider insignificant due to the size of our sample.
Page 9 of 19
Taxonomy of FinTech Business Models
Thirty Eighth International Conference on Information Systems, South Korea 2017 10
FinTech Business Model Taxonomy
Dimensions Di
D1 Dominant
Technology Component
D2 Value Proposition
D3 Delivery
Channel
D4 Customers
D5 Revenue
Stream
D6 Product/Service
Offering
Characteristics Cij
C1,7 Blockchain
C2,1 Automation
C3,1 API
C4,1 B2B
C5,1
Kickback
C6,5 Inform.
Aggregation
C1,8 Digital Platform
C2,2 Collaboration
C3,2 App
C4,2 B2C
C5,2 Pay Per
Use
C6,6 Brokerage
C1,9 Decision Support
System
C2,3 Customization
C3,3 Physical
C4,3 B2B, B2C
C5,3 Revenue
Share
C6,7 Currency
Exchange
C1,10 Marketplace
C2,4 Insight
C3,4 WWW
C5,4 Sales
C6,8 Current Account
C1,11 Database
C2,5 Matching/Intermediation
C3,5 WWW + App
C5,5
Subscription
C6,9 Device
C1,12 Transaction
Processing System
C2,6 Monetary
C3,6 Instant
Message
C5,6
Unknown
C6,10 Financial
Education
C2,7 Financial Risk
C5,7 Free
C6,11 Financing
C2,8 Transparency
C5,8 Hybrid
C6,12 Investments
C2,10
Unification/Consolidation
C6,13 Payment Service
C2,13 Security
C6,14 Personal
Assistant
C2,14 Convenience/Usability
C6,16 Lending/Credit
C6,17 Fraud
Prevention
C6,18 User
Identification
Table 3. FinTech Business Model Taxonomy. Overview of all Dimensions (Di) and
Characteristics (Ci, j). State after the conclusion of the development process following
Nickerson et al. (2013).
After the development iterations discussed above, we provide an answer to our first research question RQ1
and arrive at the final taxonomy presented in Table 3. As shown, the taxonomy of FinTech business models
contains six dimensions, each of which is composed of several characteristics. As discussed, this taxonomy
satisfies the formal requirements and ending conditions required by Nickerson et al. (2013). Of course, as
the developed taxonomy represents the state of the FinTech industry to-date, future additional
development iterations may uncover additional relevant dimensions and/or characteristics.
Because a useful taxonomy is explanatory, not just descriptive, and to make interpreting the taxonomy
easier, we elaborate on our definitions of critical characteristics that we do not consider self -explanatory.
To this end, the definitions of all dimensions are shown in Table 4. As shown, each dimension refers to
extant business model literature. Likewise, Table 6 (appendix) details the descriptions of each characteristic
contained in the product or service dimension, in the dominant technology dimension, and in the value
proposition dimension. We consider the characteristics of the other dimensions to be self-explanatory.
Dimension
Definition
D1 Dominant Technology
Component
Dominant IT artifact that is the driver for the IT-based business model (Alt and Zimmermann 2001;
Power 2004).
D2 Value Proposition
Describes the value the company creates for its ecosystem (customers, partners etc.) (Osterwalder
et al. 2005).
D3 Delivery Channel
Describes how the products and services are distributed to the customers (Osterwalder et al. 2005).
D4 Customers
Describes to whom the company intends to offer its products and services (Osterwalder et al. 2005).
D5 Revenue Stream
Describes how the company generates revenue from its products or services (Alt and Zimmermann
2001; Osterwalder et al. 2005).
D6 Product/Service Offering
Describes what the company offers to its Customers (Osterwalder et al. 2005).
Table 4. Definitions of taxonomy dimensions.
Page 10 of 19
Taxonomy of FinTech Business Models
Thirty Eighth International Conference on Information Systems, South Korea 2017 11
Archetypes of FinTech Business Models
In order to address our second research question RQ2, we build upon our developed taxonomy of FinTech
business models in order to identify typical patterns (archetypes) of business model elements from a large
collection of FinTech companies, which we also extracted from the Crunchbase database. The database
includes a set of business sector and technology tags for each firm.
