FinTechs versus Traditional Banks: Business Model Scalability and Market Valuation

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FinTechs versus Traditional Banks
roberto.moro@unicatt.it www.morovisconti.com
1
FinTechs versus Traditional Banks:
Business Model Scalability and Market Valuation
Prof. Roberto Moro Visconti
– dept. of Business Management, Università
Cattolica del Sacro Cuore, Milan, Italy, roberto.moro@unicatt.it; www.morovisconti.com
Abstract
Financial technology (FinTech) is an industry composed of diversified companies, ranging from
promising startups to some seasoned firms that use technology to make financial services more
efficient. Fintech is recognized as one of the most critical innovations in the financial industry and is
evolving at a rapid speed, driven by the sharing economy, favorable regulation, and information
technology. Fintech promises to disrupt and reshape the financial industry by cutting costs, improving
the quality of financial services, and creating a more diverse and stable financial landscape.
FinTechs have a hybrid business model, as they operate in the financial (banking) sector deploying
their technological attitudes. Evaluators may so wonder if FinTechs follow the typical evaluation
patterns of bank/financial intermediaries or those of technological firms. Preliminary empirical
evidence shows that the latter interpretation is the one consistent with the stock-market mood, and the
business model of FinTechs.
The business model of FinTechs is intangible-driven, combining the e-finance, internet technologies,
social networking, artificial intelligence, blockchains, and big data analytics, and is more scalable than
that of traditional banks. These features impact on growth opportunities and trendy sustainability
patterns. This study goes beyond the extant literature, analyzing how the differences between FinTechs
and traditional banks are reflected in market valuation, and showing the potential for digital interaction
and cross-pollination of complementary business models.
Keywords: Financial innovation; digital money; Banking as a Service (BaaS); digital platforms;
financial ecosystem.
1. Introduction
Technological startups include companies operating in the Financial Technology segment (FinTech),
providing services and financial products with ICT technologies. FinTechs reformulate business
models, making use of innovative software and algorithms, value chains based on interactive computer
platforms, artificial intelligence, and big data.
Financial services, which focus on the transmission of information on digital platforms, rely on
innovative activities concerning the processing of data and their interpretation in real-time with
automated descriptive, prescriptive, and predictive technologies.
FinTech has become a hot term due to many driven forces, which include technical development,
business innovation expectations (market), cost-saving requirements, and customer demands (Gai et
al., 2018). Other factors concern the regulatory framework and the macroeconomic scenario
characterized by low-interest rates, leading to a reduction of the institutions’ profitability, and
promoting investments aimed to increase the organizations’ efficiency (Piobbici, Rajola & Frigerio,

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2019). Fintech refers to a vast and diverse industry that disrupts the industry, solving friction points
for consumers and businesses.
The banking industry is facing radical transformation and restructuring, as well as a move toward a
customer-centric platform-based model. The competition will increase as new players enter the
industry, but the long-term impact is more open. The regulation will decisively influence to what extent
BigTech will enter the industry and who the dominant players will be. The challenge for regulators
will be to keep a level playing field that strikes the right balance between fostering innovation and
preserving financial stability. Consumer protection concerns rise to the forefront (Vives, 2019).
The valuation issues of FinTech companies must be adapted to often young companies, given the
novelty of the sector, which have all the prerogatives of startups (in terms of expected growth, survival
rate, volatility, etc ...). The valuation methodologies must consider first the underlying business model.
According to Accenture (2016), there are two types of FinTech companies: competitive and
collaborative. Competitive companies are mature firms, not necessarily specializing in FinTech,
looking to squeeze out new competitors applying lower prices. In this case, it would be any of the
previously mentioned larger companies, as they make up the bulk of investments in FinTech.
Collaborative companies are those who offer services to enhance the position of competitors. FinTechs
cooperate with banks (Dorfleitner, Hornuf, 2018). Cooperation is primarily geared to the integration
or use of a FinTech application (product-related cooperation).
FinTechs are mushrooming as a response to well-known market inefficiencies along the financial
intermediation supply chain (Philippon 2019b) that are detrimental for end-users.
Within this framework, this study aims to compare the business model of FinTechs with that of
traditional banks.
The study is organized as follows. Paragraph 2 contains a short literature survey, showing that the
research question is original. Paragraph 3 describes the methodology and the research question in
further detail. Paragraph 4 reports the empirical evidence, with the stock market valuation, and the
multipliers of a sample of FinTechs, banks, and technological firms. The implications follow in the
subsequent sections: Paragraph 5 is dedicated to the FinTechs business model (as an explanatory factor
of the different market pricing if compared to banks). Paragraph 6 synthetizes some interactions
between FinTechs and banks, showing differences, converging patterns, and behavior during
pandemics. Paragraph 7 contains a discussion, concentrated on asymmetric risk patterns, whereas
paragraph 8 collects some concluding remarks.
2. The literature
A survey about FinTechs is contained in Mehrban et al., (2020); Razzaque & Hamdan (2020); Gai et
al., (2018), Allen et al., 2020, Philippon (2019b).
Literature streams may also consider other business model declinations, from insurtech (Wilamowicz,
2019) to proptech (Saiz, 2020), or suptech (Lemma, 2020), or services offered (e.g., payments, as
shown in Agarwal and Zhang, 2020).
Other literature streams focus on the interaction with innovative intangibles, like blockchains (Cai,
2018) or big data (Ferretti, 2018) or artificial intelligence (Jakšič and Marinč, 2019).
FinTech’s regulation is a further debated issue (Magnuson, 2018), and is a business model core purpose
in RegTech firms.
The relationship between banks and FinTechs is investigated in Romānova, and Kudinska (2016),
according to which recent tendencies require the banks to increase investment in FinTech, rethink

