Blog and news posts in all categories
In my lecture, “The Tokenization of Everything,” I explore the topic of tokenization starting from the concept all the way to the status quo of the token economy. The lecture was created as part of the Frankfurt School Blockchain Center’s Industry Insights series.
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37 billion US dollars. With this issue volume, Ant Group, formerly Ant Financial, a Chinese tech group created by Alibaba founder Jack Ma, was to be traded on the Shanghai and Hong Kong stock exchanges. Many see new value creation logics, so-called business ecosystems, which Ant Group is said to have developed for itself, as being behind the rapid growth and the extremely high company valuation. However, the assumption that Ant Group is an ecosystem should be treated with caution. In the following, we have analyzed individual platforms in more detail based on the work of the CC Ecosystem.
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For banks, the evolution toward a customer-centric, flexible and adaptable organization is essential. Yet less than one percent of banks operate in a largely agile manner. The TEC model helps banks set the course for developing an agile corporate culture.
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Breaking up the value chain in the financial industry has been explicitly promoted and demanded in all states of the European Union since January 13, 2018 by the Payment Services Directive II, or PSD2 for short. PSD2 gives AIS and PIS open access to customer account data and payment behavior via application programming interfaces (APIs). This creates potential for the use of AI technologies.
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The trend towards value creation in ecosystems has led to a multitude of ecosystem definitions in the past. This makes it difficult even for the interested reader to understand the research results presented. To what extent do the ecosystem concepts really differ and can the findings of one field be transferred to the other?
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What distinguishes an ecosystem from other forms of cooperation? There are already various other concepts that describe cooperations or network structures, but they do not represent ecosystems at all. This article deals with forms of cooperation and their differentiation from ecosystems and presents 8 criteria for the identification of ecosystems.
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In my last post, I explained what characteristics distinguish the robo-advisory process from that of traditional client advice provided by a human advisor. Today, I explain how exactly these characteristics affect the traditional client advisory process and change it to create the robo-advisory process that I presented in the third post of this series. Finally, I answer the question of which of the two advisory processes is the better one.
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Another rather exciting year has passed, during which was able to advance some of my personal projects, among them being self-reflection. Reading books is one of the tools I use to reflect on my perspectives and to refine and adjust them, wherever I feel necessary. While I tried to tap into a diverse set of ideas and experiences during 2020, this article will present five highlights, hoping that some of the ideas I found most inspiring will provide some food for thought for you, as well.
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In my last post, I detailed the customer advisory processes for a human advisor and a robo-advisor. Today I explain what causes these differences. This step is important to be able to understand and explain the differences between the two advisory processes and then, in a next step, to be able to derive the impact of robo-advisory services and to evaluate the advantages and disadvantages of robo-advisors compared to the traditional customer advisory process.
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My last post before the Christmas break was about which business models and strategies a robo-advisor can align its offering with. Today, I will present the customer advisory processes of traditional, human banking advisory services and robo-advisors. In the rest of the series, I will then discuss the impact caused by robo-advisors which form of advisory is better and whether robo-advisors will be able to replace bank advisors in the near future.
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Last week’s post was about how robo advisory services are defined and how they came into being, whether they are serious competition for banks, and what stage of development robo-advisors are at now. Today I present the prevailing business models and strategies of robo-advisors.
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The culture of a company is the beliefs, values and behaviors that shape the social and psychological environment of the organization. As a 2017 Capgemini change management study shows, the companies that transform most successfully are also those that involve their employees in the transformation process from start to finish and thus actively promote a change in corporate culture. Many studies from science and practice also make it clear that the path to an agile culture is not an easy one. In a recent article, the consulting firm McKinsey has published four lessons learned from its experience with clients around the world that offer a good approach to how companies can foster the development of an agile culture.
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The ever-increasing digitalization confronts companies with dynamically changing environmental conditions requiring them to regularly adapt their product and service portfolios and business models to changing circumstances. In order to meet this requirement, an agile organizational culture is indispensable.
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Digitalization is necessary, but its costs are often not offset by corresponding additional revenues. Ecosystems can provide a solution.
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With a share of 10% of all FinTech companies in Europe, Switzerland is one of the leading innovation centers. The focus on distributed ledger technology is striking in Switzerland, especially in Crypto Valley Zug. In the meantime, 250 crypto companies have already settled there or are planning a location. However, Switzerland cannot rest on its laurels: to remain a leading innovation center, it constantly needs new FinTechs with innovative ideas. In order to identify gaps in the existing range of Swiss blockchain FinTechs and thus the innovation potential for further services, I analyze in this article the current service offering of the Swiss blockchain FinTech scene.