We use these tags for a cluster-based validation of the previously identified dimensions and their
characteristics. The developed taxonomy should be able to identify a representative firm archetype for each
cluster, determined on the basis of the Crunchbase tags. This is also done to ensure that the most important
company-archetypes are represented in the presentation of our results. The clustering is based on the
entire company sample and is consequently unbiased by our prior taxonomy development , which did not
use these tags in order to preserve them for this demonstration, which can also serve as a check as to
whether the developed taxonomy can be applied to the raw data.
In particular, we use the multiscale bootstrap resampling approach implemented in the PVClust R-package
(Shimodaira 2004; Suzuki and Shimodaira 2006). In contrast to traditional approaches, this yields nearly
unbiased p-values for each cluster (Shimodaira 2004), allowing us to assess which clusters are significantly
different from their peers. This provides us with additional information when assessing whether clusters
are of interest to our analysis. Figure 2 shows the resulting cluster-dendrogram. As shown, the clustering
results in several sensible categories, such as a “Blockchain” (7) or “Cyber Security” (32) cluster. Still, to
develop these clusters into dimensions and their characteristics, further processing is needed, as not every
cluster is likely to yield informative distinctions according to our initial FinTech definition (see
Introduction).
Accordingly, the resulting tag clusters are examined in a two-stage analysis. First, we identify cluster-nodes
in the cluster-dendrogram, which seem like promising candidates for company archetypes. Second, the
companies in each cluster are re-examined manually, and the cluster is thus checked for coherence
regarding the business model of the firms contained therein to assess the usefulness of each cluster beyond
its quantitative presence. For the first step, a company is considered a member of a cluster if it has > 0 tags
in common with the cluster and not as many matches with another cluster.
As shown in Figure 3, the first step yields 24 candidates for relevant clusters, while 14 clusters remain after
the manual coherence check and are reported in Figure 3. Cluster candidates for step 1 are determined using
two criteria, the first of which serves as a sanity barrier, while the second serves as a focus check towards
taxonomy development:
Bitcoin
Cryptocurrency
Financial.Exchanges
Virtual Currency
Internet of Things
Telecommunications
Mobile
E-Commerce
E.Commerce Platforms
Credit Cards
Fraud Detection
Billing
Mobile Payments
Payments
Hardware
Software
Cyber Security
Security
Accounting
Apps
Artificial.Intelligence
Machine Learning
Data Visualization
Analytics
Big Data
Web Development
Cloud Data Services
Developer APIs
B2B
Retail Technology
Enterprise Software
Cloud Computing
SaaS
Real Estate
Commercial Real Estate
Crowdfunding
Consumer Lending
Credit
Small and Medium Businesses
Collaboration
Crowdsourcing
Impact Investing
Personal Finance
Wealth Management
Retail
Insurance
Banking
Financial Services
Risk Management
Hedge Funds
Transaction Processing
Business Intelligence
Consulting
Business Development
Non-Profit
Curated Web
Travel
Venture Capital
Automotive
Incubators
Marketplace
Intellectual Property
Legal
Information Services
Internet
Information Technology
Biotechnology
Health Care
Digital Media
Human Resources
Recruiting
Search
Stock Exchanges
EdTech
Education
Advertising
Social Media
News
Public Relations
0.3
0.5
0.7
0.9
Height
100
100
100
100
100
97
99
100 77
93 99
90 94 95
98 91
92 91
92
77 90
82 86
99 79
79 89 91
90 94
85
81 75 89 80
96 97
85
71 89
97 83 87 95
81
84 81 92
78 90 78
67
84
89 91
87 94
84
86 96
81 97
65 90 87
75 85
15 92 1
58
00
0
00
1
2
3
45
6
7
8
910
11 12
13 14 15
16 17
18 19
20
21 22
23 24
25 26
27 28 29
30 31
32
33 34 35 3637 38
39
40 41
42 43 44 45
46
47 48 49
50 51 52
53
55 56
57 58
59
60 61
62 63
64 65 66
67 68
69 70 71
72
73 74
75
76 77
54
100
Confidence
Cluster ID
Figure 2. Cluster dendrogram of firm tags as included in the Crunchbase database. Red
numbers represent approximately unbiased p-values (confidence) indicating cluster
significance (note: > .9 is equivalent to <.1 for normal p-values). Grey numbers represent
cluster IDs counted from the branches of the tree upwards. The difference in height
between clusters can be interpreted as a distance measure between clusters.