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service distribution channels, especially the business-to-consumers models, increase further
standardization of back-office functions, etc. Barba Navaretti et al. (2018) show that Fintech can ease
access to financial services, fostering competition by new players. To survive, incumbent banks will
have to react, face rising competitive pressure, and adopt new strategies.
The FinTech business can also mitigate financial inclusion concerns (Philippon, 2019a; Moro Visconti,
2019).
A comparative analysis of the business models of FinTechs versus traditional banks is, however,
missing. This study so fills a gap in the literature, linking the business model analysis with stock market
returns.
3. The methodology
The appraisal methodology may conveniently start from a synthetic recap of the main evaluation
approaches traditionally used for startups, banks, and technological firms that may be adapted to
FinTechs.
A strategic interpretation of the business model (that derives from the accounting data) is the second
step, to extract the key evaluation parameters to insert in the model, as shown in Figure 1.
Figure 1 – Evaluation Methodology
An analysis of the business model may conveniently consider:
1. The revenue model;
2. The strategic goals;
3. The growth drivers, including the franchise factor;
4. The expected investments;
5. Market trends.
Consistently with the background shortly illustrated in the introduction, the research question is
articulated in the following queries:
1. Which are the stock market price patterns of a (sample of) FinTechs, compared to banks
and technological stocks?
2. Which is the business model of FinTechs that supports the market valuation?
3. Which are the evaluation paradigms of FinTechs (consistently with their business models
and market price evidence)?
4. Which is the trendy positioning of FinTechs within the financial market ecosystem?
•Balance sheet
•Income
statement
•Cash Flow
statement
(Perspective)
Accounting
data
•time horizon
•strategic
assumptions
•sensitivity/sce
nario analysis
Business
Plan
(consistent with
the business
model)
•economic/finan-
cial data
•book versus
market values
•franchise factor /
excess returns /
goodwill
•market multipliers
Evaluation
parameters

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The methodology follows inductive reasoning, from the particular (the empirical evidence from the
stock market) to more general assumptions.
The evaluation criteria typically follow the (actual and prospective) business model of the target
company. The link between stock market prices and the business model valuation is consistent with
fundamental analysis, the study of a business's financial statements to make financial forecasts.
Fundamental analysis, introduced by Graham and Dodd (1934) attempts to measure a security's
intrinsic value by examining related economic and financial factors including the balance sheet,
strategic initiatives, microeconomic indicators, and consumer behavior. The intrinsic value is then
compared to the market value, to assess if the stock is over- or under- evaluated. If stock markets are
efficient at least in a semi-strong form, they incorporate all the public data (and so, fundamental
analysis) in their prices (Fama, 1970).
A comparison of the primary evaluation criteria in traditional (non-financial) firms (Pinto et al., 2019),
high-tech firms (startups), and banks / financial intermediaries is reported in Table 1. In an
equity valuation theory and practice, there are generally two valuation approaches: discounted cash
flows (DCF) and comparables (Mirzayev, 2020).
Table 1 – Comparison of the main evaluation approaches of traditional firms, technological startups,
and banks
Traditional
Firm
(Fernandez, 2001)
Technological Startup /
Internet company
(Moro Visconti, 2020a)
Bank
(Financial intermediary)
(Damodaran, 2009)
Balance-sheet based Venture Capital method (
IPEV,
2018; other methods)
Expected dividends per share /
Dividend Discount Models
Income Binomial trees / Real Option
Model (Saeterboe, 2019)
Adjusted book value of equity
(to proxy Market value)
Mixed
capital
-
income
Net Asset Value
Excess Return Mo
dels
Financial (D
iscounted
C
ash
F
lows
-
DCF
)
Market multiples (comparable firms)
Banking and financial activities follow peculiar valuation patterns that often concentrate on parameters
like adjusted equity or dividends. These parameters are, however, not particularly meaningful with
FinTechs (as shown in sections 5 and 6.1.), especially if they are in the startup phase.
If the FinTech activity is developed within a banking group by a captive company, its strategic meaning
may be that of a catalyzer of (traditional) banking activity. In this case, what mostly matters is not the
value of the FinTech as a stand-alone reality, but rather its contribution to the incremental marginality
of the (traditional) banking group it belongs. FinTechs naturally tend to cooperate with banks, as in
most cases they represent their customers. (Product-related) cooperation is primarily geared to the
integration or use of a FinTech application cooperation (Brandl and Hornuf, 2017).
Demyanova (2018) considers several methodologies that, in most cases, are hardly applicable to
FinTechs. For example, the liquidation value or book value method is not consistent with the innovative
nature of startups that become valueless if wound up and derive most of their potential value from
intangible assets. The Berkus method (five factors of success: idea, technology, employees, market-
entry, and the start of sales) appears too undetermined, and real options (Lee & Shin, 2018) may be
embedded in the estimate of future cash flows with multiple scenarios.

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Among the main evaluation methodologies of FinTech companies, the following are the most relevant:
1. Financial approach (Discounted Cash Flows – DCF);
2. Market comparables.
The financial approach is based on the principle that the market value of the company is equal to the
discounted value of the cash flows that the company can generate (“cash is king”). The determination
of the cash flows is of primary importance in the application of the approach, as is the consistency of
the discount rates adopted. The doctrine (especially the Anglo-Saxon one) believes that the financial
approach is the "ideal" solution for estimating the market value for limited periods. It is not possible to
make reliable estimates of cash flows for longer periods. The conceptually correct methods are those
based on cash flow discounting. However, other methods - even though they are conceptually incorrect
- continue to be used frequently (Fernandez, 2001).
The market (empirical) approach identifies how much investors are paying for similar investments.
In practice, an examination of the prices used in negotiations with companies in the same sector leads
to quantifying average parameters like those represented in Table 2.
4. Empirical Evidence
The empirical evidence is based on the stock market trend of a sample of FinTechs, compared to an
industry benchmark of banks or technological firms. Further insights are given by the metrics of market
multipliers of a sample of firms belonging to the three industries.
4.1. The Stock Market Value of a Sample of FinTechs and Banks
FinTechs have a hybrid business model, as they operate in the financial (banking) sector deploying
their technological attitudes. Evaluators may so wonder if FinTechs follow the typical evaluation
patterns of bank/financial intermediaries or those of technological firms. Preliminary empirical
evidence – reported below - shows that the latter interpretation is the one consistent with the stock-
market mood.
This indication is important for the assessment of the best evaluation criteria.
The following graph (with data sourced from Bloomberg) contains the comparative stock market price
(from August 1, 2015, to April 28, 2020) of:
a) IFINXNT - Indxx Global Fintech Thematic Index;
b) MXW00BK – MSCI World Banks Weighted Equity Index (large and mid-cap stocks across 23
Developed Markets (DM) countries and 26 Emerging Markets (EM) countries);
c) MXW00IT – MSCI World (ex-Australia) Information Technology Index.

