Evaluation of Potential Use Cases for Blockchain in the Insurance Industry

A Qualitative Analysis

Master's Thesis, 2020

112 Pages, Grade: 1,3




List of tables


1. Blockchain: A breakthrough for the insurance industry?

2. Blockchain / Distributed Ledger Technology in general
2.1 Definitions and hands-on descriptions
2.2 Challengesandopportunities

3. Current status insurance industry
3.1 Value chain ofan insurance company
3.2 Improving value chain by introducing Blockchain
3.3 Implementing Blockchain in the insurance sector

4. Use cases and areas impacted
4.1 GeneralusecasesforBC
4.2 Use cases in the financial services sector
4.3 Areas impacted in the insurance industry

5. Empirical Research
5.1 Delphi-Method
5.2 Expertinterviews
5.2.1 Industryexperts
5.2.2 Interview planning and presentation of questionnaires
5.2.3 Implementation and evaluation
5.3 Summary and results

6. Evaluation of applicable use cases
6.1 Deep-diveintousecases
6.2 Roadmap for a successful integration of BC / DLT into the VC

7. Summaryandoutlook
7.1 Summary
7.2 Criticalevaluation
7.3 Outlook


Appendix 1: Milestones on thejourney to become a digital insurer
Appendix 2: Overview of InsurTechs on the German market in 2016
Appendix 3: Overview of InsurTechs on the German market in 2017
Appendix 4: Overview of InsurTechs on the German market in 2020
Appendix 5: Heat map to prioritize digital opportunities in the VC of an insurance business
Appendix 6: Value chain impact of InsurTechs
Appendix 7: Digital transformation scorecard
Appendix 8: Overview experts and evaluation data
Appendix 9: Questionnaire round 1
Appendix 10: Questionnaire round 2
Appendix 11: Detailed evaluation of all questionnaires of round 1
Appendix 12: Additional challenges and opportunities by Robert Crazier
Appendix 13: Detailed evaluation ofall questionnaires of round 2

Executive Summary

Blockchain is considered to be a breakthrough technology with many strengths and opportunities, but also weaknesses and threats for all industries around the globe. It has the potential to revolutionize many sectors, but it is not a universal remedy and does not replace every single traditional technology. Most businesses do feel insecure whether to invest time and money for Research and Development due to the lack of industry standards and regulations with respect to the technology.

Before being able to guarantee suitability and successful implementation in any business, it is essential to study the differences between public, private, hybrid, and consortium Blockchain types.

In areas of application not strongly relying on Blockchain’s distributed systems, alternative solutions such as Big Data Analytics, Artificial Intelligence, and Machine Learning might be more valuable. Nonetheless, Blockchain promises the unique potential for insurance companies to efficiently serve global markets with areas of application mainly concentrating on parts of the VC where bureaucracy can be reduced by automation. For instance, the use of Smart Contracts where lots of data are analyzed, saved, distributed, and processed. By implementing BC, fields, where the focus is on customer experience such as Payments, Data Security, Transparency, and Developing New Business and Product Ideas, can be improved enormously. Use cases and new products supported by Blockchain might include P2P Micro-Insurances, Ad-hoc and Parametric Insurance Models, Au- tomized Underwriting, Reduction of Fraud and Abuse, and Health Insurance Processes. Real-Time Analytics in the Form of SCs, Instant Pay-Outs, Automized Claims Management, and Improved Risk Modelling of Insurance Businesses might be achievable by connecting Blockchain and Big Data.

Contrary to the expectations of the author, most industry experts are aware of the benefits and opportunities the technology can bring to their companies. Still, Blockchain is not anchored in their medium- to long-term visions and strategies. Most of the interviewees would rather act as active observers and early followers than taking the lead proactively. Reasons for this behavior, are the lack of market standards and regulations, the absence of solid Blockchain networks, and the shortage of use cases, but also, the lack of knowhow and data security concerns.

Due to Blockchain functioning as a distributed system, its value for individual insurance businesses depends on collaborating with suppliers and competitors. For the insurance sector as a whole, this signifies starting to work with consortia like B3i and R3, technology experts and start-ups / scale-ups, regulators, and other market participants to identify the challenges around Blockchain’s open and decentralized nature. The key to shaping the future of the Blockchain insurance ecosystem is getting involved in partnerships and industry activities early on.

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1. Blockchain: A breakthrough forthe insurance industry?

Does Blockchain BC have the potential to transform the ways the world analyzes, saves, and processes big data with improved security, transparency, and data quality?

According to McKinsey (2017, p. 56), with so much real-time data being generated in a connected world, digital technologies are pushing insurers toward new types of businesses helping consumers mitigate risks rather than simply protect against those.

The trend is moving away from improving customer experience by offering help in the form of call centers and support via email. Real-time payouts for claims, pay-per-use policies, and ad-hoc insurances, all enabled by implementing Smart Contracts SCs, can revolutionize the consumer experience in the form of driving service excellence (cf. Mittal and Bhattacharjee, 2018, p. 28). Hence, the questions insurance companies are asking themselves right now are: Are there solid use cases exploiting BC technology and SCs in the insurance sector?, Does BC go along with our information technology IT infrastructure?, Does BC make conventional technologies redundant?, In case our company desires to adopt BC into its processes, what is the most suitable BC type for our needs?, Is the implementation of Distributed Ledger Technology DLT really economic on the medium- to long-term perspective or merely connected to costs?, and, more generally, Is BC technology mature enough for insurance yet? (cf. Gatteschi et al., 2018, p. 2).

