Business Model Transformation through Artificial Intelligence in the Israeli InsurTech

Abstract

This paper aims to introduce the case for digital transformation of business models (DTBM) via artificial intelligence (AI) as it takes place in the Israeli InsurTech. For this purpose, we have mapped that landscape using archival data and conducted 10 semi-structured interviews with experts in the field. External (e.g. Change in the customers' preferences) and internal challenges (e.g. low IT capabilities) have highlighted the need for DTBM with AI technology playing an important role as a driver for such. In this paper, we introduce these challenges and the value-creation opportunities AI entails within the industry. We also present how Israeli InsurTech start-ups shape the industry, solve challenges, and create value. In this way, we help bridge many gaps, e.g. how AI is used for value creation, how Data-Driven-Business-Models rely on big-data analytics as key activity for value creation, and how DTBM innovation generally works in practice.

Full text

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Business Model Transformation through Artificial
Intelligence in the Israeli InsurTech
Tal Berman*
Hochschule Neu-Ulm, Wileystraße 1, 89231 Neu-Ulm, Deutschland.
E-mail: tal.berman@hnu.de
Daniel Schallmo
Hochschule Neu-Ulm, Wileystraße 1, 89231 Neu-Ulm, Deutschland.
E-mail daniel.schallmo@hnu.de
Christopher A. Williams
Johannes Kepler University, Altenbergerstraße 69, 4040 Linz, Austria.
E-mail: chrs.a.williams@gmail.com
* Corresponding author
Abstract: This paper aims to introduce the case for digital transformation of
business models (DTBM) via artificial intelligence (AI) as it takes place in the
Israeli InsurTech. For this purpose, we have mapped that landscape using
archival data and conducted 10 semi-structured interviews with experts in the
field. External (e.g. Change in the customers' preferences) and internal
challenges (e.g. low IT capabilities) have highlighted the need for DTBM with
AI technology playing an important role as a driver for such. In this paper, we
introduce these challenges and the value-creation opportunities AI entails
within the industry. We also present how Israeli InsurTech start-ups shape the
industry, solve challenges, and create value. In this way, we help bridge many
gaps, e.g. how AI is used for value creation, how Data-Driven-Business-
Models rely on big-data analytics as key activity for value creation, and how
DTBM innovation generally works in practice.
Keywords: Digital transformation; business models; value-creation; business
model innovation; digital transformation of business model; artificial
intelligence; start-ups; Insurance; InsurTech
1 Introduction
We live in an age when advances in digital technologies are extraordinary (Li, 2020a;
Llopis-Albert et al., 2021; Rouhani et al., 2017) and the focus of vast research (Hanelt et
al., 2021). Technology has enabled businesses to innovate not only their products,
services, and processes (Gault, 2018) but also their entire business models (BMs)

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(Chesbrough, 2010). Novel digital technologies, e.g. 5G (e.g. Teece, 2021), 3D printing
(e.g. Rayna and Striukova, 2016), blockchain (e.g. Morkunas et al., 2019), Internet of
Things (IoT) (e.g. Tesch et al., 2017), and others, have created business opportunities
(Teece and Linden, 2017), contributed to better business performance (Srinivasan and
Swink, 2018), influenced businesses' strategic decisions (Bharadwaj et al., 2013), and
enabled BM innovation (BMI) and digital transformation (DT) (Al-Debei and Avison,
2010; Akhtar et al., 2019; Nambisan et al., 2019). In sum, digital technologies have
become the essence of the value any business brings about nowadays (Soluk et al., 2021).
One technology in particular that has been getting lots of research attention (Huang and
Rust, 2018; Schneider and Kokshagina, 2021) as it is considered a catalyst for BMI
(Balog, 2020; Mele et al., 2018) is artificial intelligence (AI). Through ample
technological developments in its ability to "learn" (Duan et al., 2019, Robledo et al.,
2021), i.e. foundations such as machine and deep learning, combined with the huge
amounts of data accumulated in organizations (Kitsios and Kamariotou, 2021), this
technology has become a gamechanger (Lee et al., 2019) in its potential for creating new
business opportunities (Chen and Siau, 2020) and achieving sustainable competitive
advantage (Brock and von Wangenheim, 2019).
Furthermore, this technology has been largely impacting various industries (Endres et al.,
2020; Makridakis, 2017). One of the industries it is being implemented to help reform is
insurance (Eckert and Osterrieder, 2020). Consequently, much has been debated about
AI's applications and capabilities and the many ways to achieve that goal (Eling et al.,
2021). However, most of that research has focused on how insurers can implement the
technology to revamp their value chain—as coined by Porter (1985) (Bohnert et al.,
2019; Capiello, 2020; Eling and Lehmann, 2018; Eling et al., 2021). To date, most
technology-focused research has primarily lacked empirical evidence and has been
mostly conceptual (Nadkarni and Prügl, 2021); the same is true for the topic of value-
creation via AI technology (Duan et al., 2019; Reis et al., 2020). One exception in the
field of insurance is Stoeckli et al. (2018); alas, however, their interviews took place back
in 2015.
DT of incumbents' BMs is a relatively complicated process that requires multiple
reciprocal strategies between all related business units (Aspara et al., 2013) and change in
managerial ways of thought, routines, and preferences (Gilbert, 2005). As a result,
unfortunately, this attempt fails more often than not (Reeves et al., 2018). Nonetheless,
DT expands incumbents' considerations towards the entire ecosystem (Gong and Ribiere,
2020). Therefore, and as part of their innovation process of external seeking (Laursen and
Salter, 2006), they end up collaborating with start-ups (De Groote and Backmann, 2020).
Start-ups are disruptive and innovative in their BMs (Blank, 2013) and considered
"digital exemplars" (Morakanyane et al., 2020) because they are more agile and have a
higher level of innovation capabilities (Nadkarni and Prügl, 2021). Consequently, such
collaborations aim to achieve value-creation internally, i.e. the insurers' own
organizational processes (B2B), and externally, i.e. living up to the customers'
expectations (B2B2C) (Enholm et al., 2021), thus achieving BMI.
This paper examines how Israeli InsurTech, i.e. technological companies in the vertical
of insurance (Alt et al., 2018) start-ups, utilize AI technology and data analytics for
value-creation, thus answering the call for more research on the subject (Mikalef et al.,
2020a). We aim to shift the focus and discuss how such start-ups who use AI-based