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Figure 2 – FinTech versus Technological and Banking Stock Market Index
Despite the young age of FinTechs, many of these firms are experiencing significantly faster growth
than their traditional financial services peers. This reflects in the performance of FinTech companies
tracked by the Indxx Global Fintech Thematic Index, the underlying index for the Global X FinTech
ETF (FINX), relative to the Financial Select Sector Index. The Indxx Global Fintech Thematic Index
(https://www.indxx.com/indxx-global-fintech-thematic-index-tr) is designed to track the performance
of companies listed in developed markets that are offering technology-driven financial services which
are disrupting existing business models in the financial services and banking sectors.
FinTechs are more volatile than banks and they have shown a much deeper fall in March 2020 (when
stock markets started to reflect Covid-19 news), followed by a more sustained recovery, incorporating
the digital resilience typical of most technological firms.
4.2. Market Multipliers
Market multipliers are derived from the Bloomberg dataset, and represent a sample of 7 FinTechs,
compared to 5 banks and 3 Information Technology firms.
FinTech
Technology
Banks

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Table 2 - Market Multipliers of a Sample of FinTechs, Banks, and IT Firms
Firms P /sales P/E P / (free) cash flow P / Book Value EV / sales EV / EBITDA EV / EBIT Goodwill* WACC* EV* unlev. Beta*
USD/000 USD/000
FinTechs
Adyen NV
FY 2018 8.47 106.75 37.54 24.11 7.74 70.31 73.86 12,793,400 28.60 12,793,056 n.a.
FY 2019 8.18 106.56 42.26 25.31 7.63 72.60 78.90 20,277,827 13.85 20,157,283 n.a.
27/5/2020 12.21 158.81 63.16 37.74 11.75 111.72 121.43 31,206,358 10.83 31,206,358 0.88
Fidelity National Inf. Serv. Inc.
FY 2018 3.99 30.76 18.03 3.29 4.97 14.55 28.72 41,872,894 10.22 41,872,922 0.35
FY 2019 5.99 56.76 28.01 1.76 10.18 29.56 106.13 105,181,260 7.62 105,264,756 0.57
27/5/2020 6.09 57.43 28.99 1.71 9.12 26.80 135.95 103,590,099 8.91 103,590,099 n.a.
Fiserv Inc.
FY 2018 5.12 30.07 25.00 12.58 5.91 14.96 19.62 34,388,825 9.88 34,827,617 0.15
FY 2019 5.81 38.11 28.56 1.38 6.77 20.23 42.31 69,011,775 7.40 102,243,259 0.84
27/5/2020 5.11 40.05 27.25 2.23 7.49 22.18 49.99 93,231,528 9.15 93,231,528 0.67
Intuit Inc.
FY 2018 8.68 40.06 26.30 18.76 8.55 28.43 33.04 51,541,732 11.05 51,127,591 1.69
FY 2019 10.63 46.99 33.24 19.25 10.30 33.60 37.67 69,846,516 10.01 69,586,783 0.46
27/5/2020 10.74 55.56 39.36 15.52 10.25 38.78 45.35 70,281,672 10.18 70,281,672 1.10
Mastercard Inc.
FY 2018 13.14 25.35 33.33 36.05 12.88 24.87 26.44 192,548,150 11.67 192,886,660 0.44
FY 2019 17.99 38.46 39.13 51.02 17.91 29.40 31.22 302,391,130 8.96 302,974,610 0.42
27/5/2020 18.04 37.76 37.12 56.42 18.02 29.77 31.71 306,346,558 10.41 306,346,558 0.43
PayPal Holdings Inc.
FY 2018 6.44 49.13 21.37 6.42 5.93 30.85 41.76 91,610,660 12.47 91,947,020 2.53
FY 2019 7.15 50.60 32.92 7.51 6.84 32.34 44.32 121,638,410 9.21 121,767,397 0.32
27/5/2020 9.45 72.17 39.71 10.80 9.37 46.15 65.22 171,156,649 9.61 171,156,649 0.95
Visa Inc.
FY 2018 14.92 32.43 25.16 11.65 16.44 24.97 26.15 338,758,350 9.96 340,276,502 3.31
FY 2019 14.96 31.60 28.59 11.62 15.12 22.20 23.17 347,507,740 9.11 349,213,191 0.85
27/5/2020 15.98 34.55 32.06 12.93 16.17 23.55 24.77 386,248,625 9.23 386,248,625 0.73
Banks
Bank of America Corp.
FY 2018 2.27 9.22 6.29 0.98 n.a. n.a. n.a. n.a. 12.13 n.a. 0.77
FY 2019 2.91 12.46 5.35 1.29 n.a. n.a. n.a. n.a. 9.65 n.a. 0.64
27/5/2020 1.99 9.92 3.28 0.87 n.a. n.a. n.a. n .a. 10.79 n.a. 0.48
Citigroup Inc.
FY 2018 1.34 7.92 3.91 0.69 2.91 5.50 n.a. 282,880,392 11.95 286,715,642 0.64
FY 2019 1.74 9.33 n.a. 0.96 3.16 5.63 n.a. 326,895,286 9.99 332,413,364 0.69
27/5/2020 1.01 9.42 n.a. 0.58 2.67 5.22 n.a. 277,572,306 12.61 277,572,306 0.42
HSBC Holdings PLC
FY 2018 2.15 13.09 80.81 1.01 n.a. n.a. n.a. n.a. 13.42 n.a. 0.52
FY 2019 1.89 26.17 3.76 0.98 n.a. n.a. n.a. n.a. 9.82 n.a. 0.19
27/5/2020 1.80 26.95 n.a. 0.52 n.a. n.a. n.a. n .a. 8.06 n. a. 0.23
JPMorgan Cha se & Co
FY 2018 2.52 10.83 23.37 1.39 n.a. n.a. n.a. n.a. 11.29 n.a. 0.42
FY 2019 3.15 13.67 74.28 1.83 n.a. n.a. n.a. n.a. 8.87 n. a. 0.49
27/5/2020 2.19 10.96 n.a. 1.27 n.a. n.a. n.a. n .a. 9.97 n. a. 0.33
Wells Fargo & Co
FY 2018 2.19 11.33 6.13 1.22 n.a. n.a. n.a. n.a. 10.98 n.a. 0.56
FY 2019 2.27 12.60 35.12 1.34 n.a. n.a. n.a. n.a. 8.49 n. a. 0.47
27/5/2020 1.14 8.44 6.50 0.67 n.a. n.a. n.a. n .a. 9.94 n. a. 0.50
Information Technology
Apple Inc.
FY 2018 4.21 19.02 17.45 10.02 3.58 11.62 13.41 950,773,539 9.78 967,690,495 1.95
FY 2019 3.88 18.40 17.16 10.74 3.36 11.43 13.68 874,417,901 9.37 891,035,968 1.03
27/5/2020 5.26 24.85 21.14 17.46 4.85 16.49 19.75 1,298,730,925 9.22 1,298,730,925 0.45
Intel Corp.
FY 2018 3.04 10.49 15.13 2.82 3.10 6.78 9.43 219,790,000 10.99 222,034,000 n.a.
FY 2019 3.69 13.89 15.67 3.32 3.75 8.18 12.25 270,204,200 8.95 273,809,000 0.75
27/5/2020 3.58 12.52 14.97 3.46 3.69 7.69 11.21 279,183,560 8.86 279,183,560 0.68
Microsoft Corp.
FY 2018 6.88 26.90 23.54 9.15 6.44 15.15 20.17 710,768,970 10.91 1.75
FY 2019 8.17 28.60 26.87 10.01 7.76 17.33 22.61 976,492,280 9.69 0.42
27/5/2020 9.99 32.05 31.94 12.04 9.54 20.02 25.35 1,323,324,245 8.18 1,323,324,245 0.53
The data reported above are uneasy to interpret due to their heterogeneity, even if they show some
trendy features that may be summarized as follows:
• The (stock market) price compared to sales (P/sales) or expected earnings (P/E) or liquidity
(P/cash flows) clearly shows that FinTechs command a significant premium over banks (and a
smaller premium over the restricted sample of IT firms). This is a strong rationale behind the
higher stock market price of FinTechs;
• The Price / Book Value is a proxy of the Tobin Q (the ratio between a physical asset's market
value and its replacement value); whenever P/BV > 1, the firm incorporates an implicit
goodwill, since the market value of equity exceeds the book value. P/BV metrics in FinTechs
and IT firms are significantly higher than those that represent banks. Banks often have a