Page 11 of 19
Taxonomy of FinTech Business Models
Thirty Eighth International Conference on Information Systems, South Korea 2017 12
1. The chosen category should satisfy the discussed definition of a FinTech, i.e., it should describe
companies seeking to take part in areas of business traditionally covered by banks or the financial
services industry or establish novel business models relevant to these sectors.
2. The chosen categories should be sufficiently distinct from one another to be able to serve as a basis
for taxonomy development.
The result of step 1 is presented by cluster candidates A to X, shown in Figure 3. As described, each of these
clusters is consequently examined in more detail by checking the companies contained therein for
coherence and discarding a cluster if the contained companies are not FinTechs as defined in the context of
this paper. Additionally, we discard a cluster if it does not contain firms with business models that are
coherent regarding the tags the cluster contains or if a cluster does not contain firms.
2
The clusters violating
these conditions are marked with a red dot in Figure 3 below.
As noted, 14 clusters remain after these two steps. However, examining these clusters in detail, we noticed,
that some clusters are also close to each other in terms of their manifestations (Figure 3). Namely, B, and
E, I and J as well as Q, R, and W, which then were grouped to B+E, I+J and Q+R+W. In doing so, we
obtained ten partially grouped clusters, which we select by traveling upwards in the cluster hierarchy until
a unified cluster results that passes manual introspection. Next, and in order to provide an answer to our
second research question RQ2, the actual identification of FinTech business model archetypes is achieved.
This is done by reporting the dominant characteristic for the firms contained in these clusters for each
dimension of the developed taxonomy (see Table 6). The first column of Table 6 represents the manually
labeled different FinTech business model archetypes. The label is obtained by examining the most dominant
characteristics of each cluster and the cluster tags. For example, the FinTech business model archetype
“Payment Service” is described by cluster B+E with a dominant technology component of a transaction
processing system, the value proposition is mostly convenience/usability, which is usually delivered by an
app for B2B customers. Meanwhile, the revenue stream is unknown and the product/service offering is
logically a payment service.
Bitcoin
Cryptocurrency
Financial.Exchanges
Virtual.Currency
Internet.of.Things
Telecommunications
Mobile
E.Commerce
E.Commerce.Platforms
Credit.Cards
Fraud.Detection
Billing
Mobile.Payments
Payments
Hardware
Software
Cyber.Security
Security
Accounting
Apps
Artificial.Intelligence
Machine.Learning
Data.Visualization
Analytics
Big.Data
Web.Development
Cloud.Data.Services
Developer.APIs
B2B
Retail.Technology
Enterprise.Software
Cloud.Computing
SaaS
Real.Estate
Commercial.Real.Estate
Crowdfunding
Consumer.Lending
Credit
Small.and.Medium.Businesses
Collaboration
Crowdsourcing
Impact.Investing
Personal.Finance
Wealth.Management
Retail
Insurance
Banking
Financial.Services
Risk.Management
Hedge.Funds
Transaction.Processing
Business.Intelligence
Consulting
Business.Development
Non.Profit
Curated.Web
Travel
Venture.Capital
Automotive
Incubators
Marketplace
Intellectual.Property
Legal
Information.Services
Internet
Information.Technology
Biotechnology
Health.Care
Digital.Media
Human.Resources
Recruiting
Search
Stock.Exchanges
EdTech
Education Advertising
Social.Media
News
Public.Relations
A B C D E F G H I J K L M N O P Q R S T U V W X
Cluster
Candiate
Tag Name
Passes
Introspection ● ● ●●
Grouped
Clusters
A B + E F G H I + J K L Q + R + WO
Figure 3. Coding (three coder’s consensus) of clusters. To technological and
entrepreneurial categories. Colored columns only serve to emphasize the clusters. Red
dots indicate clusters that do not contain companies in line with the FinTech definition
used here.
2
No-firm clusters are possible because of the requirement that a firm is only assigned to a cluster if it has
more tags in common with this cluster than with any other, which makes the cluster assignment very strict.