solutions create new insurance products or/and help insurers in a partnership model as a
part of the ecosystem (Weill and Woerner, 2015). Our belief is that, with such an
overview, we will assist in bridging the gap discussed by Reim et al. (2020, p. 181),
"increase insights on business model innovation related to the implementation of AI," and
deliver important findings to practitioners as AI can be another source of innovation
(Huang and Rust, 2018).
2 Theoretical Background
2.1 Business Model and Business Model Innovation
BMs have been a hot research topic for many years now (Kesting et al., 2015) as they are
considered a very important subject in management studies (Alt and Zimmermann,
2001). BMs are considered holistic definitions of how firms do business (Zott, et al.,
2011) and create an economic value to stakeholders (Amit & Zott, 2012). As a BM is
designed to better address market needs, it is defined as the way in which a firm creates
and captures value (Demil et al., 2015; Teece, 2010)—i.e. as it creates value for its
customers in the form of products and/or services, it captures value for the firm in return
in the form of financial revenues (Schallmo, 2013). Another important component of the
BM is the value proposition (VP) (Osterwalder et al., 2014), which is defined as what the
firm offers and to whom (Morris et al., 2005).
Technology serves as a natural catalyst for firms to digitally transform their processes
and products (Pousttchi et al., 2019). Although considered a driver for BMI (Spieth et al.,
2014), technology is no longer sufficient on its own to achieve that (Baden-Fuller and
Haefliger, 2013; Chesbrough, 2007). Therefore, firms do not compete solely on their
ability to introduce new technologies, even if these eventually become valid new
products and/or services (Gassmann et al., 2013; Yoo et al., 2010, 2012); contrarily, they
do so mainly by innovating their BMs because such strategy is deemed to be more
sustainable (Brown, 2008; Johnson et al., 2008).
Consequently, BMI has been a catalyst for better performance (Zott and Amit, 2007) and
is defined as new modifications to elements of the BM, i.e. value-creation, value-capture,
value-delivery, and/or what links these together (Foss and Saebi, 2017). Predominantly,
this is driven in situations where internal factors, e.g. newly formed and/or achieved
dynamic capabilities (DC) (Teece, 2018a), and/or external factors, e.g. change in
customers' preferences (Markides, 2006), come together to form the right kind of
atmosphere for a new and unique VP (Schallmo et al., 2019).
2.2 Digital Transformation and Value-Creation through Artificial Intelligence
With the emergence of digital businesses and technologies, much has been debated about
the concept of DT (Alt, 2019). Albeit a mature concept, DT's definition has yet to be
established with limited, narrow, and extraneous related systematic reviews (Hanelt et al.,
2021). Nonetheless, a common understanding is that DT refers to the "disruptive
implications of digital technologies" (Nambisan et al., 2019, p.1). It is considered a key
driver for BMI (Tesch and Brillinger, 2019) and can be highly beneficial if successful

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(Anthony et al., 2019). The DT of BMs (DTBM) is the way businesses generate new
products, elements, and processes in the BM by using such technologies (Schallmo et al.,
2017; Schallmo and Williams, 2016, 2018). One technology that has been deemed
important for transforming BMs is AI (Burström et al., 2021).
Nowadays, AI is being mentioned all over (Kaplan and Haenlein, 2020). Scholars have
strongly suggested that this technology will one day take over many chores, tasks, and
jobs that, today, are strictly done by humans (Frey and Osborne, 2017; Huang and Rust,
2018; Pi and Fan, 2021; von Krogh, 2018), because it can complete these in a much
shorter time while delivering better results (Atack et al., 2019; Kaplan and Haenlein,
2020; Ng, 2016)—e.g. it can better handle information processing without the need for
human guidance and control (Robledo et al., 2021). The benefit of this upcoming radical
change is the enablement of humans to shift their focus towards more creative tasks and
challenges (Zanzotto, 2019).
Although not easy to clearly understand (Kaplan and Haenlein, 2020), it is still generally
agreed upon that AI is a system that acts intelligently (Alt, 2021a; Poole and Mackworth,
2010); i.e. it can learn, improve, and manipulate its environment (Kaplan and Haenlein,
2019; Paschen et al., 2019). Notwithstanding that the concept of AI has been in existence
for centuries (Trunk et al., 2020), scholars have found it hard to come up with its
common definition (Collins et al., 2021). Nonetheless, for the purpose of this paper, we
will use Mikalef and Gupta's definition: "AI is the ability of a system to identify,
interpret, make inferences, and learn from data to achieve predetermined organizational
and societal goals" (2021, p.103434). Elaborating on that definition, AI receives data as
its input in two forms: structured—usually quantitative, e.g. demographics—and
unstructured—usually qualitative, e.g. human language in a written pattern (O'Leary,
2014). Thereafter, these data are processed and analysed autonomously and in an
unsupervised way, i.e. without a human inspection or overseeing (Paschen et al., 2020).
There are many technologies considered to be AI, e.g. Robotic Process Automation
(RPA) and chatbots in their soon-to-be cognitive form (Alt, 2021a), i.e. an automatic
messaging system impersonating human correspondence (Lamberton et al., 2017);
computer vision, i.e. the processing of pictures and videos (Forsyth and Ponce, 2011);
and machine learning, i.e. where the system learns by repetition (Paschen et al., 2019).
Technological improvements largely influence a BM (Gambardella and McGahan, 2010),
especially its value-creation component (Amit and Zott, 2001). Consequently, AI has
made firms and businesses act quickly to digitally transform these (Vial, 2019).
Principally, business strategy dictates the reasons, goals, and forms of AI implementation
(Paschen et al., 2019; von Krogh, 2018). Yet, it is important to emphasize that business
strategy also drives DT (Kane et al., 2015); therefore, just like any other digital
technology, AI in itself does not add any value to an organization (Kane, 2014).
Contrarily, it is its ability to help locate new ways for value-creation (Burström et al.,
2021; Vial, 2019) and value-capture (Saarikko et al., 2020) that matters. Considering AI
could change many aspects of the way customers are served across the value chain
(Teece, 2018b), much has been debated about the value-creation opportunities it entails
(Duan et al., 2019). Therefore, although this has been deemed challenging (Wuest et al.,
2016), business leaders are formatting their BMs around it (Wellers et al., 2017).
Most research in recent years focused mainly on aspects of sales and marketing
(Campbell et al., 2020; Cao, 2021; Mikalef et al., 2021; Paschen et al., 2019; Paschen et