The Ultimate Guide to BC in Insurance published by accenture in 2018, expected the global market for BC in insurance to grow from USD 64.5 million in 2018 to USD 1.39 billion by 2023, a compound annual growth rate of 84.9 percent. More than 80 percent of insurance executives reported their enterprises have adopted DLT across one or more business unit or are piloting or intend to pilot the technology. Between USD 99 million and USD 277 million might be saved annually for personal car insurance carriers solely in the USA by the third year of use. Another study, conducted by accenture and the World Economic Forum WEF, found that 65% of insurance executives agreed to the fact that their businesses must adopt DLT to remain competitive. According to Le Maire (2019), the French Minister of the Economy and Finance, BC is definitely a major technology of the 21st century.

The goal of this thesis is to analyze the potential of BC technology for the insurance industry. In the course of this analysis, the use of BC / DLT in the financial industry, focused on the insurance sector, is examined. For this purpose, a decisive research question illuminated in a versatile way was derived:

Evaluation of Potential Use Cases for BC in the Insurance Industry Based on a Qualitative Analysis.

To obtain the best results for this scientific problem, this thesis is structured as follows: BC is defined and explained, and strengths, weaknesses, opportunities, and threats are pointed out. The current status of the insurance industry referring to digitalization in the form of InsurTechs and insurance start-ups and scale-ups is presented. Also, the value chain VC of insurers is disassembled, and each part is reviewed for a possible implementation of BC. Use cases for BC among all industries are highlighted and the elaborated areas impacted for the insurance industry are illustrated. As the core ofthis thesis, a qualitative analysis based on 16 expert interviews in the form of a two-round Delphi study is conducted by the author, and its results are presented in part 5. An evaluation of applicable use cases and a roadmap for the successful implementation of BC into the VC of any insurance business build the last theoretical section ofthis work.

2. Blockchain I Distributed Ledger Technology in general

Contrary to most academic papers, the following provides readers with facts on DLT and BC, before defining each term and clarifying common misconceptions in accordance with Pawczuk et al., 2019:

- BC itself is solely a technology and therefore not the solution to all problems in every industry on our globe (BC is not a panacea)
- BC is not Bitcoin: Bitcoin is the most popular of a myriad of use cases
- Conventional technologies are not redundant or obsolete
- Integrating data from other systems like Enterprise Resource Planning ERP is still needed
- Against many opinions and statements, it is not real-time

According to IBM (2019), BC is able to bring new ways of trust and transparency of data, simplicity and efficiency by reducing immense amounts of intermediary fees and operational spending. No doubt, achieving the full potential of adoption is only possible by demonstrating utility and interoperability. Also, BC is proven to getting more valuable with rising numbers of participants in the network due to added data. Mainly, BC was developed to serve the need for a cost-effective, efficient, secure, and reliable system for conducting and recording any financial transaction around the globe (cf. Gupta, 2020, p. 3). More than $1 billion were invested in BC companies since the technology’s creation in 2009, with a 59% increase in 2015 (cf. PwC, 2016, p. 1). It can be said BC has the potential for modernizing, streamlining, and simplifying the siloed design of the financial industry infrastructure with a shared fabric of common information.

The terms DLT and BC are defined and explained by using hands-on descriptions and examples from several academic sources. On the one hand opportunities arising from the use of BC for all industries are examined and on the other hand challenges that might harm the success story of a highly discussed technology are provided.

2.1 Definitions and hands-on descriptions

Due to a myriad of explanations and definitions for both DLT and BC it is almost impossible to find the right wording but by mixing some of the existing definitions, an adequate result can be achieved. According to the Rauchs et al. (2019, p. 12), there are innumera ble terms floating around the industry having either similar meanings or referring to completely different concepts leading to misunderstandings and preventable confusion in all industries and countries around the world. As a result, DLT and BC have established as umbrella terms being used interchangeably.

DLT is a subset of distributed systems, consisting of multiple independent components communicating with each other. Often, those components are based on a peer-to-peer (P2P) architecture enabling computers (nodes) to exchange information directly between each other without the need of a central server. Anyway, most distributed systems consist of multiple nodes storing and processing data while being owned by a single entity whereas DLT is not coordinated by a central authority. Therefore, DLT systems are able to maintain operational even in the presence of unreliable components and malicious parties trying to sabotage the system.

Today most businesses like banks, notary services, credit card providers, government agencies, stock exchanges, and insurance companies have centralized networks (cf. Figure 1). Meaning two of them are using isolated systems of record-keeping and to do any transaction they need a third party (cf. Pawczuk et al., 2019).

Figure 1: Centralized network (Author’s own illustration)

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Figure 2: Decentralized network (Author’s own illustration)

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In the future, two businesses might be able to act on a shared platform, facilitating the exchange of value without the need for a mediating third party (cf. Figure 2). The resilience is ensured through decentralization, transactions are encrypted and digitally signed by algorithms, and timestamping mechanisms create a sequential order ofthe records.

