Everything about blockchain
Both technical and associated organizational decentralization are taking on an ever-increasing role due to the disruptive technologies DLT and blockchain, and continue to advance as new use cases and application areas are established. A new and increasingly growing trend are decentralized aut
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One of the biggest challenges in this context is the handling of bank-specific and personal data and its processing by artificial intelligence (AI). The basis of data-driven services is a high-quality and up-to-date database. But not all companies have a large enough database to train an algorithm, and the sharing and basic use of some data is strictly limited – sometimes even within the company. To counteract these problems, the concept of synthetic data has become established.
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The topic of digital assets has finally reached the mainstream. The potential for services that make it easier for customers to acquire digital assets is great, but so far only a few financial institutions offer such services. According to the banks, there are many reasons for the lack of a comprehensive offering, e.g. concerns about the value of digital assets, high scepticism regarding money laundering or a lack of know-how in the operational handling of the new asset class. This blog post addresses the last point and aims to show that there are various operational implementation options for a basic digital asset offering for regulated banks.
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Since 2017, the Core Banking Radar has regularly analyzed the most common as well as emerging core banking systems in Switzerland using a comprehensive assessment model[1]. The latest publication highlights the Estonian neo-system Tuum and shows success factors for its use. The Core Banking Radar is a joint project of Swisscom and the Business Engineering Institute St. Gallen.
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Cloud solutions are faster, more flexible and less expensive than on-premise solutions. However, the best-known cloud infrastructures – Amazon Web Services (AWS), Google Cloud, Microsoft Azure and IBM’s Red Hat – all have one thing in common: they are headquartered in the USA and are therefore subject to the (decidedly lax) US data protection “law”. This makes using the services difficult from a data protection perspective, especially for financial institutions. The European Gaia-X project could be a real alternative.
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When attempting to assess the potential of tokens for their business, decision makers face the challenge that while there are tons of technical literature on token design, there are no simple decision support tools to help identify suitable use cases for their own business.
Therefore, we conducted a study to derive archetypes that can be observed in a variety of existing token-based solutions. These archetypes provide companies with an initial starting point for addressing the benefits and potential applications of token-based solutions.
The credibility of data in the supply chain is often questioned. Using distributed ledger technology in this context is proving to be a promising use case in various industries.
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For a long time, fintechs were seen as a direct threat and competitor to traditional banks due to their high innovative capacity and the disruption potential attributed to them. In recent years, however, it has become apparent that both fintechs and banks are more interested in cooperation than in competition. However, there are factors that can cause a cooperation between a bank and a fintech to fail. How to prevent this is the topic of this article.
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The second Open Banking Summit in Switzerland took place at the end of August – a good opportunity to take another look at what is currently happening in the Swiss open banking scene.
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Claims such as “robo advisors will replace bankers” or “banks will have disappeared” could be read frequently over the last decade in connection with accelerated digitization. However, we don’t notice much of this in Europe. Is the fintech movement really a revolution or rather an evolution?
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Despite many known advantages, it is still difficult, especially for practitioners, to identify concrete application areas for blockchain-based applications. This article takes this as its starting point and presents a sector-independent typology of application fields, which not only serves to analyze existing blockchain applications, but also provides users with an orientation as to which general possible uses there are for the blockchain and how complex an initial implementation of the respective applications can turn out to be.
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For small and medium-sized enterprises (SMEs), AI offers enormous potential for scaling and increasing efficiency, but for these companies in particular there is no alternative to using GAFA services because they themselves lack, for example, machine learning knowledge or the necessary data to train algorithms. An analytics marketplace could solve these challenges.
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Two weeks ago, we introduced the BlockWiki here on the blog, a website where we compile and explain the most important blockchain and DLT terms. The blockchain map, a graphical summary of blockchain terms, can also be purchased there. The overview differentiates between the technical and the business view of the Blockchain. After the left side with the technical view was described in detail in the blog post “The Technical Side of the Blockchain Map”, in this article we focus on the business view of the blockchain universe and explain the terms depicted there within their context.
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In our last blog post, “The BlockWiki – The Blockchain Explained,” we introduced the BlockWiki, a compilation of blockchain terms and research on blockchain and distributed ledger technology. One component of the BlockWiki is the Blockchain Map, which graphically depicts the technical background, various uses and functions of the technology. It is divided into two sections: technical view and business view. In this blog post, the focus will be on explaining the elements of the technical side.
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The blockchain is gaining more and more attention and importance worldwide and across industries. Although there is a broad field of application behind this technology on both the technical and economic side, the terms surrounding blockchain in research are not entirely consistent and often unclear. The BlockWiki is a website where relevant terms regarding the blockchain are explained and related to each other and can therefore be used as a reference tool by anyone interested in the blockchain.
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On the occasion of the 20th anniversary of the Agile Manifesto, I would like to take another look at the central concepts of the document. Because even after this long time, the values and principles contained represent the basic tenets of the agile working method and can be found in many different agile methods, such as Scrum, Kanban or SAFe.
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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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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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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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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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In the course of the change from a personalized customer experience at a bank to the desire for standardized and digitized processes, “robo-advisors” are becoming increasingly important. Today’s article looks at 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.
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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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