al., 2020) due to the potential of this technology to improve many capabilities across the
sales funnel (B2B and B2C). Furthermore, to date, this technology has not received
enough attention from information systems (IS) scholars (Maucuer et al., 2020). This
seems to be a profound oversight because implementing AI in organizations well,
although a relatively slow process (Burström et al., 2021), has been deemed important
across the board (Dwivedi et al., 2021). It can improve the response to competitors'
actions, manage firms' information better, and reduce risks and costs (Haefner et al.,
2021).
In short, AI can be described as a driver for BMI since new skills and especially new
innovative BMs are needed to harness its value-creation capability (Sjödin et al., 2021).
Principally, AI serves as a vessel for firms to shift their focus from product innovation to
BMI (Gebauer et al., 2020; Paiola & Gebauer, 2020), and although, to date, not many
positive research results on its implementation have been found (Müller et al., 2018), the
few available so far have been deemed affirmative (Björkdahl, 2020).
2.3 Value-Creation and Business Model Innovation through Big Data Analytics
Business organizations rely more than ever on their information technologies (IT) (Alt
and Zimmermann, 2017; Lu and Ramamurthy, 2011) and related IT capabilities, defined
as the "ability to mobilize and deploy IT-based resources in combination or copresent
with other resources and capabilities" (Bharadwaj, 2000, p.171). Various IT resources
store huge amounts of data in every organization (Alt, 2021a; Loebbecke and Picot,
2015), e.g. customer relationship management (CRM) and enterprise resource planning
(ERP) (Nadkarni and Prügl, 2021). Thus, we clearly see a move towards sophisticated
methods of using it wisely (Kitchens et al., 2018), i.e. its organization, management, and
manipulation to receive useful insights (Kaplan and Haenlein, 2020; Tee et al., 2007).
Big data (BD) is a fundamental key resource for any organization (Mikalef et al., 2020a)
and may be used to innovate products and services (Yang et al., 2017). It is defined as
“the information asset characterized by such a high volume, velocity and variety to
require specific technology and analytical methods for its transformation into value” (De
Mauro et al., 2016, p.127). However, just like AI, BD does not contribute to business
performance or/and create value by itself (Agarwal and Dhar, 2014; Power, 2014),
especially if it is not organized in a way that can be used to gain useful insights (Dhar,
2013; Grover et al., 2018). Contrarily, it must be analysed by using advanced
technologies to achieve such a goal (Müller et al., 2016; Power, 2014).
AI, as formerly mentioned, is a system that learns. Its main source for achieving that goal
is BD (Mikalef and Gupta, 2021), therefore it is considered a BD analytics (BDA) tool.
BDA is defined as a "means to analyze and interpret any kind of digital information"
(Loebbecke and Picot, 2015, p.2). Predominantly, it is a method that helps a business
gain insights from BD to make better managerial decisions (Akter et al., 2016; Ngai et
al., 2017). Furthermore, it has been suggested by various scholars as a tool that increases
financial success (e.g. McAfee et al., 2012), drives business growth (e.g. Sen et al.,
2016), and creates new opportunities (e.g. Davenport, 2014). It remains to be seen,
though, whether future predictions regarding the potential of BDA indeed materialize
(Ransbotham et al., 2016) as, so far, only limited research has been conducted on how
and what value can be generated from its applications (Mikalef et al., 2019a).

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Nowadays, organizations are still in the process of figuring out how to use BDA wisely
(Wamba et al., 2015), i.e. searching for ways of mobilizing value (Wixom and Ross,
2017). As it has the ability to bring together, integrate, and set up BD (Gupta and George,
2016), capitalizing on a high level of BDA capability (BDAC), defined as "the ability of
a firm to capture and analyze data towards the generation of insights by effectively
orchestrating and deploying its data, technology, and talent" (Mikalef et al., 2019b,
p.273), may influence a business firm's productivity and performance (Chen et al., 2012;
Mikalef et al., 2020b; Quaadgras et al., 2014) and, at the same time, may assist with
penetrating new markets and/or creating new innovative products (Woerner and Wixom,
2015). Consequently, incumbents have already started to engage in the process of
rethinking and reconfiguring their entire BMs around the concept of BDA (Wirtz et al.,
2010; Yin et al., 2020).
As formerly suggested, being a digital tool that enables BMI, AI relies mainly on data
mining and analytics, i.e. De Mauro et al.'s (2016) formerly mentioned term, the
transformation of data into value. Therefore, as AI and BD interrelate, with one unable to
exist without the other for the purpose of BMI, such DT entails a new breed of BMs (Li,
2020b) to build upon their joint value-creation capability (Hartmann et al., 2016). Data-
driven business models (DDBMs) are a relatively new concept (Wiener et al., 2020). By
building on Osterwalder's (2010) Business Model Canvas tool, Hartmann et al. (2014)
defined DDBM as a BM that builds on BD as a key resource and on BDA as a key
activity. As such, and as per the formerly described AI's ability to create a competitive
advantage, every organization rich with data, e.g. insurers (Nadarajah and Bakar, 2014),
will implement AI solutions of sort in the future, thus converting their BMs to data-
driven ones (Phillips-Wren and Hoskisson, 2015; Schüritz and Satzger, 2016). At the
same time, more and more start-ups have been producing innovative models of the
DDBM kind themselves (Loebbecke and Picot, 2015).
In sum, when companies and industries are going through a stage of DT, they heavily
rely on BD acquisition and analytics (Dremel et al., 2017), because these can be used to
either create new BMs or enhance older ones (Zarifis and Cheng, 2021). As a result, BD
and AI can no longer be considered only as resources, as suggested by Hartmann et al.,
but their management as a DC that assists with creating and capturing value (Cetindamar
et al., 2009). Such AI capability (AIC), a very new concept, is proposed as "the ability of
a firm to select, orchestrate, and leverage its AI-specific resources" (Mikalef and Gupta,
2021, p.103434).
2.4 Value-Creation in Insurance
We have already discussed the fact that technological changes, updates, upgrades, and
advances drive BMI. Consequently, it is all but certain the insurance industry will change
over the next decade in a revolutionary shift in its BM from "detect and repair" to
"predict and prevent" (McKinsey, 2021). AI is a state-of-the-art resource for prediction
purposes (Loebbecke and Picot, 2015; Wang and Srinivasan, 2017), and therefore it has
gained much attention among incumbents' management within the industry (PWC, 2021).
Eling et al. (2021) provide as an example the consequences of the upcoming radical
innovations in the mobility industry, which is perhaps one of the biggest insurance-
consuming industries of all (Thomas, 2021). According to all forecasts, autonomous

vehicles (AVs) will take over and become the main transportation tool for private
commuting in the future (Hancock et al., 2019). This phenomenon will entail a new breed
of insurance products and services and naturally newly innovated and digitally
transformed BMs. The rationale is that most likely changes in mobility BMs, e.g. MaaS
(Mobility-as-a-Service), will make private car owners abstain from purchasing vehicles
(Garfield, 2017). Consequently, insurance for cases of theft and accidents would become
redundant for the consumer (Bauman et al., 2020; Deloitte, 2016). At the same time, AV
fleet owners and managers would still need to insure themselves, but, to date, no such
insurance tool has been created (Bauman et al., 2020). Nevertheless, although the number
of car accidents would reduce to a minimum with the termination of the human error
factor (Heilig et al., 2017), one would still have to consider a risk of such incidents
happening in extreme-use cases, e.g. cybercrimes (Kennedy et al., 2019).
Although this is still quite far from happening, insurers have begun innovating their BMs
nonetheless (Eling and Lehmann, 2018). To achieve that goal, insurers are using novel
technologies that assist in providing new offerings to current customers, i.e. innovate
their value-creation, e.g. customized pricing based on user personalization while at the
same time also appealing to a new customer base, such as the formerly mentioned
autonomous fleet managers (Eling et al., 2021). Furthermore, they also find it easy to
provide new benefits for existing customers, i.e. innovate their VP, e.g. reduce fraud and
increase its detection to enable the approval of claims in a much shorter time while
building better trust among all stakeholders (City Ventures, 2021; Eling and Lehmann,
2018). Furthermore, as previously mentioned, insurers can also use technological
innovation to digitally transform their internal processes, e.g. process and analyze internal
data to examine their productivity and effectiveness, which are needed to promote a
higher operational level (Eling and Lehmann, 2018; Helfand, 2017).
Nowadays, the industry is going through a serious DT process assisted by technological
start-ups in the up and rising vertical of InsurTech (Capiello, 2020). As previously
mentioned, this seems to be the time to create value for various actors within this industry
and across the value chain. InsurTech start-ups have been doing so by coming up with
new innovative products based on digital technologies, such as the one we are focusing
on in this paper: AI.
3 Research Questions
Building on this theoretical knowledge, we aim to answer the following research
questions :
• What current challenges is the insurance industry trying to cope with?
• How can AI be used in insurance for value-creation?
• How do InsurTech start-ups use AI to create such value for consumers and insurers
and, at the same time, solve the industry's challenges?