According to Rauchs et al. (2019, p. 12 f.) a DLT system is a multi-party consensus system enabling multiple distrusting parties to reach agreement over the ordering of transactions in an adversarial environment without relying on a central trusted entity. Mainly, a DLT system needs to be capable of ensuring five properties:

- shared recordkeeping: enabling multiple separate parties to provide data inputs and participate in creating new records.
- multi-party consensus: requiring multiple separate entities to collectively reach agreements on ordering of transactions while the central authority being absent.
- independent validation: enabling each participant to independently verify the state of its transactions and the integrity of the system, also involving detection of unauthorized changes applied to records.
- tamper evidence: allowing each participant to detect on-consensual modifications applied to all records.
- tamper resistance: make it hard to impossible for a single entity to unilaterally change any past records like e.g. transaction history.

Thus, DLT systems are constantly evolving and highly dynamic. Many ofthem still operate in closed, safeguarded environments with no adversarial dynamics for a plurality of reasons.

Also, the distinction between permissioned (private) and permissionless (public) DLT systems is important to understand the background of DLT. Permissioned DLT systems solely allow a limited number of actors to access the network, authorized by a gatekeeper. The participants are assigned various level of permissions. Besides, everyone has a fixed membership to identify all of them and as a result establish contractual agreements between them. In contrary, permissionless DLT systems allow anyone joining and leaving the network, as well as participating in the network consensus without any permission. Since those systems are based on a dynamic membership, relying on a combination of economic incentives via their native token and game theory in order to properly function and remain secure is essential.

For the upcoming parts of this thesis, especially for sections 3 and 4, it is essential to distinguish between public, private, and hybrid types of BC. While public BCs are accessible and transparent to everyone, private and hybrid BCs restrict access to the network and data to selected participants. The first application of a public BC was the crypto currency Bitcoin. Another well-known example is SCs in the Ethereum BC. In this context, Initial Coin Offering ICO offers a new way of raising capital by companies procuring capital from different investors.

1. Public BCs: Accessible to everyone and therefore comparable to the internet. This means that any user can participate in this type of BC technology and thus become part of the network. Public BCs are usually permissionless. In a public BC, any participant can join the consensus building and thus contribute to the validation of transactions. Accordingly, public BCs are P2P networks and function without an intermediary.
2. Private BCs: Contrary to public BCs, they are solely accessible to certain participants, similar to the intranet. Thus, there is a central authority regulating the access to a network in private BCs. It can also control the rights to read and write and assign transactions to certain network participants. Usually private BCs, unlike public types, are permissioned, so only selected participants can contribute in the validation of transactions. It should be noted that the validation is nevertheless performed by only one legal entity.
3. Hybrid BCs: A mixture of both BC types, consisting of a public and a private network state. Several private states can be linked to the public state. In a hybrid BC, the administrative rights to view, modify, and approve transactions are restricted to selected participants. Those can grant different rights to the different network participants, for instance limiting the visibility of transactions to individual participants only. A transaction in a hybrid BC is first processed in a private state and validated by the selected participants. It is then stored in the public state of the BC as an unchangeable data record as proof of existence. In this step, the transaction must be approved using the network consensus mechanism. This prevents individual network participants from being able to modify transactions on their own authority.
4. Consortium BCs are a special form of validation. With this BC type, the validation of transactions is distributed to a consortium consisting of several companies. Consensus is reached by the majority ofthe authorized participants.

According to Espich and Wunderlich (2019, p. 82 - 87), when combining the criteria from the literature search and the empirical study of, the findings are largely identical. Based on the results obtained, it can be concluded that BC technology in its public form is not suitable for the financial industry. One reason is public BCs are fully accessible due to their decentralized nature. Because they are open to any participant, anyone can contribute in the validation ofthe transaction using the consensus mechanism. This ensures the public BC functions without intermediaries, thus distributing the responsibility for transactions among the participants in the network. Therefore, a public BC does not define who takes responsibility for legal matters and how those are regulated, e.g. liability issues. Depending on how the consensus mechanism is implemented, it also brings disadvantages such as high energy consumption and the 51% attack1. Another reason against the implementation of public BC types in traditional financial institutions is transparency. It prevents the necessary confidentiality in the processing of transactions and leads to pseudo anonymity, which is why an identification of the network participants is impossible. Regulatory requirements such as know-your-customer KYC2, however, require participants to be clearly identifiable (cf. Demarco, 2019, p. 62 ff.). In addition, traditional financial institutions fulfil the role of central intermediaries and would make themselves superfluous by using a public BC.

Due to their centralized structure, private BCs are particularly suitable for internal company developments and for applications between a financial institution and its customers, the business-to-consumer B2C area. The financial institution as the central authority manages the access of the network participants and regulates their administrative rights. This means that the possibility to change a transaction afterwards is solely up to the financial institution. Incorrect or erroneous transactions can therefore be reversed if necessary. Due to the closed structures and limited access, there is a restricted number of participants in private BCs as opposed to public ones. However, one disadvantage of the small number of participants is reflected in the limited reliability. Nevertheless, the degree of transparency, which can be individually adjusted for the participants of the private BC is a major advantage. Thus, there is no anonymity in a private BC, so the financial institution can identify the network participants easily, clearly defining the legal responsibilities.