As shown, this strict criterion still results in a variety of clusters.
Page 12 of 19
Taxonomy of FinTech Business Models
Thirty Eighth International Conference on Information Systems, South Korea 2017 13
Archetype
Label
Cluster
Dominant
Technology
Component
Value
Proposition
Delivery
Channel
Customers
Revenue
Stream
Product/
Service
Offering
Cryptocurrency
A
Blockchain
Convenience/
Usability
API
B2C
Unknown
Currency
Exchange
Payment Service
B_E
Transaction
Processing
System
Convenience/
Usability
App
B2B
Unknown
Payment
Service
Financial
Markets
Intermediary
F
Marketplace;
Transaction
Processing
System
Matching/
Intermediation; Security
Physical
/ WWW
B2B
Sales,
Unknown
Brokerage;
Device
Information
Aggregator
G
Decision
Support
System
Convenience/Usability
App
B2B, B2C
Unknown
Inform.
Aggregation
Information
Extractor
H
Decision
Support
System
Insight
App
B2B
Unknown
Inform.
Aggregation
Insourcer of
Sub-Processes
I_J
Decision
Support
System
Automation
App
B2B
Unknown
Inform.
Aggregation
Lending
Community
K
Marketplace
Monetary; Transparency
WWW
B2C
Revenue
Share;
Unknown
Lending/
Credit;
Financing
Alternative
Trading Venue
L
Marketplace
Matching/
Intermediation
WWW+
App
B2B, B2C
Unknown
Investments;
Lending/
Credit
Robo Advisor
O
Decision
Support
System
Monetary
App
B2C
Revenue
Share;
Unknown
Personal
Assistant
Co-Creator of
Financial
Analysis
Q_R_
W
Decision
Support
System
Convenience/Usability
App
B2B
Unknown
Inform.
Aggregation
Table 5. FinTech business model archetypes coded from dominant (most common
occurrence) characteristic for firms within each cluster resulting from coding in Figure 3.
As shown in the table, cluster A relates to cryptocurrency based business models, which typically relate to
consumer based convenience offerings. Cluster B + E contains firms offering payment services. The prime
example of firms contained in this cluster is given by PayPal, and many firms contained therein offer similar
but more specialized services. The companies contained in Cluster F focus on offering device based security
offerings that support other firms business processes. One example of this is supplying biometric
authentication for brokerage offerings.
Firms that are part of cluster G offer their customer information aggregation services, which focusses on
the supplying of information. Going further, firms in cluster H focus on extracting information from such
data. Cluster I + J offers similar services but the business offerings of firms in this cluster tend to be more
cloud-centric and revolve around hosting the entire service on the FinTechs servers. Cluster K contains
lending communities, which are marketplaces for lending or financing, and often focus on providing micro
funding or aim at providing a form of disintermediation. Similarly, cluster L contains firms that focus on
providing alternative trading venues.
Cluster O relates to financial “robo” advisors, which perform actions such as user specific portfolio
suggestions or credit scoring. Cluster Q+R+W offers co-creation of financial analysis, which involves tasks
such as credit risk assessment but also reaches into educational offerings in this area. These archetypes
present the answer to RQ2 by using the developed taxonomy to examine groups of firms regarding the
commonalities in their business models, and shows how the taxonomy can be applied to specific companies.
Page 13 of 19
Taxonomy of FinTech Business Models
Thirty Eighth International Conference on Information Systems, South Korea 2017 14
Implications
Implications for Research
The first implication of this paper is its contribution toward developing a consensus on the question of what
a FinTech actually is and upon what elements their business models are built. As discussed, due to the
rapidly changing landscape of FinTech companies and the relative youth of the “FinTech phenomenon”, the
lines are not distinct. Furthermore, the lines are blurred between traditional tech firms selling their
products to banks and the new phenomenon of FinTech, in which firms challenge the established banking
industry by providing either what was traditionally considered a banking service or entirely new related
services. To this end, the presented taxonomy of business models can be interpreted in terms of what it does
not include. Considered in conjunction with the above-mentioned existing definitions of FinTech firms, this
enables researchers to focus on the new phenomenon. Additionally, like all taxonomies, ours provides an
overview of the studied phenomenon. Thus, the dimensions and characteristics of FinTech business models
included in the taxonomy presented here help to identify different types of FinTech business models by
abstraction beyond the business model of individual firms. In conjunction with the presented clustering of
firm attributes, this allows for the identification of firms that are especially unlike each other, each of which
represents a different facet of the FinTech landscape. In addition, the developed taxonomy and archetypes
will assist researchers to find and position future contributions. Furthermore, the presented dimensions
and characteristics provide a basis for further theory development and theory testing related to the FinTech
phenomenon (Varshney et al. 2015). Finally, the iterative taxonomy development process described by
Nickerson et al. (2013) allows other researchers to extend the presented taxonomy by adding further
iterations if new FinTech business models are observed in the future.