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4 Methods
4.1 Research Design
We used qualitative methods for this study. Because, as formerly mentioned, there is not
enough empirical evidence in this field, we opted for a case study approach (Yin, 2018).
A single case study of the revelatory type of methodology was used because DTBM is a
new and underexplored concept (Schallmo et al., 2017); thus, it represented a concurrent
fit (Margherita and Braccini, 2020). Furthermore, such methodology allows researchers
to describe and analyse relevant cases from which grounded theories can ultimately be
generated (Eisenhardt and Graebner, 2007).
Firstly, we picked the Israeli InsurTech scene. We chose Israel as it is considered a world
innovation leader (Jin, 2020), and its InsurTech scene is growing rapidly with over 100
start-ups operating in this vertical (Munich Re, 2021). Some of them—Lemonade, Next
Insurance, At-Bay, Earnix, and Hippo Insurance—have already reached “unicorn” status
or IPOed (TechAviv, 2021). Furthermore, the country is among the world's largest homes
for start-ups who are using AI as part of their solutions and stands at a staggering number
of 1,649 (IVC, 2021).
For triangulation, we used archival digital data research as this methodology is becoming
popular among researchers in the field (Tob-Ogu et al., 2018; Vlaisavljevic et al., 2020).
Moreover, we conducted expert interviews to gain insights on existing approaches and
provide a thorough overview and empirical evidence (Bogner et al., 2009). This case
study methodology will allow us to base this paper on empirical data (Eisenhardt, 1989)
because the expert interview method is one of the more practical ways to analyse such
theoretical approaches (Brinkmann and Kvale, 2015).
4.2 Data Collection
To answer the first and second research questions, we used data gathered from our expert
interviews. As can be seen in Table 1, we conducted 10 interviews from July 1st to
August 18th with an even split between InsurTech start-up founders and related
practitioners. We applied semi-structured interviews methodology as we wanted to
receive a detailed response from our experts, and these provided a decent level of
adequate information (Harrell and Bradley, 2009). We aimed to learn about common
practices while earning new knowledge, if possible, by using open-ended questions
(Labuschagne, 2003).
Table 1 Experts Interviewed
Position
Organization
Date
E1
Innovation Leader
Israeli branch of a global insurance group
01/07/2021
E2
Founding Partner
FinTech consultancy firm
05/07/2021
E3
CEO
InsurTech consultancy firm that operates an
accelerator and invest in related start-ups
11/07/2021

E4
VP of Innovation
Israeli branch of a global insurance group
12/07/2021
E5
VP of Technology and
Innovation
A big Israeli Automobile conglomerate
19/08/2021
E6
Co-Founder and CEO
InsurTech start-up
06/07/2021
E7
Serial Entrepreneur, Co-
Founder and CTO
InsurTech start-up
08/07/2021
E8
Co-Founder and COO
InsurTech start-up
11/07/2021
E9
Co-Founder and CTO
InsurTech start-up
14/07/2021
E10
Co-Founder and CEO
InsurTech start-up
20/07/2021
To answer the third research question, we used archival research. We browsed the
companies’ websites and third parties' digital data, e.g. Start-up Nation Central and
Crunchbase, as these are accepted for data collection (Marra et al., 2015).
4.3 Data Analysis
For the interview analysis, we used a thematic analysis as it is a method that offers much
research flexibility and enables researchers with finding patterns "within and across data
in relation to participants’ lived experience, views and perspectives, and behavior and
practices" (Clarke and Braun, 2017, p.297). To report our findings, we used extensive
direct quotes and presented the results from actual data and directly related what the
participants said (Whittemore et al., 2001; Yardley, 2000).
To establish reliability, two experienced researchers coded the ten interviews separately.
Cohen's Kappa was 0.922, which is considered to indicate excellent agreement and is
well above the acceptable 0.75 cut-off (Banerjee et al., 1999).
To analyze our archival data, we built on Garbuio and Lin's (2019) methodology
combined with Remane et al.'s (2016); we elaborate further in the Discussion section.
5 Research Findings
In general, our findings suggest the following phenomena:
The majority of our interviewees (90%) firmly believe that AI is a game-changing
technology and is here to stay.
AI is absolutely a gamechanger in the insurance industry.... This industry is
logical and is equipped with lots of data. Good use of this data combined with
the mentioned logic will lead the industry to a better place—more precise, more
profitable. Better processes will enable new and better products and better
approaches towards the customers. In sum, the challenges…would be better
handled. (E3)
All participants (100%) are aware of collaboration in the Israeli InsurTech ecosystem
between incumbents and start-ups.
There're also collaborations with start-ups. For instance, there's this start-up for
which we've been a design partner locally here in Israel, and their first version

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was launched for our business entity in Singapore. Soon after, we've also
launched their product here locally…. Nowadays, they serve others as well.
(E4)
Lastly, the majority (70%) believe AI is uncommon in the industry in general.
Nonetheless, most (70%) believe firmly that InsurTech start-ups predominantly use AI as
their main technological solution.
It's very common (among start-ups). There're companies who are based entirely
on that…. (E5)
The results, which will be discussed in the next section in a much more detail, are as
follows:
In the case of RQ1, i.e. the insurance industry's challenges, our data suggest both external
and internal forces that influence the insurance industry as a whole and, as a result, the
insurers. In the case of RQ2, i.e. how AI can be used for value-creation in the insurance
industry, our data indicate three opportunities for the insurance industry: adapting to new
customers’ preferences and demands, improving internal processes, and improving
BDAC and AIC. These will be elaborated in the next section.
6 Discussion
6.1 The Insurance Industry's Challenges
As can be seen in Figure 1, the data indicate that there are both external and internal
issues that cause some challenges for the industry.
Figure 1 Insurers Challenges
6.1.1 Challenges that Originate at the External Environment
Firms must adhere to changes in the market and customers' preferences (Weking et al.,
2018). Therefore, BMs need to be dynamic (Teece, 2010) and should better reflect the
new market conditions (Demil and Lecocq, 2010). In this section, we will explore some
of these conditions and how they affect the insurance industry.