Hybrid BC types are suitable for applications in both the business-to-business B2B and B2C sectors. The reason for this is that one or more private states can be linked in a public one. This makes it possible to store specific information of a transaction privately and other information publicly, for instance to gain the trust of potential customers. By combining the private and public network states, hybrid BC types are decentralized to a limited extent. Like a public BC, the hybrid BC is open to all users, but its operation is restricted to selected participants. This means that a transaction on the public level only serves as proof of existence and does not contain any private content. At the private level, it is determined which participants are provided with visible access to sensitive and private transactions. The validation of transactions is thus limited to a few central participants, who reach an agreement in the course of a consensus mechanism and store the transaction in the public network state. The subsequent modification of false or erroneous transactions depends accordingly on the majority of those participants. Furthermore, the visibility of transactions can be individually adjusted, thus ensuring confidentiality. Therefore, transactions can be tracked, and the affected participants can be identified as they are known to the financial institution, clearly defining the legal responsibilities in a hybrid BC.

The author supports the opinion of Espich and Wunderlich (2019), indicating in particular consortium BCs offer a wide range of possible applications, including the optimization of securities business and trade financing. This fourth type of BC, fundamental for all sections of this thesis, is described as follows: Unlike private BCs, consortium BCs comprise several instances regulating access and rights of network participants. Thus, consortium BCs are particularly suitable when several companies work together, in the B2B area. As a special form, consortium BCs have some characteristics being similar to private BC types. They also have an individually adjustable degree of transparency, so that transactions can be handled confidentially. In addition, the participants in a consortium BC know each other, which is why there is no anonymity and each network participant can be clearly identified. An advantage over private BC types, however, is the validation of transactions, which is dependent on the majority of participants in the course of the consensus mechanism. Thus, consortium BCs have a higher degree of security than private ones. Another advantage is that there are potentially more participants in a consortium network than in private BC networks. This might lead to a higher degree of reliability and decentralization. As a result, private and consortium BCs show the most promising use in the financial industry.

BC enables record keeping and transfer of value through its innovative features:

- Single source of truth: one single source of records for all involved participants
- Immutable records: data immutability being created through encryption and distribution ofthe chain of records
- Decentralization/Resilience: due to decentralization the failure of an individual node does not lead to failure ofthe overall system
- Native automation: SCs can enable the automatic definition and processing of rules

According to Pawczuk et al. (2019) record keeping and transfer of value lead to use cases in the areas of efficiency and disruption. Efficiency in the sense of making operations cheaper, faster, and more transparent through increased auditability, improved working capital from faster settlement, reduced costs through automation of processes, and increased transaction and reconciliation speed. Whereas disruption is achieved by disrupting existing and creating new business models through micropayments for digital services, elimination of middlemen, monetizing the new infrastructure, and tokenizing and trading of products and assets.

Swan (2015, p. ix), differentiated multiple areas of application of BC by categorizing the technology in three maturity phases, BC 1.0, 2.0, and 3.0:

- BC 1.0: Application of BC technology mainly refers to the development of crypto currencies and their fields of application, such as currency transfers, remittances, as well as electronic payment systems.
- BC 2.0: The application of BC essentially comprises those contracts being concluded by BC in the financial industry. The application of BC technology in this category thus exceeds the complexity of simple financial transactions, for instance by securities, bonds, loans, mortgages and SCs.
- BC 3.0: The applications of BC go beyond the financial industry: use cases in social areas such as government, health, science, literature, culture, and arts.

BC 1.0 and BC 2.0 are mainly used in the financial services industry, whereas BC 3.0 might be considered in other sectors.

2.2 Challenges and opportunities

Since its introduction as one of innumerable use cases known as Bitcoin more than a decade ago, BC is hyped in most industries around the globe. Almost every day new use cases pop up and most of them are highlighted in section 4 of this paper, but what about the challenges and opportunities BC is facing? According to Pawczuk et al. (2019), Desphande et al. (2017), and IBM (2019), there are a myriad of both challenges and opportunities to be faced when thinking of implementing BC in any business active in any industry (cf. Table 1, p. 8).

Table 1: Challenges and opportunities for BC / DLT (Author’s own illustration)

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To sum up, BC can be seen as a technology with high potential to revolutionize many industries and therefore global markets. On the other hand, although it is known for over a decade, there is a lack of industry standards and regulations making it a tough call for most businesses around the globe whether to invest time and money for Research and Development R&D.

3. Current status insurance industry

To illustrate the current status of the global insurance industry and highlight future opportunities, this section is subdivided into four parts. This part addresses an analysis of existing and future digital technologies that might be able to change insurance business models radically and gives detailed insights into the present world of InsurTechs (cf. Rajani, 2019, p. 56 ff.). Part 3.1 does not solely focus on the conventional VC of an insurance business, but also illustrates a heatmap for digital opportunities in the VC of insurers and explains the impact of digitalization and InsurTechs on the VC. Besides, section 3.2 mainly concentrates on the impact BC might create on parts of the VC of any insurance business and how the VC can be improved by introducing BC. To conclude this topic of the thesis, several insurance innovations that can be powered by BC and the results of a SWOT-analysis helping to decide whether to implement BC in the insurance industry are illustrated.

An analysis focusing on the impact of digitalization on the insurance VC and the insurability of risks by Eling and Lehmann (2018) resulted in four major tasks the industry is facing. First, enhancing the customer experience, followed by improving the business processes of insurance companies, offering new products, and preparing for competition with other industries. Moreover, both industry experts identified three key areas of change with respect to insurability: the effect of new and more information on information asymmetry and risk pooling, the implications of new technologies on loss frequency and severity, and the increasing dependencies ofsystems through connectivity:

Table 2: List of digital technologies (Author’s own illustration, in accordance with Eling and Lehmann, 2018, p. 364 f.)