Implications for Practice
For practitioners, business model taxonomies may at first appear very academic in the negative sense of
the word. However, they allow for the necessary abstraction needed to identify unoccupied business models,
as reflected by combinations of characteristics currently not offered by competing firms. While, naturally,
not every such combination is likely to be sensible, spotting what is not being done by others is an inherently
difficult problem for which academia can provide support by delivering abstractions, such as the presented
taxonomy. Additionally, incumbents can use the taxonomy to gain an overview of which traditional business
models are threatened by new competition and which new business models are being developed. Overall,
as the industry is still developing and highly dynamic, this is a critical feature to ensure the usef ulness of
any taxonomy in this industry.
Policy Implications
The dynamic nature of the FinTech movement presents policy makers with a number of challenges, such as
the identification of whether or which FinTech firms need to be subject to regulation. The presented
taxonomy can provide information regarding this question. The banking sector is a highly regulated
industry, in which incumbents comply with regulations regarding problems such as fraud prevention,
identity theft, organized crime, and sanctions against nation states. While regulators have established
processes to address these and many other concerns with incumbent firms, FinTechs have not been subject
to the same level of scrutiny if they themselves have not been classified as banks or providers of financial
services. This pacing problem (Marchant et al. 2011) has yet to be fully addressed by regulators. However,
the nature of FinTech business models implies that these firms face many of the same risks as traditional
banks. For example, they handle similarly sensitive customer information and may be targeted by illegal
activity, such as fraud. Thus, it is imperative for regulators to gain an overview of what business models are
being created in this new sector of the financial industry to identify FinTechs that may play a crucial role in
the financial industry, which may become relevant when considering market stability. While certainly not
every FinTech needs to face the same scrutiny as traditional banks, parts of this new industry may create a
need for new forms of regulation or an extension of the applicability of existing rules. Therefore, a taxonomy
of business models is needed to enable judgment, on a case by case basis, whether any regulatory
consequences should apply.
Page 14 of 19
Taxonomy of FinTech Business Models
Thirty Eighth International Conference on Information Systems, South Korea 2017 15
Limitations and Future Research
Limitations
The presented taxonomy should be used while keeping in mind several assumptions and decisions made
during its development. First, regarding the development of dimensions, the selection of dimensions based
on the business model literature is inherently selective. For other researchers, other dimensions may be of
more interest. As discussed, this may be addressed by adding new dimensions to the taxonomy and
performing additional development iterations when such extensions are desired. Due to the dynamic nature
of business and the FinTech movement in particular, we cannot exclude the possibility that, for a given
company, multiple possible characteristics exist, which contradicts the definition of Nickerson et al. (2013)
of mutually exclusive characteristics. In this research, we opted to map each company based on its dominant
characteristic if firms presented insurmountable challenges in this regard. Still, we developed the
characteristics of the taxonomy with this goal in mind.
Second, regarding the development of characteristics during our empirical-to-conceptual development
iterations, we were inherently limited to the companies contained in our sample. While this sample is quite
large, not every company has an inherent need to be listed in such a database. This is especially true for
non-US or non-EU firms.
Future Research
In this paper, we developed a taxonomy of FinTech business models. However, the aspiration to generality
limits the granularity of both the dimensions and characteristics developed to fit different types of FinTechs.