6.1.1.1 Convoluted Market Conditions
The insurance industry has become extremely competitive in recent years (Deloitte, 2018;
PWC, 2021), especially with online tools providing much flexibility and transparency for
the end-user who purchases products sometimes without even talking to or consulting
with real insurers' representatives (Kumar and Jain, 2021).
Lots of competition and heavy pressure on pricing. The ability to price
insurance risks has transformed into a commodity. There's not really any edge
for any of the competitors as everybody has the access to data and similar
models, rates, and profits. Today perhaps they're trying to win with features,
trying to outsmart the market, i.e. selling us products we hardly need and
convince us we really do. It's not an insurance game anymore but marketing.
(E9)
This has been worsened by new entrants, who are exploiting the new reality of
diminishing entry barriers (Deloitte, 2018) and are mainly split into two groups. The first
group is the challengers or the neo-insurers, start-up companies like the formerly
mentioned Lemonade and Hippo, who are using licenses from re-insurers, i.e. firms who
provide financial umbrellas to insurance companies or resellers, to sell insurance products
and compete with the incumbents. The second group is the BigTechs, who are also
entering the industry (Capgemini, 2020).
The technology enables new actors and players to penetrate the industry.
Mainly talking about BigTechs like Amazon, Facebook, Expedia, Tesla, etc.
This is a business development type of challenge. (E3)
I do see a threat however to the traditional players if and when BigTechs like
Google and Facebook enter the insurance industry. ApplePay entered the Israeli
market and conquered it in roughly 3–5 days. Anyone with an iPhone and a
credit card has paired these together, and that's it. Incredible! No campaign, no
push notifications, no market education… Just like that. Perhaps if Apple offers
insurance of some sort, e.g. traveling insurance via the mobile, it may change
the industry entirely overnight, especially when talking about a data giant like
Google who already knows so many things about us. (E6)
Recent events, especially the COVID-19 pandemic, obviously did not help as insurers
had to accelerate their digitalization efforts when face-to-face meetings ceased to be an
option (Deloitte, 2020). Additionally, many "milking-cow" products such as car and
travel insurance sales were down significantly, and, moreover, modifications were
needed to adapt to the new reality (Metz, 2021), e.g. does travel insurance cover COVID-
related situations and incidents (Compton, 2021)?
The main challenge, which is felt strongly by the entire industry especially in
these COVID times, is the need for digitalization. This is not truly a disruption
if we look at it from the capabilities perspective, but it's dramatic if we look at
it from the perspective of the mindset. (E3)
Finally, even though deregulation actions have been taken to increase competition levels
(Cummins et al., 2017), the need to live up to extreme regulatory demands has still hurt
the insurers' bottom line (Deloitte, 2014, 2021).
All of these have made insurers step up their efforts to reduce their loss rates.
Firstly, there's the commercial insurance, which is mostly car, life, and all other
related insurance domains. The expectation with this practice is more or less to
"lose as you profit," i.e. the acquisition costs are 100% of the premiums, and

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the profit comes from the fact they keep the money in their bank accounts for
12 months and use it to profit from financial transactions. Second, there's
property and casualty, the more business-oriented, where the revenue is above
the policy's cost. These two formats force insurers to use different strategies to
create a profit. In the commercial segment, you'll see that the InsurTech players
have an advantage, not in reducing the loss rate, but in much more efficient
customer acquisition. (E7)
6.1.1.2 Changing Customers' Demands and Preferences
Selling household insurance is not like selling a house. House and car are sexy;
they're tangible and have a form. These are actual products. Although insurance
is also a product, it's a serviceable kind of product, it's regulated, and
sometimes you're obliged to purchase it. People prefer not to deal with it. It's
very unappealing, and it sounds very financial. Therefore, it's very hard to sell
it as you can't turn it into something which is fun like Libra is trying to do in
their campaign. They and others are trying to show the customers, “oh, our
insurance is so outside of the box, it's different, it's fun,” meaning they're trying
to present it as something futuristic, innovative, and technological. (E2)
Nowadays, the customers and/or the users (not the sellers) seem to be the decision
makers regarding which value-creation is more important and which is less (Haaker et al.,
2021; Keen and Williams, 2013). Therefore, it is extremely important to discuss and
define these needs.
Our data indicate that insurance consumers have changed their preferences significantly
in three ways, which we will expand on next: 1) purchasing insurance products directly
from the insurers and not via an agent; 2) personalization, i.e. tailormade products; and 3)
engaging with the insurers in a digital way, e.g. via their smartphones, especially instant
messaging tools like WhatsApp.
With the entrance of fully digital financial firms, e.g. neo-banks, online stockbrokers,
etc., the concept of "cutting out the middlemen" has gained much traction in much of the
financial services industry. This phenomenon did not bypass the insurance industry, and
InsurTech companies like Lemonade have become challengers by doing the same with an
ever-increasing amount of success. This has driven insurers to change their way of
engaging with the consumers, moving from working with agents to different direct
approaches (Ralph, 2019).
Insurers nowadays deal with moving to “direct,” B2C. They try to create better
capabilities for recognizing risks with remote handling and analysis. (E5)
Furthermore, consumers today are expecting insurers to provide an entire tailormade
customer journey (Deloitte, 2018). A report by Israeli InsurTech giants, Earnix (2019),
has indicated that, by the year 2022, 79% of customers will demand personalized
marketing communications, and 70% will demand personalized insurance products and
services. Therefore, this concept presents a good opportunity to retain them as well (Alt
and Zimmermann, 2017).
The customers expect different insurance products. For instance, they want
procedures that are not insurance-based to be included, customized to their
needs, fit them better, on-demand, etc., i.e. there's a vector that pressures
insurers and drives them to another direction. This vector happens because of
other technological improvements within the industry. The customers are

exposed to technologies and products in other industries and expect
personalized products in order to purchase insurance. (E3)
Finally, as formerly mentioned, the COVID pandemic was only a catalyst of this; for a
long time, the eCommerce revolution did not bypass insurance (McKinsey Global
Institute, 2019). Tech-savvy people, who comprise and define the majority of the
Generation X and Y populations, i.e. digital natives (BusinessWire, 2021), are naturally
attracted to businesses with digital overtures (KPMG, 2021). In a survey from 2014, 71%
of consumers admitted to researching online before buying a policy with 26% of these
having actually purchased it online (PWC, 2020a). Furthermore, upon purchasing these,
digital natives want to manage their policies online entirely (Deloitte, 2018).
Unfortunately, financial institutions have not invested enough resources in DT in recent
years (Alt et al., 2018).
There're organizations who are more progress-oriented; there're organizations
that are born digital, e.g. Lemonade, who have a full and complete digital layer.
As soon as all adopt such technological solutions, they'll be able to solve these
challenges. There're lots of legacy industries that are lagging behind, insurance
one of whom, alas, they'll keep facing these same challenges over and over
again. (E8)
6.1.2 Challenges that Originate at the Internal Environment - Low Level of AI
Capabilities
Upon looking at the Israeli insurance industry, especially at incumbents, almost all are
suffering from inadequate AIC. This is by no means a local issue but a global one, it
seems (PWC, 2020b), with insurers lagging behind, spending significantly lower
percentages of their income on developing their IT than banks (Gopalan et al., 2012).
This is a result of two other inadequate DCs, as can be seen in Figure 1: ITC and BDAC.
Incumbents in the industry are suffering mainly from insufficient ITC, and that makes it
very hard for them to tackle the formerly mentioned external challenges. Some of their
legacy systems are outdated; some have yet to implement advanced technologies, e.g.
cloud computing; most use multiple systems and channels to complete their daily tasks,
and these cannot be integrated much of the time; and a lot of the processes are still being
done manually with overreliance on paperwork (McKinsey & Company, 2019).
We still deal with too much paperwork instead of systems that can make the
customer's life a lot easier and also the employees’, whether they work for
insurers or insurance agencies. Another challenge is the technological
interfaces, which are impossible to link together. Insurers have their own
systems, and insurance agents have their own, and then there're the customers
as well, who may be laggards. We need to link this chain together, and that's
very complicated. Our methods are very out of date. (E1)
The fact that most start-ups are using cloud computing and insurers are not
creates a big conflict. For instance, when we create a digital policy, insurers ask
for a PDF file, and they input it manually into their systems. They don't have an
interface that can accept the digital policy. Some insurers can do it nowadays;
some have started to work on that a year ago, but it's so stupid that we're 100%
digital, and, at the end, an insurer employee retypes it…. (E6)
There're also the interfaces. For instance, there're amazing new start-ups with
solutions who weren't developed to fit the current systems of the insurers. Not
that it's complicated but just different. When you develop something, you'd like