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The discussion on the use of artificial intelligence (AI) often revolves around the optimization of processes or the recognition of patterns in huge amounts of […]
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Multinational companies have extensive and complex supply chains to operate as efficiently as possible. Within these production networks there is little room for error and preventive stress tests are rarely carried out. The COVID-19 pandemic has shown how fragile such structures can be and what consequences can arise if too little attention is paid to risk exposure (Sneader & Lund, 2020).
But how could companies have better prepared for an unexpected event like the COVID-19 pandemic? And how relevant is organizational agility and resilience in this context?
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The current corona virus situation has shown us that we are capable of taking big steps in digitalization in a very short time in the education sector. Digital methods have already found their way into schools and universities, but this sometimes hesitant development was reinforced to an unprecedented degree by the homeschooling required during the peak of the pandemic. Collaborative platforms have grown enormously, and new teaching and learning formats have been introduced in the form of explanatory videos and virtual lessons or group work. One technology, or rather a concept that has been largely ignored so far, but which offers great potential for improving both digital and face-to-face teaching, is augmented reality (AR), the enrichment of reality with virtual content.
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According to a survey, 90% of bankers are convinced that open banking can increase the organic growth of banks by 10%[1]. A global banking survey also found that 86% of banks plan to use open APIs in the next 12 months to enable open banking capabilities[2]. To address this development and better understand the opportunities and implications of the trend towards opening up a banks’ own IT infrastructure, the Business Engineering Institute and Commerzbank have jointly authored an open banking white paper on the future of corporate banking collaboration. In this white paper, we explored the overarching questions of what the open banking concept is all about and how it will change value creation, particularly in corporate banking.
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Recently, the Swiss Bankers Association published an overview that outlines the status of open banking in Switzerland and formulates requirements for its further development of open banking. These requirements include the clear strategic positioning of Swiss banks and the joint development of standardized APIs for data exchange between financial institutions and third-party providers. The aim of this overview is to “support the dialogue in the financial center”, as Richard Hess writes in the initial contribution to the blog parade of the Swiss Bankers Association. The Open Banking Summit of the standardization initiative OpenBankingProject.ch pursued the same goal on September 10th, with various presentations focusing on these two requirements.
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The examples of Alibaba, Amazon and Airbnb impressively demonstrate the value creation potential of ecosystems compared to traditional, linear value creation structures. But how do you plan and develop an ecosystem? As I explained in the first part of this blog post, traditional strategy concepts cannot be easily transferred to ecosystems. So what constitutes an ecosystem strategy? In the following, I will present three further prerequisites for the successful design of an ecosystem, which must be part of every discussion about building or participating in any ecosystem.
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The term ecosystem strategy is very present in science and practice and numerous strategic recommendations for action, for example for taking on certain roles, can already be identified. However, a uniform understanding of the term or an overarching approach to building up or positioning oneself in ecosystems does not yet exist. In this blog post I try to approach the topic by identifying four prerequisites for the success of an ecosystem and by investigating the extent to which an ecosystem strategy differs from a classical corporate strategy.
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In the first two contributions to this three-part series, we have shown that the simultaneous use of distributed ledger technology and artificial intelligence can improve today’s lending process in many ways. However, the lending process is only one of many processes that can potentially benefit from the merging of DLT and AI. In the following we have compiled the process characteristics that indicate that a process leads to the convergence of the two technologies and can therefore be optimized by them.
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Technological convergence means that two or more technologies merge into a single system that is more powerful than each of the technologies individually. Since AI and DLT are technologies that have some opposing characteristics (e.g., centrality vs. decentralization; transparency vs. black box), the two technologies complement each other and can compensate for crucial weaknesses of the other. Since the lending process is one of the main processes in the banking business and involves both communication and data analysis (AI) and the transmission of sensitive data (DLT), it is ideally suited for a practical examination of both the prerequisites and the effects of technological convergence.
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The distributed ledger technology (DLT) and artificial intelligence (AI) are two promising technologies that are at the top of their hype cycle. Scientists assume that the convergence of the blockchain, the Internet of Things (IoT) and artificial intelligence offers considerable opportunities, such as accelerating the pace of service, process and business model innovations. However, the information available in the current literature and research on how such an integration could be implemented in practice is still sparse. Over the next week, this series of articles will explore how specific elements of DLT and AI can be combined to achieve potential technological convergence, and what significance this convergence has for the lending process.
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