Thus, future research focusing on more specialized taxonomies may provide further insights. Additionally,
the developed taxonomy can be used to analyze the landscape of FinTech companies more directly. For
example, clustering can be performed on the characteristics assigned to each company, as opposed to the
approach chosen here, namely to cluster the tags not used during taxonomy development as a confirmatory
effort. Such clustering could help to identify which combinations of characteristics are common and show
patterns across different types of business models, identifying which roles are already being filled by
companies and which are not.
Additionally, the taxonomy can be used to analyze companies based on other data points available on
Crunchbase, such as funding success or the likelihood of long-term success, or which types of investors
favor particular types of FinTechs. As noted, the dynamic development of the FinTech field creates a need
for future investigation. New companies may follow entirely different business models than the ones
included in our dataset. Thus, future research may focus on exploring whether our taxonomy still holds.
Page 15 of 19
Taxonomy of FinTech Business Models
Thirty Eighth International Conference on Information Systems, South Korea 2017 16
Appendix A: Description of Characteristics
Definitions of Characteristics in selected Dimensions
Product or Service
Brokerage
The service acts as an intermediary between market participants and markets (Reuters 2016).
Credit/Lending
The product enables the customer to enter credit contracts as either lender or borrower (Oxford-
Dictionaries 2016).
Currency Exchange
"An exchange, or market, is a physical location or an electronic system in which securities are traded in an
orderly, regulated way […]" (Reuters 2016).
Current Account
"An account with a bank or building society from which money may be withdrawn without notice, typically
an active account catering for frequent deposits and withdrawals by cheque." (Oxford-Dictionaries 2016).
Device
"A thing made or adapted for a particular purpose, especially a piece of mechanical or electronic equipment."
(Oxford-Dictionaries 2016).
Financial Education
Improves the understanding of financial concepts or products (OECD Directorate for Financial and
Enterprise Affairs 2005).
Fraud Prevention
or Detection
The product intends to either prevent fraud from happening (fraud prevention) or detect fraud after the fact
(fraud detection) (Bolton and Hand 2002).
Information
Aggregation
The product collects information from multiple sources and provides the user with an aggregated
information basis.
Investments
Investing money for profit beyond lending money, e.g., real estate investments (Oxford-Dictionaries 2016).
Payment Service
A service that enables users to send and receive payments.
Personal Assistant
The system provides the user with recommendations on which the user may choose to act, e.g., the
recommendation of a portfolio structure (Resnick and Varian 1997), e.g., routing information.
User Identification
The system authenticates user identity (Todorov 2007).
Dominant Technology Component
Blockchain
„[...] is a distributed database in digital form maintaining a continuously-growing list of records which are
grouped into blocks and protected against malicious alteration through being encrypted and grouped into
blocks“ (Cohen et al. 2016).
DSS
„[...] are interactive computer-based systems, which help decision- makers interactive computer-based
systems to solve unstructured problems“ (Morton 1971).
Marketplace
„[...] facilitating the exchange of information, goods, services, and payments. In the process, they create
economic value for buyers, sellers, market intermediaries, and for society at large“ (Bakos 1998).
Platform
"[...] a building block, providing an essential function to a technological systemwhich acts as a foundation
upon which other firms can develop complementary products, technologies or services" (Gawer 2011).
Database
System
„[...] is basically a computerized record-keeping system; in other words, it is a computerized system whose
overall purpose is to store information and to allow users to retrieve and update that information on
demand“ (Date 2004).
Transaction
Processing System
„[...] is a collection of transaction programs designed to do the functions necessary to automate a given
business activity“ (Bernstein and Newcomer 2009).
Value Proposition
Automation
A machine agent (computerized system) that executes a function previously carried out by a human
(Parasuraman and Riley 1997; Parasuraman et al. 2000).
Collaboration
"The action of working with someone to produce something" (Oxford-Dictionaries 2016).
Convenience/
Usability
„The ease of use and the degree to which it is easy for the user to understand the system in order to use it
for its intended purpose“ (ISO 2010).
Customization
The ability to either customize a product according to the user’s wishes or letting the customer accomplish
such customization (Hart 1995; Pine et al. 1993).
Financial Risk
The product is intended to make financial risks, i.e., uncertainty about future returns due to market
developments, more manageable (Reuters 2016).
Insight
Provides the user with the means to advance his or her knowledge (Chang et al. 2009).