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to stay agile, and then you face these systems which are built differently. By the
way, it's solvable…. We are aware of it; it's a matter of priorities and resources.
(E4)
Although results are just starting to confirm this (Müller et al., 2018), little empirical
proof confirms the success of companies who invest in their BDA resources and BDAC
(Günther et al., 2017). Consequently, incumbents in the industry are suffering from a low
level of BDAC (LaValle et al., 2011) as they lack fundamental resources, e.g. qualified
people and satisfactory tools (Grover et al., 2018), and that goes hand in hand with recent
explained inadequate ITC. Data in many organizations can be noisy, untrusted,
unlabelled, and imbalanced (Grover et al., 2018). Similarly, insurance organizations are
rich with data; however, these data are insufficiently organized to use analytical tools,
and, moreover, they are not centralized around the customer (Rao, 2020). Finally, one
must also consider the lack of a competent workforce within the industry among others.
Think for a second, please…. How much data do those companies have? It
sums up years of operating in the market in many segments: automobile, travel,
etc. Now, what is the basic need for AI-based solutions? BD. What's the
problem with this data? It's unorganized. It's not ready for the AI to accept it as
it needs to be. In order to employ this data well, you first need to process it.
Making this data ready is a process that can take a very long time. You may
approach an insurer and ask for all data points which are related to one specific
segment and then receive tons of these, meaning you'll get access to a data
warehouse. Trust me, it's not an Excel sheet…. It's huge. From that point, you'll
surely get lost. You won't be able to feed this data to your machine learning
algorithm. (E2)
Think about it…. It's a very outmoded industry with unqualified manpower,
and that's an understatement…. Instead of true machine learning scientists that
cost nis 100,000 per month each, they employ an unprofessional workforce,
from top to bottom…. They have huge amount of data, and they are failing to
manage it. They manage it very poorly, and I'm doing my best to say it in the
most delicate way. Their ability to manage data and use data-analysing
technology is poor. I don't think I say something different than what you've
already heard; I'm just being more brutal, I believe…. The data of the insurers
is hogwash, I can't find a nicer way to say it. Look…, they have 100 Excel
sheet tables in one place while these other guys are actually using SQL, and
there's one guy who has it all in hardcopy and needs to scan it. And the
integration, analysis, drainage of this data… It's a nightmare. (E10)
Both below-par technological capabilities are direct contributors for a low-level AIC, as
formerly mentioned. For example, the challenge of pairing AI initiatives with internal
processes was the main reason for abandoning these (Davenport and Ronanki, 2018). As
such, implementing AI technology-based solutions for incumbents has become a huge
challenge (Lamberton et al., 2017).
It's very feasible but will take a long time. It's very slow because a radical
change is needed in the thinking and understanding culture of the insurers. In
general, people don't embrace change that quickly…. Specifically, things that
can go wrong, i.e. the delta between the potential and the harm it may inflict,
still doesn't favour the AI. So, one of the barriers to AI implementation is the
fact that a radical cultural change is very much needed. (E3)
Without insulting anybody, the average actuary is a 60-year-old white male
wearing a yellow tie. And this guy feels it doesn't concern him, and he has no
desire to get into it. He usually comes from a background in commercial
insurance and doesn't seem to mind the loss rate. Why should he look for

clusters of organizational behaviour based on the firms' assets and control? To
build such systems, you need a person with three ways of thoughts: actuary,
you must have this statistical knowledge; a CISO in the governance level, i.e.
how do you build it effectively?; and security, i.e. practice, someone who
comes from a security type of background and knows his or her stuff. (E7)
6.2 AI's Value-Creation Opportunities
As can be seen in Figure 2, our data indicate that AI entails three value-creation
opportunities within the insurance industry. These will be elaborated on next in this
section.
Figure 2 AI's Value-Creation for Insurers
6.2.1 Adapting to New Customers’ Preferences and Demands
Businesses design their BMs as a conjecture on their customers’ needs (Teece, 2007,
2010); thus, when these change, it results in the entire reconfiguration of the BM (Warner
and Wäger, 2019). Therefore, customers' needs are the starting point for any
technologically based value-creation (Sjödin et al., 2020). One of the main goals for
using digital tools is to better serve the customer base (Furr and Shipilov, 2019). Thus,
these technologies assist with the insurers' top priority, which is to improve customer
experience and gain loyalty and satisfaction (Siggelkow and Terwiesch, 2019). In sum,
DT in its core is about not only improving self-productivity but, more importantly,
upgrading the customers' experience (Haaker et al., 2021; Weill and Woerner, 2018).