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P/BV<1, meaning that there is a “valuation badwill”, since the market value is lower than the
book value. This occurrence is unusual in listed stocks, and show the negative attitude of
investors towards banks;
• Other multipliers compare the Enterprise Value (EV, the market value of the listed firms,
including its financial debt) to sales, EBITDA, or EBIT. The ratio EV/EBITDA is particularly
meaningful, being used for the market value estimate (EV/EBITDA * expected EBITDA ≈
expected EV). Even these multipliers show a meaningful premium for FinTechs, against banks
(or even, to a lesser extent, technological firms);
• The goodwill and the EV express the cumulated wealth (and tend to coincide);
• The Weighted Average Cost of Capital (WACC) is the rate used to discount operating cash
flows (i.e., liquidity before debt service). The higher, the riskier the firm. FinTechs are on
average no riskier than banks or IT firms. This is confirmed also by the unlevered beta, a
complementary parameter that measures the market risk of the company (sensitivity to the
market index) without the impact of debt.
5. FinTech Business Models
FinTech is an elastic business that can concentrate on market niches and specific customer segments,
leveraging an innovative use of (big) data, and proposing new disruptive products and services.
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.
FinTechs can complementarily be a:
a) A catalyzer/upgrader (digital enabler) of traditional business models, bringing to efficiency
gains and pollinating the activity of ordinary banks or other financial intermediaries; FinTech
providers use technology to disrupt these services by offering consumers a more compelling
offering such as enhanced capabilities, convenience, or lower prices and fees (EY, 2019).
b) A pioneer of innovative products and services, normally through a B2B channel. An invented
service is one that did not exist before but is now possible by technology and alternative
business models, such as peer-to-peer lending and mobile-phone payments. Some invented
services fill niches in the market, and others have the potential to redefine and transform entire
financial subsectors (EY, 2019).
Innovation may for instance concern:
• Digital platform economy: handling of third parties: improving existing processes – coopetition
as a new business model;
• Open architectures & cloud: open vision – biometric & geolocalization to improve security
standards;
• Change management – new legacies;
• Frictionless processes for client onboarding.
The main areas of activity are (Haddad & Hornuf, 2019; Gai et al., 2018):

