Everything about data, big data and their opportunities

Need for Change – Challenges for Companies in Using Distributed Ledger Technologies

Digital products and services are not only changing the everyday life of individuals or society. The new technologies on which they are based can also lead to changes in operational IT in a wide range of business areas, especially in service companies. The distributed ledger technology, a new form of a distributed database, which ensures the integrity of all types of transactions without a central authority, is one such technology. Besides the question, in which areas it can be used, companies therefore also have to ask what internal impact can be expected when using this technology.

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Insights from the Open Banking Summit 2022

“Financial Industry Meets BigTech”: On the 25th of August 2022, the third Open Banking Summit opened its doors in the Google event rooms in Zurich. The OpenBankingProject.ch organized this event for the third time and once again provided a stimulating get-together on the topic of open banking. The event featured national and international success stories, and the subsequent panel discussion reflected on and discussed selected topics and questions posed by the audience. Once again this year, around 120 decision-makers from the Swiss financial sector gathered to learn about current developments and to network over the subsequent aperitif.

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Core Banking Radar – “Neo Core Banking Systems and their importance for the IT Architecture of the future”

Various trends, such as an increased focus on customer interaction and embedded banking, will shape the bank of the future. To provide services along the entire “customer journey”, banks are increasingly dependent on networking with other industries. The expansion of partnerships in the ecosystem goes hand in hand with the promotion of integration capabilities via APIs and steady investment in the banking architecture. The latest Core Banking Radar article examines the system architecture of the bank of the future and, in this context, compares the four neo-core banking systems examined in earlier publications of this series.

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Data Lineage: A Path to the Data-Driven Enterprise?

In recent years, the call for data-driven decisions and processes has grown rapidly in companies across every industry. Data-driven companies such as Apple, Alphabet, and Microsoft are now among the most valuable companies in the world. But in order to make data-driven decisions, different challenges concerning a company and its data (management) need to be solved. These include the challenge of keeping an overview of their own data stocks. An overview of one’s own data is playing an increasingly important role in the financial industry, too. Banks must be able to disclose their data to the regulator at any time, and regular reporting is becoming the norm. In this context, the principle of “data lineage” has become established. Based on the approach of metadata management, this concept enables the structuring and visualization of one’s data inventories and sources. The following article explains the most important basics of the topic “data lineage” and shows possible areas of application in the financial industry.

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DeFi 2.0

Since DeFi Summer 2020, DeFi applications have become a significant trend in the blockchain industry. However, vulnerabilities of DeFi applications have also been identified over the past 24 months. The third and final part of our series “Decentralized Finance – a Hype, a Threat or an Opportunity for Regulated Financial Institutions?” takes a closer look at recent developments in the DeFi sector (DeFi 2.0) and how they address well-known challenges in the context of DeFi, such as the potential for errors when setting up smart contracts, the lack of incentive structures for investors, or the requirements for investors’ technical and professional knowledge, without compromising the strengths of DeFi applications.

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Opportunities & Challenges for Banks in the Context of Decentralized Finance

The term decentralized finance (DeFi) refers to a decentralized, blockchain-based architecture for processing financial transactions without intermediaries. we will show in this article why offering access to digital assets and DeFi business models is worthwhile for financial institutions and what challenges they have to overcome in order to provide such an offering to their customers. Based on practical examples, positioning possibilities for regulated institutions will be presented as well as concrete opportunities and challenges.

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Digital Twins – The Merging of the Real and the Virtual World

The amount of data about real products, processes, and services has increased dramatically in recent years. This opens up new possibilities for planning, simulation and analysis. For this purpose, more and more companies use the concept of a digital twin. But what are digital twins and what potentials do they offer at the enterprise level, especially in the financial industry?

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Federated Learning – Efficient Machine Learning That Respects Privacy?

In the financial industry, customers expect high standards with regard to data protection and the integrity of their own data. Nevertheless, from the perspective of value creation, it is essential for banks to evaluate customer data using statistical methods and algorithms. Banks are thus caught in a conflict between maintaining data privacy and enforcing their own business model. To address this problem, the concept of “federated learning” has become established on the market in recent years, in which the data used for model training is always stored decentrally and the models are trained decentrally.

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“Data-centric AI” – A shift in the AI mindset?

The traditional approach to AI focuses on the process of training the model. The underlying data is often a secondary concern. This approach works particularly well for Internet corporations, as they have vast amounts of data and the capabilities to analyze it. In contrast, there is little potential for using AI in small businesses with this approach due to a lack of data. Therefore, it is worth taking a look at the data – moving away from model-centric to data-centric AI.

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What Exactly Is “Decentralized Finance”?

Decentralized Finance (DeFi) has been one of the dominant trends in digital assets at least since the DeFi summer 2020. Since the summer of 2020, the digital assets community’s gaze has turned towards the possibilities of a decentralized financial ecosystem, DeFi applications have become highly popular and the prices of corresponding tokens have multiplied. Why is DeFi seen as having such great potential and what does this mean for regulated financial institutions? In the next three blogposts, I will outline these and other questions, show different perspectives and provide an outlook on the opportunities for regulated financial institutions in the context of DeFi. To get started, this blog post will outline the functionalities of DeFi applications, present selected use cases, and highlight opportunities and risks in this context.

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Open Banking and the (Eternal?) Crucial Question: Market-Driven or Regulated?

For a good four years now, open banking has been a topic considered relevant by the Swiss financial industry. This was triggered in particular by the EU regulation PSD2, which became valid for all member states in 2018 and was subsequently implemented. Various players have been working on this topic since then, but implementation is progressing slowly.For this reason, the State Secretariat for International Financial Matters (SIF) feels compelled to make a clear announcement. Regulation seems to be getting closer.

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Five Data Strategies for Banks

Ideally, a bank’s data strategy should be derived from its corporate strategy. Possible goals of the data strategy can be process optimization with associated cost reduction, better customer understanding (which can also lead to cost reduction or revenue optimization), or the development of new business models. The following blog post provides an overview of which data strategies for banks there are generally and which use cases can be observed in practice.

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Cross-Company Data Exchange in a Cross-Industry Trust Network – Added Value for Customers, Companies and Partners! 

More extensive business cases such as opening a bank account or an account with a FinTech require several minutes of time each for a low-value-added customer experience – wouldn’t it be great if you could skip all the tedious steps relating to data collection during onboarding and start right away with the advisory service or the effective satisfaction of your core needs? These and other questions have been considered for a little more than half a year as part of a workshop series of the OpenBankingProject.ch (OBP) and discussed together with more than 30 companies from the Swiss financial industry. This article is intended to provide an overview of the background, initial delivery results as well as the current state of knowledge of the efforts within the OpenBankingProject.ch workshop series.

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Synthetic Data – The Future of Data-Driven Financial Services?

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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Gaia-X – A Revolution for the Financial Industry?

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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Towards a Framework for Understanding the Potentials of Tokenized Assets

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.

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Classification of Blockchain-Based Applications: A Conceptualization from a User Perspective

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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An Analytics Marketplace for SMEs

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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Open Banking White Paper – The Future of Collaboration in Corporate Banking

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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Open Banking Summit 2020 – Opportunities and Implementation Scenarios for Open Banking in Switzerland

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 Convergence of Distributed Ledger Technology and Artificial Intelligence Exemplified by the Lending Process (Part 1)

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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