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Businesses are constantly seeking better ways to respond to customers' needs (Endres et
al., 2020). At the same time, DT and BMI have assisted in revamping customers'
expectations (Verhoef et al., 2021). Ever since the dawn of the digital revolution,
(considered the fourth industrial revolution [Kowalsky, 2015]), led by companies like
Uber and Netflix, consumers' expectations in terms of quality are almost endless, and
they expect no less from any business they employ (Enkel & Gassmann, 2010).
Moreover, they seek personalized products and wish to pay different rates based on their
true consumption (Li, 2020b); therefore, at times, they prefer to consume it as a service
instead (Tukker, 2015). This has created common ground for BMI (Haaker et al., 2021).
Consequently, AI may assist with addressing many of the formerly mentioned challenges.
One of the targets of BDA value-creation is providing a better customer experience
(Grover et al., 2018). In the area of digitalization, there are tools nowadays such as the
formerly mentioned RPA, which allow customers to engage with insurers via machines,
i.e. in a seamless, automated process. These will eventually turn to multiple VPs, e.g.
proactive and speeding of service arrangements and personalized services (Lehrer et al.,
2018).
The most interesting technologies in my opinion are those which drive
automation. There're these processes called RPA, which aim to computerize
every human action, meaning a machine can do it on the backend. In insurance,
there're huge amounts of data for underwriting. When you determine the risk
premium to issue a policy, you do so by knowing the purchaser completely and
using as many information sources to achieve that. You also try to be aware of
all the possible cases this person may be involved with. It really is all about lots
of data on a person so that you can underwrite his/her policy. These RPA
processes are very common in insurance. And because this world is full of
paperwork, "forms automation" is also very common; all these papers, forms,
claims, every written thing is also being automated as we speak. (E2)
Using predictive analytics via AI can assist with the creation of personalization and
tailor-made products (Alt, 2021b), and, indeed, such offerings have been in existence in
the insurance industry for a while now (Lehrer et al., 2018). There are multiple examples
today of such offerings by insurers, e.g. linking fitness data with life insurance policies
via smart wearables (Littlejohns, 2019) or car insurance via safety features (Lee, 2020).
In cars and properties, we encourage… For instance, let's look at Mobileye;
we'll give you a discount if you have their product in your car. By the way, it
actually promotes the gadget. Furthermore, if you maintain a healthy lifestyle,
you'll also get a discount on your life insurance. And these are things which are
developing all the time…. (E4)
One of the targets of BDA value-creation is product and service innovation (Grover et al.,
2018). AI accelerates the personalization of products and services with its formerly
mentioned prediction ability (Alt, 2021b). Insurers will know their customers better and,
with its assistance in improving processes such as underwriting, will speed up the
onboarding and purchasing of the products. Moreover, customers will only purchase
products that are suited to their needs.
For example, I live in Israel, I'm married and have a 3-month-old daughter. As
soon as I insert this data into the system, it will automatically offer me suitable
life insurance. Why? Because I have a baby daughter. And this data is saved, so
if one day I'll have another child, the system will be able to update the premium

automatically and accordingly, and I'll receive a push notification that
everything is settled. Once they reach adulthood, the system will advise me to
reduce this premium. So, you may constantly use AI to increase sales and
personalize the products. (E1)
It enables us more and more to produce new products internally in insurance—
products that, in the past, we lacked the data to produce. It may have existed in
the past, but it wasn't accessible…. For instance, the health status of a potential
customer and its identification. AI enables scouting that can assist insurers to
understand the real health status or the property status, i.e. an insurance status,
which is based on the vector of the segment. Then, predicting the risk level in
order to decide whether to go for it or not…even if you don't know the history
of the asset. (E5)
There're lots of areas where AI can assist, especially in my expertise, cyber
insurance. It starts from picking organizations better and pricing their insurance
more aggressively. If I can reduce my loss rate significantly, I have no problem
with giving 10–15% discounts or alternatively to fine 10–15% and increase the
rate. If an organization comes and their security level is not good enough, the
InsurTech start-up will price them 15% higher than AIG and will have no
problem in a case where the customer chooses them instead. The probability of
breach is too high, and AIG who are unaware of it will probably lose money on
such an account in the future. (E7)
6.2.2 Improving Internal Processes
DDBMs replace less-competent BMs to improve the overall efficiency of the company
(Loebbecke and Picot, 2015). Consequently, another target of the BDA's value-creation is
indeed the improvement of business processes, which coerces better performance (Grover
et al., 2018) as companies who utilize AI for such purposes are able to improve many of
their internal operations, e.g. automation, data analysis, and product innovation (Goundar
et al., 2021; Holmlund et al., 2020).
With their inability to predict catastrophes and subsequent miscalculation of various
risks, insurers have suffered huge losses over the years (e.g. Jones, 2015). The recent
pandemic is a great example. But even more than that, pricing the risk incorrectly may
end in high customer dissatisfaction (e.g. customerreports.com, 2017; Frankel, 2020).
Furthermore, there are brand new insurance domains, e.g. cyber insurance, that are not
easy to cover or calculate risks for efficiently at this time (Johansmeyer, 2021).
It is suggested that AI has the ability to improve internal processes (Holmlund et al.,
2020), not only automating these with tools such as the previously discussed RPA but
also making them more accurate overall. The technology helps in various ways: moving
from manual to digital, calculating and managing risks better, and speeding core
processes, e.g. onboarding, underwriting, claims management, etc. (beyondminds, 2021).
All these and more are improving the insurers' bottom line, which, as previously
mentioned, is still a key pain point for them.
AI is able to solve intra-organizational challenges. It recognizes the specific
workflows of a specific employee so it can assist with the process and provide
an added value. It can also solve underwriting processes as we've previously
discussed.... (E1)
I think that, if you work with this tool the right way, it's THE tool to support
decision making. If you use it correctly, you really don't need anything else….

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It enables dynamics, precision, and advanced capabilities to deal with multiple
variables. In sum, if they'll use it correctly, it's THE tool insurers need to use.
My venture is a good example of that. Insurers were unable to offer what we
do…. The underwriting is done in milliseconds—300 milliseconds, to be exact.
The number of features and changes in modelling the data, which is very
dynamic. We're 13 people combined, and we do all that…. (E9)
They will make miracles one day in the field of prediction surely. The
technology will beat the actuaries and underwriters. It is happening already….
You can see Lemonade's results, not in their profitability but in their ability to
price quickly and accurately, with an emphasis on speed, I would say. You
can't compare…. A machine is quicker than any human. You press a button and
get a price. That's it…. Goodbye. (E10)
These capabilities are great, and I assume one day insurers will be able to know
what the chances are of you getting into a car crash situation and already when
you talk on the phone with their rep, they'll politely reject you, accept you, or,
wiser, price accordingly. Mobileye, for instance, knows exactly how you drive,
whether you're too close to the car in front of you or are you deviating, etc.
(E6)
6.2.3 Improving BDAC & AIC
Organizations with high-level technological capabilities can better satisfy customers'
needs and achieve additional value-creation (Haaker et al., 2021). Such organizations rely
on BDAC to predict consumers' predilection to purchase products and services (Grover et
al., 2018); hence, it is very important to improve this capability as much as possible.
One of the main issues for businesses that have yet to harness value-creation via AI
technology is implementation challenges (Brynjolfsson et al., 2019; Reim et al., 2020).
Implementing digital technologies to improve internal processes compels an extensive
look at the company and its resources (vom Brocke et al., 2014). Therefore,
implementing AI would help insurers not only serve their customers better but also
improve their technological capabilities (Reim et al., 2020). The automation of the
processes, making operational procedures more efficient, and reduction of the loss rate
and technological costs may achieve just that.
Insurance is very logic and binary; thus, it makes it very easy to operate AI
solutions in it. When everything is either yes or no, e.g. Is there coverage? Yes
or no. Is there a policy? Did he pay? It's all zeros and ones. So, for AI
developers, it's a party. Everything is simple, and the expectations for a result
of 0 or 1. In such a logical world, it's easy for AI to be very influential. So,
obviously, one can expect a breakthrough of this technology all over the
industry…. The problem mainly is that the available data is no good; it's not
categorized enough. They may do something about it, but the insurers have to
change their mindset in order for that to happen. (E3)
We try our best, and we're open to listening about new technologies,
understand the industry we're operating at, which changes. We are at the heart
of a change like many people like to say. When you're at your own spot, you
don't always see everything, and, therefore, you need sometimes to zoom out to
see what really happens…. (E4)
Two insurers have invested in my venture. We're relevant to them, they find
what we're doing very interesting, they'd like to understand better, and they
want to help. I sat with the chief actuary of AXA to build a risk model. They