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As to be seen, the impact of digitalization on the VC of any insurance business is enormous. There have been lots of emerging technologies on the market in 2018 and the numbers are increasing continuously. This is why most insurance companies find theirselves in a complicated situation whether to invest into digitalization respectively in which of countless technologies. Thus, it can support all insurers to follow a path with several milestones, created by EY (2017, p.12) with the goal of becoming a digital insurer (cf. Appendix 1, p. 80).

The first milestone includes mobile apps, customer and agent portals, paperless processing, and integrated systems and is described as the evolving stage with the following capabilities:

- New business models operationalize innovation and transform the customer experience across segments and channels.
- Minimally viable products, clearly defined and well-executed, will address key business and technology challenges, provide automation opportunities, and improve customer experience across the VC.
- Digital strategy enables agility and flexibility in order to effectively respond to industry changes and market opportunities in line with overall business strategies.

Next, big data, predictive analytics, Internet of Things (loT), smart homes / businesses, pay-as-you-go models, BC, and drones form the second milestone, a mature phase, with capabilities like:

- Advanced business and technology capabilities enable rapid development with a focus on integration with and maintenance of legacy systems.
- Business model and governance enables effective partnering to rapidly integrate evolving technologies into the innovation agenda.
- Third-party businesses and IT platforms increase speed to market and streamline product development and launch.

Last, AI, machine learning, product development and pricing from social data, and P2P business models are parts ofthe optimized stage with the following capabilities:

- A fully integrated digital strategy enables constant innovation across the organization’s people, processes, and technology.
- Advanced data and analytics capabilities apply AI, machine learning ML, and predictive analytics in real-time to determine the “next-best” actions to deepen customer relationships and drive efficiency.
- The combination of an agile operating model, cloud-enabled “microservices” architecture and multidimensional product management capabilities leads to rapid development and deployment of technology to further transform the customer experience across channels.

If any insurance business strives to follow these milestones and puts its focus on deeply analyzing potential opportunities followed by working on a consistent implementation strategy, not only customers but also employees and thus, the companies can highly profit.

Not only technologies being described during the three phases by EY (2017), but also profound analyses ofthe insurance industries all around the globe led to highly interesting ideas and business models for InsurTechs.

According to Braun and Schreiber (2017, p. 19), almost no game-changing business model innovations were found until May of 2017. Empirical research illustrates that the three incumbents primary insurers, brokers, and reinsurers favor significantly different reactions when it comes to working with InsurTechs. Reinsurers and primary insurers strongly lean toward cooperation, whereas the brokers are rather competitive.

In 2016, an EY report (p. 2-3) explained that drivers accelerating InsurTechs might be very similar to the ones responsible for the rapid expansion of FinTechs. Therefore, not only connectivity and data, but also the consequences of the financial crisis with the resulting pressures on interest rates, and the general customer dissatisfaction with insurance can be essential for driving innovation in the insurance industry.

Between the years 2008 and 2015, USD 4.63 billion have been invested in insurance FinTechs or InsurTechs worldwide. In Germany, EUR 53.52 million have been raised by a total of 18 InsurTech start-ups until the end of 2016. The trend continued, when an U.S.- based InsurTech start-up providing on-demand insurance named Trov, received USD 45 million of HSB Ventures Inc., the venture capital subsidiary of German reinsurer Munich Re (cf. Reuters, 2017). Not even two years later, Munich Re invested another USD 250 million in Californian InsurTech start-up Next Insurance to increase shares to 27.5% (cf. Handelsblatt, 2019). In contrary to the opinion of most media and economic experts, the German insurance industry is well aware ofthe need to drive innovation.

According to EY (2016, p. 4 - 5) until the end of Q1 in 2016, the German InsurTech universe consisted out of 18 businesses with the following criteria:

- A ready-to-use product or service offering that is actively marketed
- Founded by independent partners and not as a direct or indirect spin-off from established companies
- Headquartered in Germany
- Exclusively focused on insurance products (as opposed to personal finance management solutions with appended insurance functionalities, etc.)
- Funded by investors or self-financed
- Exclusively focused on core insurance operations (as opposed to asset management, (IT) security, etc.)
- Market entry within or before Q1 2016
- Recently founded, early or later-stage start-up companies, normally with limited human, technological, and financial resources

Contrary to individual players in the U.S., each German InsurTech company is forced to cooperate with incumbents in one way or the other. Due to this interdependency, every InsurTech was classified into one ofthe four business model clusters Supporters; eMar- ketplaces, Aggregators, and Intermediaries; Disruptors; and Innovators. In total, 72% of the 18 analyzed InsurTechs are doing business in the cluster eMarketplaces, Aggregators, and Intermediaries, whereas solely 17% are Supporters and 11% Disruptors. Besides, most InsurTechs lack the capabilities and regulatory approval to issue own insurance products.

In 2016, the New Players Network published a report indicating out of 37 InsurTechs, 25 focus on B2C, 7 on B2B, and 8 on B2B2C (multiple answers were possible). This shows that cooperative starting points with assurance are essential for InsurTechs. The overview of all InsurTechs on the German market (cf. Appendix 2, p. 80) illustrates the different business models and activities, reaching from comparison portals, sales and product concepts and products, and services for B2C to insurance product B2B(2C) integration, policy management, brokers with online policy management, brokers via social networks, and finance brokers with online policy management.