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• Financial technologies applied to blockchains and distributed ledger technology based on data
archives, whose records are public on a computer network and without the need for a central
register;
• Crypto and digital money;
• Peer-to-peer loans (P2P);
• Smart contracts (using the blockchain) that automatically execute contracts between buyers and
sellers;
• Open banking supported by the blockchain applications that create a service through a connected
network of financial institutions and third-party providers.
• IT security, through or decentralized storage of data, and anti-fraud systems;
• Applications in the insurance field (InsurTech) or regulation (RegTech);
• Asset management (robo-advice, social trading, wealth management, personal financial
management apps, or software).
Table 3 synthesizes the FinTechs main typologies and business models (see also Tanda & Schena,
2019; Das, 2019).
Table 3 –FinTech typologies and business models
Typology Business Model
Financing
solutions
Pure equity crowdfunding (retail); club deals; funding from institutional investors.
Blockchain
The blockchain is a decentralized and distributed digital ledger that corresponds to an
open database with a pattern of sharable and unmodifiable data that are sequenced in
chronological order. The main applications are cryptocurrencies; banking and payments;
cyber-security; supply chain management; forecasting; networking & IoT; insurance;
private transport & ride-sharing; cloud storage; charity; voting; healthcare;
crowdfunding.
Payment
systems
and
processing
(PayTech)
Credit cards; mobile payments through apps; virtual POS; online wallet; money
transfers. Payment innovations throughout the year have been largely all about mobile
e-wallets and contactless payments. PayTech firms also focused on ensuring the security
of transactions leveraging artificial intelligence (AI) and machine learning (ML)
technologies.
Global consumers have grown less reliant on cash, enhancing the growth profile of
mobile payments firms
.
P2P
loans
Peer-to-peer (P2P) lending is the practice of lending money to individuals or businesses
through online services that match lenders with borrowers. Peer-to-peer lending
companies often offer their services online and attempt to operate with
lower overhead and provide their services more cheaply than traditional financial
institutions.
Open
Banking
In October 2015, the European Parliament adopted a revised Payment Services
Directive, known as PSD2. The new rules included aims to promote the development of
neo-banks or challenger-banks use of innovative online and mobile payments through
open banking.
Big Data
&
Analytics
Big data analytics is the often-complex process of examining large and varied data sets,
or big data, to uncover information - such as hidden patterns, unknown correlations,
market trends, and customer preferences - that can help organizations make informed
business decisions. Big data based on payment transaction data provide insight into
customer retention, identification of crimi
nal activities, or future customer
behavior.
Insurtech Insurtech refers to the use of technology innovations designed to squeeze out savings
and efficiency from the current
insurance
indus
try model.

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Typology Business Model
RegTech
Regulatory technology, in short, RegTech, is a new technology that uses information
technology to enhance regulatory processes. With its main application in the Financial
sector, it is expanding into any regulated business with an appeal for the Consumer
Goods Industry. Regtech, post-financial crisis - with MiFiD II, Basel III, and GDPR -
may have been the initial external driver to ensure full compliance, and this has ensured
a dramatic rise in technological solutions, and crucial in increasing efficiency, for
example, by reducing gap
-
analysis time.
SupTech
Use of innovative technology (big data, artificial intelligence, blockchains, etc.) by
supervisory agencies to support supervision. Suptech will help authorities to become
mor
e dat
a
-
driven (Di Castri et al., 2019).
Micro
FinTech
FinTech applications to microfinance activities (microcredit; microdeposits;
microinsurance; micro-consulting). M-banking boosts volumes and fosters marginality
gains. Improved sustainability positively affects the outreach of the unbanked (Moro
Visconti, 2019a).
Banking-
as-a-Service
End-to-end process ensuring the overall execution of a financial service provided over
the web.
Artificial
Intelligence
AI will transform nearly every aspect of the financial service industry. Automated wealth
management, customer verification, and open banking all provide opportunities for AI
solution providers.
PropTech Property technology (PropTech), sometimes called Real estate technology, encompasses
the application of
information technology
and
platform
economics to
real estate
markets
.
6. Fintechs versus Banks (Growth versus Maturity)
FinTechs and traditional banks operate in the same (financial) market and sometimes share common
clients. They are also part of a contiguous supply and value chain. It is so worth wondering why they
are different (as shown in § 6.1.), and how they may converge, thanks to cross-pollination, and scalable
synergies (§ 6.2.). FinTechs (and M-banking) may also represent a digital shelter during pandemics (§
6.3.).
6.1. Why are FinTechs different?
FinTechs seem far from the banks even because they have a different model, as they do not collect
deposits and lend money, intermediating financial resources. FinTechs are not hyper-regulated deposit-
taking institutions, and they just provide financial service and do not intermediate “money” as a
product, and they do not need a supervisory capital like banks. The very fact that FinTechs are not
deposit-taking institutions is possibly the main differential factor from banks. Banks are both labor-
and capital- intensive, to fuel a business model that still relies on “physical branching” and requires
huge compliance-absorbing resources.
The preliminary conclusion that FinTechs follow the evaluation parameters of technological firms has,
however, some caveats that may tentatively be summarized as follows:
a) If FinTech firms are the purchase target of (much bigger and consolidated) ordinary banks /
financial intermediaries, then the valuation criteria of the latter predominate, at least after the
acquisition (and especially if FinTechs are merged into traditional banks);
b) The underlying market and business model of maturing FinTechs may become less
technological and more “client-based”;