really wanted to help although we're miles apart in almost everything. Still, it
seems very interesting to them…. (E9)
6.3 InsurTech's AI-Based Value-Creation
DT of incumbents has become a priority for their top management (Berman, 2012), and,
while digital technologies are proving to be positively influencing the performance of
implementing companies, they also cast a huge shadow of uncertainty (Heavin and
Power, 2018; Hill and Rothaermel, 2003; Matt et al., 2015). Unfortunately, most
incumbents choose to move into a DT process without first considering all the relevant
variables and aspects of it (Hess et al., 2016). They are not, to use a term coined by
Saarikko et al. (2020), “digitally conscious”—i.e. they misread the opportunities and their
limitations. Therefore, a good idea may be to join forces and collaborate with start-ups
known as digital exemplars, as mentioned previously.
Insurers are collaborating with start-ups in the industry. I can see that insurers
are very interested in these companies. They want to deal with them, learn, and
invest. (E9)
As can be seen in Figure 3, Israeli InsurTech start-ups are split into three main groups
based on their DDBMs, i.e. value-creation and capture models: 1) the challengers or neo-
insurers, e.g. Lemonade and Hippo, who are selling innovative insurance products
directly to consumers in a B2C model; 2) those who use a B2B model and are split into
two: a) those focusing their VP on the insurers and who mainly help them perform better
financially, e.g. calculate the risk more quickly and accurately, or avoid frauds, and b)
those who, just like Group 1, are selling innovative insurance products but to
organizations, e.g. cyber insurance like At-Bay or small business insurance like Next
Insurance; and 3) those who, while creating value for the insurers, focus their VP on the
consumers, i.e. the insurers' customers (B2B2C), e.g. assist insurers with better customer
engagement, led by companies like Lightico and Voca.ai.
It's a matrix, a very big one…. One side of the matrix is the stages of the start-
ups; some are grown, some are in the middle stages, some are in early stages,
and some are just starting. On the other side, you can find the domains, is it in
life, health, etc., and there's actually a third dimension, which is the process,
e.g. an early-stage company that is covering the underwriting in health
insurance or claims in cyber insurance. It's like spaghetti if you think about
that…. (E3)

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Figure 3 Israel's InsurTech Landscape
As seen in Figure 3, we have categorized the value creation of available Israeli InsurTech
start-ups. We decided to split the value-creation into three: 1) value for the insurance
consumers, i.e. end-users, who can be businesses or consumers, 2) value for the insurers,
e.g. assisting with increasing the insurers' profitability, and 3) a hybrid value-creation, i.e.
helping insurers better serve their customers.
There're the new insurance models or products, and there're improvements to
existing products. These are the two main categories: either go to a whole new
domain or something current, which lacks an insurance model, or calculate the
risk in a much more efficient way for existing products. If I drill down on the
side of the currently existing products, you can look at Lemonade and their
similars that sell "regular" insurance, e.g. life or property, or something more
esoteric…. You know what? Let's split these into B2C and B2B. Improve the
service to the consumers or the current models, and sell them to businesses.
Businesses need insurance coverage as well. In the world of new products, the
majority are B2B, because the regulation is weaker over there. Everything is
contract-based, and for that, you can take an example from my venture. I don't
have to be an insurer to provide an insurance model. Therefore, it's much easier
to operate in that domain. Perhaps one day ventures like ours will consider
becoming an insurance company. (E9)
7 Conclusion
The theme of emerging technologies, e.g. AI, and their effect on BMs is a relatively new
subject in IS literature (Maucuer et al., 2020). Such technological developments are
considered enablers for DT of BMI (DTBMI) (Schallmo et al., 2017) as they seem to
challenge the BMs in many traditional industries (Loebbecke and Picot, 2015). Therefore,
this concept has been gaining attention from researchers in various management fields
(Metzler and Muntermann, 2021). Unfortunately, the concept of DTBMs is still unclear
and lacks a consistent approach for a common process, assistive instruments, and valid
examples and enablers (Schallmo et al., 2017). Primarily, this topic remains ill-

understood (Spieth et al., 2014); thus, this paper has contributed strongly to clarifying the
concept.
Figure 4 Digital Transformation of Business Models through AI in Israel's InsurTech
Scene
AI is becoming a popular buzzword among entrepreneurs, practitioners, investors, and
innovation leaders in organizations (e.g. Schmidt, 2020). Yet, we do not know enough
about the ways to create value using it (Mikalef et al., 2020a)(e.g. the technology can be
used for service automation and product personalization, as we have suggested).
Furthermore, it seems it has become the most popular digital technology among Israeli
start-ups (IVC, 2021). As such, it is crucial to understand its effect on DTBMI across
industries (Metzler and Muntermann, 2021), e.g. insurance, which our paper deals with.
As BDA and AI are relatively new concepts, there is not much empirical evidence for
their value-creation capabilities (Duan et al., 2019; Grover et al., 2018). As in Figure 4,
we have shown various types and examples for how such DDBM-based start-ups use AI
to create and capture value and, at the same time, address markets’ needs.
Finally, we have contributed further to the literature of BMI as we showed new ways of
value creation (Foss and Sabei, 2017) and used such value-creation opportunities to
present ways for collaboration between start-ups and their stakeholders, e.g. their
customers (insurers, firms, or end-users) or their customers' customers (Berman and
Johnson-Cramer, 2019).
7.1 Limitations
This study is not without its limitations. Our interviews and case study were purely local
(Israel). Although Israeli start-ups are considered "Born-Global," i.e. they serve global

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November to 2 December 2021. Event Proceedings: LUT Scientific and Expertise Publications:
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market needs on one hand and global insurance firms and/or customers on the other, we
cannot be sure that the global perspective this paper wishes to present is thorough
enough.
7.2 Future Follow-up Research
We have chosen to deal in our research with InsurTech, and, while other researchers have
done some work on other industries, e.g. mobility (Anton et al., 2021), still more
industries need to be analysed as well. Furthermore, we opted for Israel as a context for
the reasons listed in the Methods section. A more global study is necessary to determine
whether our results are also valid in other strong InsurTech markets like the US and the
UK.
We have discussed the DTBM innovation in the field of insurance. We primarily
suggested only one component—value-creation—and one technology—AI. Future
research should focus on other components, i.e. value-capture and/or delivery, and other
technologies, e.g. blockchain.
7.3 Business Implications
This paper discussed, among other things, AI and the opportunity for value-creation it
entails in the field of insurance. We have linked these two together, showing how this
technology is solving major industry challenges, by providing evidence from the Israeli
InsurTech scene. Insurance actors may use this research to focus on the technology and
its advantages while entrepreneurs who wish to enter this realm are able to get some ideas
for value-creation in the field.

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