The New Players Network report of 2017 shows that 47 InsurTechs focus on B2C, 16 on B2B, and 21 on B2B2C (again multiple answers were possible). Thus, one year later, the trend goes into redesigning conventional products and services, enabling higher transparency and regulation in claims management for the customers, and tackling the concept of P2P insurances. In a nutshell, the focus lies on the innovation of processes instead of specific products and services. As to be seen in Appendix 3, p. 81, the business models have been expanded by introducing new fields of operations: digital insurers, P2P, data management, claims management, consulting, and chatbots. Besides, the number of insurance products and comparison portals for B2B and B2C has increased immensely within one year.

One year later, in 2018, the New Players Network report indicates rising numbers. 23 InsurTechs put their focus on B2B, 61 on B2C, and 25 on B2B2C (multiple answers were possible). The trend towards digital insurance companies continued in 2018. The new players demonstrated great speed with highly professional structures, diversified teams, industry expertise, and above-average funding. For the first time, the responsible drivers behind the digital insurers were, in addition to independent start-up teams and company builders, increasingly insurance companies. Nevertheless, the innovative power in the business models of digital insurers still originates from the process level - but not from the products and services. Besides, insurance companies are more willing to take risks when cooperating with technology-driven start-ups and develop customer-centric innovations.

The New Players Network report of 2019 showed major differences when looking at the customer orientation with 60 InsurTechs concentrating on B2B, 60 on B2C, and 36 on B2B2C (again multiple answers were possible). The InsurTech core market stagnated: The newly founded companies were opposed by a noticeable consolidation of the market. At the same time, a handful of players had been able to strengthen their market position through scaling and initial cooperation successes. On the other hand, a large number of young tech companies were successfully entering the market. At this time, in Germany alone, studies counted 92 co-operations between InsurTechs and insurance companies. This was made possible by the new B2B orientation of many start-ups, which were initially confrontational with insurers and competed with them for customers. However, since these companies rely on the large insurance businesses, especially for long-term insurance products, the development of their own customer base proved to take too much time and to be difficult and cost-intensive for the InsurTechs. For the insurance companies, on the other hand, these collaborations opened up new opportunities to meet the challenges of the changing needs of the digital customer. Thus, the cooperative reorientation of startups leads to positive synergies. While the InsurTechs gained access to markets that were previously reserved for the large, trustworthy insurance companies, the insurance companies in turn improved the loyalty to their enormous customer base by counting on the expertise of the start-ups with digitalization and customer experience. Also, the InsurTech market became more mature by increased investment and expansion, especially from other countries. Besides, providers from other markets - above all from Israel - were entering the German insurance market and impressing with technological know-how.

But what about BC? The BC bubble continued to grow: Many interesting discussions and panels addressing and reminding of the hype came up on a weekly basis. There were exciting approaches, but also a lack of concrete insurance use cases in which BC technology is anchored in the direct value chain VC. Due to many insurers being unsure how to use the technology, the use cases have so far been limited to internal administrative processes.

Last, the New Players Network report for 2020 (cf. Appendix 4, p. 81) shows 88 InsurTechs focusing on the B2B market, 71 on the B2C side, and 48 on the B2B2C segment (multiple answers were possible). The diversity of digital products and services has also increased due to the establishment of new digital insurers and the market entry of international digital insurers and underwriters. The focus is usually on the consistent digitalization of processes, especially in the property-casualty sector. In claims and benefits management, some players are concentrating on AI and big data, thus taking claims diagnosis to a new level.

The segment of InsurTechs that are active as API service providers continues to grow, as insurers want to be part of the new ecosystems but are afraid of not being able to position themselves in time.

The symbiosis between technology-driven founders and cooperative groups seems more promising than ever.

According to Braun and Schreiber (2017, p. 7), the business model patterns in InsurTech start-ups include comparison portals, P2P insurance, big data analytics / insurance software, digital brokers, on-demand insurance, loT, insurance cross sellers, digital insurers, and BC I SCs. When looking at all reports of the New Players Network from 2016 to 2020, these business model patterns are complemented by sales and distribution concepts, chat bots, consulting, digital underwriters, claims and performance management, digital enablers, API service providers, mobility, policy managers, authentication, customer interaction, and data security.

When looking at the InsurTech Radar of 2019 (p. 13 - 29) by Wyman and Policen Direkt, today not only start-ups but also scale-ups3 and zombies4 form significant parts of the

German InsurTech market. Their evaluation radar consists out of three categories: digital insurance offers, sales of insurance products, and digitalization of operations. Each of those addresses various fields scale-ups, start-ups, and pre-lnsurTechs are competing in (cf. Table 3, p. 16).

Table 3: Evaluation Radar InsurTechs (Author’s own illustration, in accordance with Wyman and Policen Direkt, p. 16-27)

Abbildung in dieser Leseprobe nicht enthalten

As to be seen in the overview of the InsurTech Radar 2019, there is very high strategic potential for neocarriers in digital process innovation, from insured to protected, and new digital risks in digital insurance offers. When it comes to sales of insurance products, the highest strategic potential can be found in sales platforms, business platforms, comparison portals, and financial partners. Besides, offering / underwriting, claims, and enabling insurance sales form the competitive fields with the highest strategic potential in the last category, digitalization ofoperations.