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11
c) Some established criteria used in the evaluation of traditional banks are, however, hardly
applicable even in perspective (e.g., consideration of “physical” banking branches as a positive
element).
The business model of a bank is vastly different from that of a typical FinTech and this difference
reflects in the balance sheet and in the income statement.
The balance sheet of a bank is characterized by a binding structure, due to the presence of the
supervisory capital and bank deposits (in the liabilities) and loans to customers (within the assets). The
assets and liabilities structure of a typical FinTech is much “lighter”, being represented by net working
capital and some capitalized assets (tangible and intangible), against equity and financial debt in the
liabilities.
The income statement reflects these differences:
• the bank has economic margins represented by the interest rate differential and the net
contribution of commissions; interest rate margins are still compressed by historically low
market rates (due to the soft monetary policy of Central Banks, to stimulate the economy), and
the long wave of the 2008 recession (with huge amounts on Non-Performing Loans); the
pandemic crisis of 2020 may fuel new defaults, in a context where low marginality may not be
sufficient to absorb growing credit delinquency
• the FinTech has a more standard EBITDA and EBIT, sourced by the difference between
operating revenues (from services) and monetary OPEX (to get to the EBITDA) or
comprehensive OPEX, including depreciation and amortization, to determine the EBIT.
FinTechs business model is less dependent on labor and capital, although more exposed to
technological investments, whose returns are intrinsically risky.
Other differences reflect on systemic risk. Whereas banks are traditionally a major source of contagion,
FinTechs are much more segmented and their probability of default (very frequent in startups) is
mitigated by their limited leverage. Startups are typically debt-free, and in this case operating result
(EBIT) or cash flow tends to coincide with net result or cash flow, cost of equity replaces WACC, and
Enterprise Value equals Equity Value (Moro Visconti, 2020b).
Banks are strictly connected among themselves, with their clients (borrowers), suppliers (depositors),
and regulators, and any concern about their stability may have severe systemic implications.
The different business model of FinTechs versus banks has strong scalability implications. Whereas
the operating profits (interest and intermediation margin) of a standard bank are difficult to scale up
(unless the volumes of loans consistently increase, which is highly risky), the EBITDA / EBIT of a
FinTech may follow the blitzscaling trend of many successful technological startups.
The intrinsic scalability of a FinTech depends also on the plasticity of its business model that can be
extended to many applications (e.g., RegTech, InsurTech, PropTech, etc.), using synergistic
technologies and products (e.g., blockchains, artificial intelligence, big data, digital platforms, etc.).
FinTech so embody real option features (to expand, contract, defer their business development) that
may ignite scalable growth opportunities (incorporated in market valuations).
A more analytical explanation of the intrinsic value of FinTechs may be conducted considering classic
models like the Economic Value Added, the Residual Income Model, or the Franchise Value Model
(for a survey, see Patev and Petkov, 2018).

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The impact of growth assets on the “accounting” system (represented by the interaction of the balance
sheet with the income and the cash flow statement, bringing to the market value estimate using the
Discounted Cash Flows or the market multipliers) is represented in Figure 3.
Figure 3 –Impact of Growth Assets on Valuation Metrics
Income Statement
revenues Implicit Goodwill ROIC > WACC; MVA > 0
- monetary OPEX Assets in Place Financial Debt
= EBITDA* CAPEX (Net Financial Position)
+ (growth) EBITDA** NWC
- depreciation/amortization
EBIT Growth Assets Equity
- interest rates, taxes …
Net Result Invested Capital = Raised Capital
Cash Flow Statement
EBITDA*
±Δ Operating Net Working Capital (NWC)
±Δ CAPEX
Operating Cash Flow* (unlevered / debt-free)
+ (growth) EBITDA** EBITDA* + (growth) EBITDA** x n x EV/(EBITDA*+EBITDA**)= EV**
Adjusted Operating Cash Flow**
- debt service …
Net Cash Flow (Free Cash Flow to Equity)
Balance Sheet
discounted at WACC*
discounted at WACC* +
risk premium = WACC**
discounted at cost
of equity - Ke
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market
capitalization
Enterprise
Value (EV)
market
ratios
Discounted Cash Flows
The presence of growth assets in the balance sheet ignites excess returns (also used in bank valuation,
as shown in table 1) and a positive Economic Value Added (EVA). EVA is a performance measure
devised by Bennet Stewart (1991), based on the difference between the return and the cost of capital.
It is obtained by subtracting the cost of capital employed from the operating result (= EBIT) normalized
and after taxes (NOPAT):
EVA = NOPAT - WACC * Ic = (ROIC - WACC) * Ic [1]
where:
•
NOPAT = normalized operating income after taxes;
•
Ic = [adjusted] invested capital (shareholders' equity + financial debts + equity equivalents);
•
ROIC = NOPAT / Ic = [adjusted] return on invested capital;
•
WACC = weighted average cost of capital.

FinTechs versus Traditional Banks
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13
Cumulation of positive EVA across years produces a positive and growing Market Value Added
(MVA). MVA is the difference between the market value and the invested capital, equivalent to the
sum of the discounted future EVA:
MVA = market value - invested capital = present value of all future EVA = EVA
1
/ (WACC - g) =
(economic profit of existing assets and growth opportunities) / WACC [2]
The MVA is the measure of the value that a company has created in excess (goodwill) compared to the
resources already bound to the company. This relates to the measure of the excess market value
concerning the book value of the capital raised. When MVA > 0, Price / Book Value > 1.
The implicit (not recorded) goodwill occurs whenever ROIC > WACC (the market return on invested
capital exceeds the weighted average cost of capital), and is a surrogate of EVA or the franchise
Price/earnings factor determined by the difference between the return on the new business opportunity
and the cost of equity.
These growth options embedded in the assets and depending on the innovative business model of the
target FinTech are likely to produce excess economic returns that increase the EBITDA (EBITDA* +
growth EBITDA**). A higher EBITDA** increases the liquidity produced within the income
statement and positively affects the Operating Cash Flow (Operating Cash Flow* + growth EBITDA**
= Adjusted Operating Cash Flow**). The Net Cash Flow, after debt service, is also positively affected
by growth.
The valuation metrics, considering the DCF or the market multipliers, records the marginal impact of
growth, whose riskier occurrence is, however, to be discounted at a higher rate. According to
Damodaran (2018), p. 5 “firms generate cash flows from multiple assets … so the discount rates we
use should be different for each set of cash flows”. The scaling effect which drives the growth rate is
difficult to be maintained in the long run, and forecasts of firms with little track-record are intrinsically
riskier. Technological discontinuity also impacts on market risk, threatening the business of incumbent
FinTechs. For these very reasons, the discount rate should fairly incorporate this hardly predictable
outlook that also reflects potential changes in risk over time. The value creed says rapid growth must
eventually peter out.
Traditional banks that operate in a mature market embed in their business model limited growth
potential. Mature businesses are typically safer than innovative ones (and that is why scalable growth
is discounted at a higher rate of risk) but this may not be the case for old-fashioned banks that front a
recession. The market mood, as indicated in Figure 2 and Table 2, discounts this credit-driven risk that
does not appear significantly different from growth (technological) risk.
6.2. Cross-Pollination and Scalability
The different income statements, driven by the respective business model of either the bank or the
FinTech, reflect a completely different attitude towards (digital) scalability, as anticipated before.
The interaction between FinTechs and banks can be understood even comparing their income
statements: whereas banks transfer (or share) some of their clients with FinTechs, the latter provide
cost-saving solutions that decrease the OPEX of banks and improve their resilience. Figure 4 shows
this interaction and its impact on valuation.

