Everything about digitization in the financial industry
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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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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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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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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How do you decide whether you want to participate in an ecosystem? And if so, in which position? This article offers a first clue.
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Insurtechs are constantly making news in the financial world. Whether they are seen as the big competitor to insurers, exploiting the limping digitalization of the big players, or helping conventional insurers to arrive in the 21st century, startups are bringing a breath of fresh air to the industry through new technologies such as artificial intelligence (AI) or natural language processing (NLP). Due to the abundance of new services and business models, it is appealing from both an entrepreneurial and scientific perspective to gain an overview of the structure and innovative capacity of the market.
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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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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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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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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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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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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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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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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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Since the beginning of 2020, the coronavirus pandemic has caused an enormous slump in the global economy due to lockdowns all over the world. The IMF expects global GDP to decline by 5%, and for the euro zone it is likely to be as much as 10.2% [1] – and these are the figures before a possible second wave. It remains to be seen how the economy will recover. What can already be well assessed, on the other hand, are the qualitative effects of the pandemic on the macro trends relevant to the financial industry.
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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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