To sum up, InsurTechs cannot solely help insurance companies to reinvent their sales division but a considerable number of underwriting start-ups will go into action. Also, it seems very likely the German market will experience the first InsurTech-unicorn arising in the upcoming two to three years. Nevertheless, Eling and Lehmann (2018, p. 377) mention several reasons indicating InsurTechs might not cause a major change to the insurance industry: business models can be imitated fast and easy, regulations and lack of expertise as entry barriers when InsurTechs are willing to expand their businesses, insurers are powerful enough to easily acquire any InsurTech, and the fact InsurTechs are seeking for cooperation instead of rivalry with conventional insurance businesses.

3.1 Value chain of an insurance company

In this section, different concepts of VCs of insurance businesses, ordered from the most essential parts to very detailed descriptions are presented, followed by a heat map to prioritize digital opportunities in the VC of any insurer. Besides, the impact of numerous digitalization technologies on each specific VC process are illustrated and explained.

When thinking of elements, a VC of any insurance company generally consists of, the illustration in the following appears to include the most fundamental processes:

Figure 3: Value chain of an insurance company (Author’s own illustration, in accordance with PwC, 2003)

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In their paper The Impact of Digitalization on the Insurance Value Chain Eling and Lehmann (2018) put the focus on an insurance specific VC based on Porter and Rahlfs giving a more specific and detailed overview of the departments of a conventional insurance company:

Figure 4: Insurance specific value chain based on Porter and Ralhfs (Eling and Lehmann, 2018, p.362)

Abbildung in dieser Leseprobe nicht enthalten

Since the insurance specific VC illustrated in Figure 4 does address all primary and support activities but lacks in detailed descriptions, the VC to be seen in Figure 5, p. 19 has been used for the empirical analysis, including the questionnaire for all expert interviews in this thesis.

Figure 5: Value chain of an insuer as used in questionnaires (Author’s own illustration, in accordance with Strategic Systems International, 2020) (cf. Appendix 9, p.92 for bigger version)

Abbildung in dieser Leseprobe nicht enthalten

Due to a myriad of digital opportunities to support, modify, or improve processes in the VC of an insurance firm, it is important to prioritize possible areas of application. In the heat map McKinsey created in 2015, the opportunity ratings are based on assessment of both business impact and readiness ortime-to-impact (cf. Appendix 5, p. 82).

As to be seen, the digital opportunities for personal products in the VC steps marketing, product development, sales and distribution, policy issuance, policy servicing, and claims management are high, especially for insurances covering homes. When looking at commercial products, the highest digital opportunities can be seen in marketing, product development, underwriting, sales and distribution, policy issuance, and policy servicing for SME insurance. Group products seem to offer less digital opportunities on steps of the VC than personal and commercial ones. However, marketing, product development, sales and distribution, and policy servicing might be considered.

In the following, an overview of the impact of numerous digital technologies, including big data, video platforms, websites, social networks, and messenger, loT, BC, cloud computing, chatbot and AI, robo-advisors, IT development, mobile devices and apps, and video calls on primary and support activities of the insurance specific VC created by Porter and Rahlfs based on the paper of Eling and Lehmann (2018) is presented (cf. Table 4, p. 20 - 22).

Table 4: Impact of digitalization on an insurer’s value chain (Author’s own illustration, in accordance with Eling and Lehmann, 2018, p. 367 f.)

Abbildung in dieser Leseprobe nicht enthalten

To sum up, big data, AI, loT, and BC are the digital technologies having impact on most primary and support activities. In a nutshell, the impact of these technologies on the VC of insurance businesses is changing the way of interaction between insurers and customers, influencing all business and decision-making processes due to automation, including risk assessment, and modifying existing products as well as allowing new product offerings.

Since InsurTechs form a large part of the modern insurance industry and also this section of the thesis, EY (2016, p. 14) evaluated the relationship between the degree of disruptiveness of InsurTechs and the potentials for further disruption of the VC of insurance companies (cf. Appendix 6, p. 82). The study shows InsurTechs operating in proximity to end customers (B2C), and thus offering front and middle office functionalities, represent only limited depth of disruption. Nevertheless, exceptions apply where disruptors have successfully entered the market. Also, the findings are aligned with the opinion of EY (2016, p. 1) indicating most German InsurTech providers operating B2C and as a result sales and distribution being the most affected areas in the VC.

Eling and Lehmann (2018, p. 377), explained that the insurance industry will not lose parts of its VC to other industries, since the realistic return on equity is too small to justify investments and due to more attractive alternatives existing like cooperation with insurers. Besides, lack of expertise and market regulations form significant entry barriers.

According to EY (2017, p. 14), the leaders of tomorrow have to focus on the art of the possible today and begin moving instantaneously toward incremental performance improvements based on better digital capabilities across the VC.

3.2 Improving value chain bv introducing Blockchain

The following section mainly deals with the impact BC might create on parts of the VC of any insurance business. As shown above, a myriad of technologies might change the landscape of insurers on a global basis, but BC is considered to be one of the most powerful opportunities.

To illustrate BC’s impact, its solutions and possible benefits on the seven business processes gathering information, underwriting and pricing, quote and bind, billing and payments, policy issuance, claims, and insurance regulators BC, Capgemini Consulting created a detailed overview:

Figure 6: BC applications’ impact across the insurance VC (Author’s own illustration, in accordance with Capgemini, 2016, p. 7)

Abbildung in dieser Leseprobe nicht enthalten

To summarize, BC can have a significant impact on changing / improving all parts of the VC of any insurance company. When looking on the market, several impressive solutions can be found, and the number of new application fields is growing steadily. But not only Capgemini focused on analyzing the potential solutions and benefits of BC on the insurance VC.