FinTechs versus Traditional Banks
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Figure 4 - Economic Interaction between FinTechs and Banks
FinTech Bank
Revenues ↑ interest rate margin
contribution margin
- monetary OPEX - monetary OPEX↓
EBITDA
Discounted Cash Flows (DCF) / Dividend Discount Model
market multipliers = EV/EBITDA*forecast EBITDA = forecast EV
Income Statement
savings
clients
Figure 4 shows a win-win interaction: if banks share some of their clients with FinTechs, the latter
improve their inventive capacity, sharing it back with the banks. The clients represent the ultimate
“shadow” stakeholder in this triangular relationship and may participate in this value co-creation
paradigm with her precious feedbacks that fuel big data and their interpretation and use.
Banks may internalize this value pattern, buying and merging the FinTech. In this case, the valuation
approaches and the business models also merge.
FinTechs have a revenue model that is much more scalable than that of a typical bank. Whereas a bank
is limited in its growth potential by constraints such as the supervisory capital (a percentage of its loans,
weighted for lending risk), huge fixed costs for personnel, and difficult upside in a mature market,
FinTechs incorporate a digital potential in an intrinsically scalable business model.
Even if FinTechs have a higher marginality potential, they still need the volumes (client base, etc.) and
the market caption bound to traditional banks.
7. The Financial Market Ecosystem
The interaction between banks and FinTechs shapes an innovative financial intermediation ecosystem.
Digital platforms represent a bridging node among the players involved, in a context where customer-
centricity is increasing its strategic relevance. This ecosystem, illustrated in Figure 5, may be
interpreted, even mathematically, with network theory (Barabási, 2016).

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Figure 5 – The Networked Financial Ecosystem
Digital
Platforms
/ M-Apps
FinTechs
(Traditional)
Banks
Other
Digitali zing
Industries
Insurance
Companies
RegTech
Clients
Shadow
Banks
Blockchains
Artifi cial
Intell igence
Big Data ...
InsurTech
SupTech PropTech
Central
Banks
Listed
FinTechs
Establ ished
Fir ms
StartUps
Big Tech
Due to their network effects, digital firms enjoy increasing returns to scale – the bigger they get, the
cheaper it is to serve additional customers. FinTechs may partially follow this paradigm, even if there
is more to building enduring firms, whose businesses need a distinctive culture or a superior
technology, uneasy to be replicated by others. Not an easy task, especially in the crowded FinTech
arena.
8. Discussion (The Dark Side of Valuation)
The inductive reasoning of this study explains why the stock market price of FinTechs us so divergent
from that of traditional banks. A complementary deductive methodology, starting from the financial
market ecosystem, may provide top-down evidence. The flow is summarized in Figure 6.

FinTechs versus Traditional Banks
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Figure 6 – The Value-Price Valuation Process, From the Financial Ecosystem to the Stock Market
Financial Market Ecosystem
Financial (Dis)Intermediation-
Supply Chain
Business Model
Business Plan / Accounting Metrics
Valuation Metrics
Stock Market
d
e
d
u
c
t
i
v
e
t
o
p
d
o
w
n
bank FinTech Client
i
n
d
u
c
t
i
v
e
b
o
t
t
o
m
u
p
The business model comparison between FinTechs and banks is a primary methodology to confront
diverging market prices, explaining with fundamental analysis and the intrinsic valuation the difference
between the value and prices. Young or complex businesses are difficult to estimate, as shown in The
Dark Side of Valuation (Damodaran, 2018). Valuation across the business life cycle changes and start-
upping FinTechs are quite different from established ones.
The comparison of this study is somewhat asymmetric, being focused mainly on FinTechs, with little
reference to traditional banks. There is so room for more comprehensive confrontation, and integration
of the business models, driven by the sharing of similar clients.
Evidence collected so far, and market feedbacks show that listed FinTechs command a premium over
traditional banks. But this consideration does not consider important caveats. Most FinTechs are still
represented by fragile and unlisted startups. Besides, FinTechs are mostly unsupervised by Central
Bank authorities, especially if they are not involved in deposit collection (that implies a bank license)
and lending. Supervision is expensive and time-consuming but reduces the probability of default and
provides a parachute, being central banks a lender of last resort to ailing banks. FinTechs may be
tempted to pursue circumventive innovation strategies, to get a competitive advantage over hyper-
regulated banks.
Stock market prices of FinTechs are sustained by levered expectations of future gains. This bet is
intrinsically risky and may be biased by underestimated risk pricing.
A further aspect is represented by a well-known property of banks that is almost absent in FinTechs:
lending risk. Credit rationing is the limiting by lenders of the supply of additional credit to borrowers
who demand funds, even if the latter are willing to pay higher interest rates. It is an example of market
imperfection, or market failure, as the price mechanism fails to bring about equilibrium in the market.