Matching/
Intermediation
The products make it easier for buyers and sellers to align their transaction intentions by providing them
with the means of discovering each other (Spulber 1996).
Monetary
The product promises financial gains.
Security
We follow the CIA-Triangle definition of security, i.e., the product intends to improve users' perceived or
actual security by addressing systems’ [C]onfidentiality, [I]ntegrity or [A]vailability (Avizienis et al. 2004).
Transparency
Increasing market fairness by enabling market participants to act on more timely or comprehensive
information (Madhavan 2000).
Unification/
Consolidation
"The action or process of combining a number of things into a single more effective or coherent whole."
(Oxford-Dictionaries 2016).
Table 6. Definitions of characteristics. For each characteristic, a reference to a description
or our own understanding of what is contained therein is provided.
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... The goal of the second phase was to systematically develop a taxonomy for ML-based FDSs that incorporates the most relevant dimensions. We applied the taxonomy-development method suggested by Nickerson et al. (2013), which provides a structured process for developing taxonomies based on existing theoretical foundations (deduction), as well as empirical evidence (induction) in an iterative manner (Eickhoff et al., 2017). Therefore, we conceptually develop the taxonomy and derive associated dimensions by building upon the previously gathered publications regarding ML-based FDSs (conceptual-to-empirical). Subsequently, related characteristics are drawn from empirically examining ML-based FDSs and associated components (empirical-to-conceptual). The applied taxonomy development approach has been applied by several other studies in the IS field, such as Tan et al. (2016) or Eickhoff et al. (2017), and has been shown to be useful. ...
... We applied the taxonomy-development method suggested by Nickerson et al. (2013), which provides a structured process for developing taxonomies based on existing theoretical foundations (deduction), as well as empirical evidence (induction) in an iterative manner (Eickhoff et al., 2017). Therefore, we conceptually develop the taxonomy and derive associated dimensions by building upon the previously gathered publications regarding ML-based FDSs (conceptual-to-empirical). Subsequently, related characteristics are drawn from empirically examining ML-based FDSs and associated components (empirical-to-conceptual). The applied taxonomy development approach has been applied by several other studies in the IS field, such as Tan et al. (2016) or Eickhoff et al. (2017), and has been shown to be useful. Moreover, this approach suits our research and assists us in addressing the research problem as follows: First, we can verify that specific FDS components are applied in practice and provide practicality by reviewing implementations and their evaluation in literature. ...
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... During their discussion at the International Conference on Information Systems, Eickhoff, Muntermann, & Weinrich (2018) presented the research outcomes from their investigation into what FinTech companies actually do. They offered a taxonomy they had designed based on their findings, illustrating the FinTech business models they had identified as being in use, to support their arguments, shown in Figure 6 below. ...
... From a policy and regulatory perspective, Eickhoff, Muntermann, & Weinrich (2018) argue that the FinTech business model taxonomy they developed, shown in Figure 6, section 2.3 above, could find useful application by regulators as they consider how to move forward. They suggest that not all FinTech business models require regulation of the type placed before traditional banks, dependent on their impact on the issue of stability. ...
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Since decades, the financial industry has experienced a continuous evolution in service delivery due to digitalization. This evolution is characterized by expanded connectivity and enhanced speed of information processing both at the customer interface and in back-office processes. Recently, there has been a shift in the focus of digitalization from improving the delivery of traditional tasks to introducing fundamentally new business opportunities and models for financial service companies. Digital Finance encompasses a magnitude of new financial products, financial businesses, finance-related software, and novel forms of customer communication and interaction—delivered by FinTech companies and innovative financial service providers. Against this backdrop, the research on finance and information systems has started to analyze these changes and the impact of digital progress on the financial sector. Therefore, this article reviews the current state of research in Digital Finance that deals with these novel and innovative business functions. Moreover, it gives an outlook on potential future research directions. As a conceptual basis for reviewing this field, the Digital Finance Cube, which embraces three key dimensions of Digital Finance and FinTech, i.e., the respective business functions, the technologies and technological concepts applied as well as the institutions concerned, is introduced. This conceptualization supports researchers and practitioners when orientating in the field of Digital Finance, allows for the arrangement of academic research relatively to each other, and enables for the revelation of the gaps in research.
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