According to EY (2017 p. 4), digital transformation in general delivers tangible and intangible value with specific benefits in six key areas across the insurance VC: cost reduction, customer experience enhancement, speed to market, sales productivity, underwriting efficiency, and claims efficiency. The researchers developed a digital transformation scorecard reflecting how the benefits apply to different technologies and initiatives: Omnichannel, Big data analytics, loT, Telematics, Voice biometrics and analysis, Drones and satellites, and BC. The scorecard (cf. Appendix 7, p. 83) illustrates Big data analytics and BC as the only digital transformations delivering tangible and intangible value with specific benefits to all six key areas across the VC of insurance businesses.

Thus, BC is among the most powerful technologies for enabling digital transformation. It provides a foundation for entirely new business models and product offerings, such as P2P insurance, thanks to its ability to provide virtual assistance for quoting, claims handling, and more tasks. Besides, BC provides a new level of information transparency, accuracy and currency, with easier access for all parties and stakeholders in any insurance contract. Through higher levels of autonomy and attribution, BC’s architectural properties provide a strong digital foundation to drive use of mobile-to-mobile transactions and swifter, secure payment models, improved data transparency, and reduced risk of duplication or exposure management. All insurance businesses are interested in converting selected policies from an existing book to a P2P market. A BC network is developed as a mechanism for integrating exactly this P2P market with a distributed transaction ledger, transparent auditability and smart executable policy.

Another emerging business model, E-aggregators, is likely to gain traction, since it is appealing to both insurers and their customers. Insurance companies can offer better pricing due to reduced commissions compared to a traditional agent-based distribution model, while customers gain new freedom to compare different policies based on better information. Obviously, e-aggregators (whether fully independent or built through an existing technology platform) will require a sophisticated and robust digital platform for gathering information from different insurers to present it to consumers in the context of a clear and intuitive experience. It is also important for insurance businesses to transfer information to e-aggregators rapidly, since otherwise, they might risk to missing out on sales opportunities. As a result, BC is the perfect technology for connecting e-aggregators and insurance companies.

In a nutshell, insurance businesses being able to integrate process innovations and new tools like Big data analytics and BC with existing systems - and do so efficiently and without introducing new operational risk - will gain a tangible and sustainable advantage. In detail, product development can benefit from SCs, e.g. Fizzy by AXA, and underwriting can be automized by storing and retrieving all relevant information on customers. Also, customer experience in claims management can be enhanced by automated payouts through storing and retrieving all relevant information via mobile devices with apps so customers can file claims via their smartphones and on-the-go. Besides, the transaction costs in asset management can be decreased immensely by using one central database.

3.3 Implementing Blockchain in the insurance sector

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Almost 75% of insurance leaders surveyed by PwC (2016, p. 1) considered insurance to be the most disrupted industry when it comes to BC / DLT. In 2016, Bought By Many Chief Marketing Officer (CMO) Sam Gilbert created his version of Gartner’s hype cycle (cf. Figure 7) addressing insurance disruption (cf. Figure 8).

Figure 7: Gartner’s hype cycle (Gartner, 2020)

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Figure 8: Insurance disruption hype cycle (Glibert, 2018

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BC / DLT can be seen almost at the very beginning of the curve, between the technology trigger phase and the peak of inflated expectations, signifying BC is comparatively new to this sector and has not been fully explored yet (cf. Gatteschi et al., 2018, p. 2). Today, in the first quarter of 2020, BC / DLT already passed the peak of inflated expectations and the trough of disillusionment, settling between the latter and the plateau of productivity, in the middle of the slope of enlightenment. Nevertheless, it is hard to classify BC / DLT since new applications and innovations based on this omnipresent technology emerge on a daily basis, eventually kicking it back to the almost same position in the hype cycle it was assigned to in 2016 by Sam Gilbert.

As mentioned several times throughout this thesis, innumerable applications and benefits for BC do exist and many more are still to discover. In 2016, Capgemini listed nine insurance innovations that can be powered by BC (cf. Figure 9, p. 26).


1 The 51% attack describes a state in which 51% ofthe computing powerfor the mining of digital currencies comes froma single mining pool. With a51% share, this pool theoretically gains control ofthe digital currency and could act as follows: Obstruct transaction confirmation, issue coins multiple times, or prevent other miners from mining valid blocks (cf. Duthel, 2017, p. 19).

2 With KYC, the customer grants a company access to identity data when necessary for a contract closure. Once the KYC profile is verified, a customer can forward the verified identity data to other companies for different contracts with the same tool, avoiding the need to repeat the full identification and verification process, thus speeding up and increasing efficiency in the onboarding of new customers (cf. McKinsey, 2016, p. 3).

3 Scale-up: Start-ups in a veryfast-growing phase

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Evaluation of Potential Use Cases for Blockchain in the Insurance Industry
A Qualitative Analysis
International School of Management, Campus Munich
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Blockchain, Insurance, Insurtechs, Analysis, Use Cases Blockchain, Insurance Industry
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Ricardo Escoda (Author), 2020, Evaluation of Potential Use Cases for Blockchain in the Insurance Industry, Munich, GRIN Verlag, https://www.grin.com/document/994303


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