Blogpost

AI Usage in Europe: Why the Nordics Have the Edge

Germany is lagging behind the rest of the EU when it comes to AI adoption. Whilst Nordic countries are making consistent use of artificial intelligence, Germany often gets bogged down in pilot projects. In this article, you’ll find out why implementation is more important than strategy and how banks can catch up now.

23/04/26

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Artificial Intelligence, Digitalisation, Future Banking, Strategy

AI Usage in Europe: Why the Nordics Have the Edge

Contents

AI market surveillance: What BaFin is currently investigatingSince July 2026, BaFin has been responsible for AI market supervision in the financial sector. Many of these obligations already apply to banks and insurers. Where the authority is focusing its scrutiny and how institutions can prepare.

IAM as the foundation for the secure use of AIArtificial intelligence (AI) opens up new possibilities, but also places greater demands on governance, security and compliance. Why effective Identity & Access Management (IAM) is becoming the foundation for the trustworthy use of AI.

From AI pilot to contribution to results: How banks are embedding AI in their business models and creating tangible added valueFinancial institutions now have a growing list of AI pilot projects, chatbots and assistant solutions. Yet, at the same time, process lead times, capacity bottlenecks and the cost-to-income ratio often remain virtually unchanged. The cause is rarely a lack of ideas. What is missing is an operating model that brings together business value objectives, data, technology, governance, change management and accountability for results.

High-risk AI in banking: What the EU AI Act really means for credit institutionsCredit scoring, fraud detection, AML: AI has long been deeply embedded in banks’ core processes. The EU AI Act classifies many of these applications as high-risk AI and sets out specific obligations for banks as operators – with new deadlines introduced by the Digital Omnibus on AI.

Weathering a weak market phase: How Agentic Operations can transform natural staff turnover into a variable cost structureNEWS 02/2026Banks are currently facing a two-pronged challenge:: on the one hand, regulatory requirements are tying up investment budgets; on the other, there is a shortage of skilled staff across the board. Agentic Operations offers a solution that avoids drastic changes to the workforce, instead transforming natural staff turnover into a variable cost structure.

Sovereign AI in BankingNEWS 02/2026The topic of "sovereign AI infrastructure in banks" is taking on a new sense of urgency in light of the EU AI Act, DORA and the debate surrounding the US CLOUD Act. At the same time, open language models have reached a level of maturity that covers most typical banking use cases. This means that any decision to use US hyperscalers must increasingly be well justified.

Digital Omnibus on Artificial Intelligence: What banks need to knowThe political agreement on the Digital Omnibus on AI introduces new deadlines and simplifications, but does not exempt institutions from their obligations. What specific changes this brings for banks, what deadlines apply, and why now is the right time for structured AI governance.

After Claude Mythos: How banks can ensure their AI resilienceAI-powered cyberattacks in a matter of seconds, AI-assisted phishing and highly fragmented attack vectors: the threat landscape for financial institutions has changed fundamentally. The question is not whether security controls are in place, but whether they are still effective in the face of these new dynamics.

Claude Mythos in Banking: AI on the Way to Information SecurityA new AI model is currently causing a stir in the world of cybersecurity: Claude Mythos. What initially appears to be just another technological leap is actually quite significant – because, for the first time, it becomes clear how AI not only identifies vulnerabilities but can also potentially turn them into attacks of its own accord. For banks and financial services providers, this creates an immediate need for action. In this article, we analyse this development for you and show how you can set the right course today for greater security and resilience.

Innovative AI-Powered Document Processing in the Banking SectorBanks process thousands of documents every day – often still manually. Modern AI-powered document processing, using OCR, natural language processing and computer vision, transforms PDFs into structured data sets, thereby delivering speed, efficiency and audit assurance.

Artificial Intelligence in Treasury – from periodic financial reporting to a continuous management functionNEWS 01/2026Real-time payments, 24/7 availability of payment infrastructures, shorter settlement cycles such as T+1, volatile markets and new digital asset classes are forcing treasury functions to take on a new role. Treasury is evolving from a function that operates on a periodic basis into a permanent management body.

Artificial intelligence is regarded as a key technology for Europe’s competitiveness. However, whilst Germany is well positioned strategically, a look at Northern Europe reveals that it is not development that is crucial, but consistent application.¹

Between ambition and implementation

Germany has set itself ambitious goals. With its national AI strategy, updated in 2020, the country aims to establish itself as a leading hub for artificial intelligence and secure its industrial competitiveness in the long term. At the same time, comparative European data on digitalisation paints a more nuanced picture: overall, Germany often ranks only in the middle of the pack, whilst countries such as Denmark, Finland and Sweden are acting in a significantly more dynamic manner.^{2, 3}

The discrepancy between regulatory aspirations and operational implementation is thus increasingly becoming a decisive competitive factor.

AI adoption: the real game-changer

At the heart of this development lies a concept that is often underestimated: AI adoption. This refers not merely to the introduction of new technologies, but to their targeted and efficient integration into processes, business models and decision-making structures.⁴

Innovation therefore does not arise solely from the existence of technology, but only through its targeted and consistent application in everyday practice.

A similar picture emerges in industry. Nordic companies are making targeted use of AI to optimise production processes, predict maintenance cycles and manage supply chains more efficiently. Iterative approaches and rapid implementation are the norm.⁹

In contrast, Germany focuses heavily on technological refinement – for example, in quality assurance through image recognition. However, many projects remain at the pilot stage and are only slowly being rolled out into regular operations, rather than launching initial practical trials with MVPs.