Increasing efficiency in Swiss wealth management: This article highlights how open banking and the standardization of data and calculations are laying the foundation for innovative portfolio management infrastructures—and why this foundation is essential for the successful use of artificial intelligence. An outlook on the future of Switzerland as a financial center.
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The financial IT system of tomorrow is modular, open, and networked. It enables customer-centric service bundles and embedded finance within ecosystems, leverages data and AI broadly, and ensures compliance automatically. The core remains as a critical piece of infrastructure, but in a slimmer and more invisible form. Those who think “service-first” today, orchestrate real-time data, and embrace DevOps operations are laying the groundwork for scalable business models in a connected financial world of tomorrow.
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Artificial Intelligence offers enormous transformation potential along the entire mortgage value chain. Whether in property search, application processing, property valuation, or credit decisions, AI can not only accelerate and streamline processes but also improve them qualitatively.
What matters is a responsible approach to these technologies. Explainable models, robust data quality, and clear regulatory frameworks form the foundation. In addition, the principle of the human in the loop plays a central role, since in critical phases, human judgment remains indispensable.
Those who manage to combine technological innovation with trust, transparency, and customer value can help shape the mortgage world of the future.
I look forward to continuing the discussion and to developing concrete solutions together with partners.
From the next major LLM release such as ChatGPT 5 to autonomous agents, everyone is talking about artificial intelligence (AI). However, the expected benefits often cannot be fully materialized in practice. In many companies, data and AI initiatives do not make it past the pilot phase, while others fail completely (Estrada 2025; Haefner et al. 2023) .An important reason is that the current enthusiasm often focuses too much on technologies and ignores fundamental business problems.
Ultimately, the goal of any data and AI initiative should be to create strategic business value that pro-vides companies a competitive advantage (Grover et al. 2018). This requires a shift in perspective to-wards the fundamental data assets and the exploitation of their inherent potential, regardless of the analysis technologies used, such as business analytics, traditional or generative AI, following the max-im: AI as a means and data value creation as an end.
In this blog post, Nick Kakuschke introduces the research area “Value Creation from Data and AI” at the Competence Center Future Financial Services, where we develop applicable tools that support compa-nies to realize strategic value from data and AI.
The first version of our AI Radar for the DACH region from fall 2024 clearly showed how intensively banks are already using artificial intelligence (LINK). Based on publicly available information, we analyzed the largest banks in the DACH region to determine which AI use cases they have already implemented and systematically structured them according to various criteria.
But how has reality changed since then? Stefanie Auge-Dickhut describes our current AI Radar survey, in which we conducted qualitative interviews with partner banks and IT providers of the Competence Center Future Financial Services to identify deeper insights, experiences and new strategic challenges in dealing with AI in the financial industry.
Everyone is talking about artificial intelligence (AI) and generative AI (GenAI) in particular. Beyond the hype, executives have agreed for some time that data and its analysis (such as through AI) play a key role in the transformation of entire business models (Gartner, 2018) and are seen as an in-vestment focus for companies worldwide (IBM, 2023) . However, to date, many companies have struggled to successfully implement their data and AI activities due to a variety of associated chal-lenges. Due to the complexity of the perspectives to be considered, companies are constantly faced with the challenge of identifying the specific use cases that are best suited to them and promise the most value potential, whether classic AI, GenAI or data sales.
Accordingly, one research focus of the Competence Center Future Financial Services (CC FFS) is to provide companies with guidance on how to create value from data and AI. This blog post is dedi-cated to the decision factors that support the choice of the right data and AI use cases and presents excerpts from a scientific paper that was written as part of the CC FFS research (see Kakuschke et al., 2025) .
The financial industry is beginning to undergo a profound transformation through the use of AI. In particular, the use of generative AI opens up the possibility of creating new content such as texts, images, videos or music. In autumn 2024, we started building an AI radar that systematically analyzes use cases of banks in German-speaking countries. This blog post offers an overview of use cases and maturity levels in the banking sector and provides exciting insights into the status quo.
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