FinTechs versus Traditional Banks
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17
Stiglitz and Weiss (1981) developed a model to illustrate how credit rationing can be
an equilibrium feature of the market, in the sense that the rationed borrower would be willing to obtain
the funds at an interest rate higher than the one charged by the lender, who will not be willing to lend
the extra funds, as the higher rate would imply lower expected profits. It is equilibrium rationing as
there exists excess demand for credit at the equilibrium rate of interest. The reason for that is adverse
selection, the situation where the lender is faced with borrowers whose projects imply different risk
levels (types), and the type of each borrower is unbeknownst to the lender. The main intuition behind
this result is that safe borrowers would not be willing to tolerate a high-interest rate, as, with a
low probability of default, they will end up paying back a large amount to the lender. Risky types will
accept a higher rate because they have a lower chance of a successful project (and typically a higher
return if successful), and thus a lower chance of repayment.
The absence of lending risk makes the business model of FinTechs scalable (and so, able to generate
high economic and financial margins). Traditional banks face much riskier scalability options: whereas
it would be easy for them to expand exponentially their borrowing (to billions of potential unbanked
customers), they would face marginally growing risk, and higher fixed costs, probably bringing to
negative margins. Bank scalability may so end up in a boomerang, especially before and during
recessions, when credit quality deteriorates.
Risk is a primary component of firm evaluations (being incorporated, for instance, in the cost of capital
that represents the denominator of Discounted Cash Flows) and is embedded in stock market prices. It
is so unsurprising that banks have consistently discounted market prices if compared to FinTechs or
other technological firms.
9. Conclusion
FinTechs are reshaping the banking industry, proposing innovative technological solutions that foster
customer-centricity, creating shared and sustainable value.
The main thesis of this study is that the evaluation of FinTechs follows appraisal approaches that are
(unsurprisingly) like those of technological startups. Even if the underlying industry is represented by
bank activities, FinTechs are innovators / facilitators of financial activities and are not personally
involved in the borrowing/lending intermediation business. Due to their nature of technological
providers of financial services, FinTechs can so be assimilated to innovative startups (or more mature
companies). The evaluation metrics incorporate risky growth expectations that reflect the potential
goodwill.
Evaluation methodologies are important to assess and refine not only to ease the M&A activity but also
to foster value recognition for all the stakeholders that are involved in the value co-creation paradigm.
The customer’s experience (and the big data continuously fuelled by feedbacks) is a central factor in
the digital economy as it adds value to the whole process. Fair remuneration of the clients remains,
however, a controversial issue (are clients going to be properly repaid for the feedbacks and big data
that they produce?).
Stock market prices nowadays reward FinTechs that incorporate steep multiples of earnings and book
value, if compared to unfashionable banks. But reversals of fortune are always possible, as the Roman
poet Horace remembers (“many shall be restored that now are fallen and many shall fall that are now
in honour”). And sobriety is always the best antidote against irrational exuberance (Shiller, 2016) and
the legacy of the dot.com bubble of the Spring 2000, driven by analysts’ distorted valuations
(Cervellati, 2012).

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18
Furthermore, FinTechs and banks operate in the same financial business (although with different
features) and share similar clients. And banks may internalize a FinTech, buying it. FinTechs and
traditional banks converge towards a common market, with co-opetition strategies that reduce the
conflicts of interest and other governance concerns. This strategic convergence is catalyzed by the very
fact that banks are digitalizing their business models, so reducing the differences with FinTechs.
M-banking and digital payments represent the most popular FinTech solution and are compliant with
contactless pandemic prescriptions. Changes induced by the Covid-19 crises are likely to accelerate
existing trends, possibly bringing to a “TechFin” open-banking scenario where unregulated BigTech
players (like Amazon, Apple, or Facebook) intermediate data and consumer relationships, using
standard interfaces.
Digital platforms represent the bridging node (interface) that connects the FinTechs, the banks, and the
clients within the financial intermediation ecosystem. They can be mathematically interpreted with
network theory and fostered with blockchain validation and artificial intelligence algorithms.
Innovation represents a key for the evaluation of digitized unconventional businesses (Moro Visconti,
2020a).
Further research avenues may concentrate on the converging business of FinTechs, banks, and
BigTech, driven by digitalization, disintermediation, customer centricity, and other centripetal forces.
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FinTechs versus Traditional Banks
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APPENDIX
IFINXNT - Indxx Global Fintech Thematic Index
INDEX CHARACTERISTICS
Base Date
06/30/2015
No. of Constituents
34
Dividend Yield
*
0.36%
52 Week High/Low
**
2618/1548
*
Trailing 12 months data for current year portfolio
**
Trailing 12 months
INDEX CONSTITUENTS (FIRST FIVE FINTECHS)
ISIN
Company Name
Weight(%)
NL0012969182
Adyen Nv
7.13%
US31620M1062
Fidelity National Information Services Inc
6.04%
US3377381088
Fiserv Inc
12.73%
US4612021034
Intuit Com
5.79%
US70450Y1038
Paypal H
oldings Inc.
5.67%
INDEX RISK & RETURN STATISTICS
Statistic QTD YTD 1 Year 3 Year Since Base Date
Beta
1
1.13 0.95 0.96 0.97 0.96
Correlation
1
0.95 0.94 0.91 0.86 0.84
Returns
2
13.25% -9.75% -3.51% 18.17% 16.50%
Standard
Deviation 49.33% 54.18% 33.60% 24.29% 20.83%
1
W.R.T.: Indxx 500 Index TR
2
As of last trading day.