Pål Krogdahl: Generative AI in Banking – A Strategic Path Forward
At Samlink Advisory Services, we see generative AI as an unprecedented opportunity for banks. Banks are no strangers to technology shifts, but generative AI brings a unique combination of potential and complexity that makes it stand apart. Our recent white paper, Generative AI in Banking: A Must for Compliance and Growth, dives deep into this topic, but I want to offer a more conversational take on what generative AI really means for banking strategy.
A Fork in the Road for Banking
Today, banks find themselves at a crossroads: they’re under increasing pressure from regulatory changes that demand accountability, security, and transparency, while also facing a competitive push from non-traditional players capitalizing on those very same regulations. Meanwhile, customers are expecting seamless, personalized experiences at every turn. Generative AI, with its ability to generate text, code, and even support us in decision-making based on patterns it learns, has the potential to address these pressures head-on. But banks need to approach implementing generative AI with a clear plan to use the technology in a way that can benefit the business.
At Samlink, we believe in order to use generative AI to the full potential, banks need a comprehensive strategy. Generative AI can reshape operations, compliance, and customer experience, but only if adopted thoughtfully. And that’s the lens through which we work with our customers: identifying opportunities and risks, and guiding them toward a structured, sustainable implementation.
Making Compliance a Strength, Not Just a Requirement
One of the important questions banks face with generative AI is: how can we manage regulatory requirements, while also achieving efficiency and innovation. In our experience, this question should be reframed. Generative AI isn’t just a tool to help meet compliance; it’s a chance to transform compliance into a strategic advantage.
For instance, regulatory reporting, risk assessments, and fraud detection are all areas where generative AI can not only simplify processes but also improve precision and speed. By automating repetitive reporting tasks or generating real-time fraud insights, generative AI frees up employee’s time to focus on higher-value work, turning compliance from a responsibility into a source of operational efficiency. And with Europe’s AI Act and related regulation adding further complexity to the regulatory landscape, adopting generative AI in a structured way could be a proactive move that positions banks to better manage future compliance shifts.
But compliance in banking isn’t only about technology. A successful generative AI adoption must start with a clear framework to manage risk, data quality, and transparency. This is where Samlink’s guidance comes in: helping banks categorize applications by risk, assess the right LLM models for their needs, and build transparency into their systems from the start.
The Choice Between Private and Public LLM
The choice between private and public large language models (LLMs) is another pivotal decision. Public models are powerful and cost-effective but may raise additional concerns about data privacy and control. Private models offer security and customization for banking-specific needs not on a same security level but are more resource-intensive to build and maintain.
In our experience, the right answer often lies in a balanced approach. Many banks we work with use a hybrid model, tapping into public LLMs for, for example low-sensitivity applications and reserving private LLMs for critical, data-sensitive tasks. This lets them benefit from the flexibility and cost savings of public models without impacting security where it matters most.
Structuring Generative AI for the Long Haul
Banks know that implementing generative AI isn’t a one-off project—it’s a long-term commitment that touches almost every part of the organization. Success requires more than just technology; it demands a cultural and operational shift. At Samlink, we take a methodical approach: helping banks start with manageable pilot projects, measure ROI early, and gradually scale to broader use cases.
Our framework includes setting measurable goals, developing cross-functional teams to manage integration, and establishing clear metrics to track progress. A focus on change management is critical— generative AI can only deliver value if it’s effectively adopted across the organization. We encourage our clients to think about generative AI as a journey, starting small and scaling up with lessons learned along the way.
Moving Forward with Confidence
Generative AI is reshaping the banking sector, and banks that move now to adopt it strategically will be well-positioned for the future. Banks need to have a comprehensive plan, which is an imperative in such a regulated and risk-sensitive industry. At Samlink Advisory Services, we see our role as guiding banks through this new terrain, helping them turn complex technology and compliance challenges into a structured, effective generative AI strategy.
Generative AI is here to stay, and with a thoughtful and strategic approach, it may become a cornerstone of sustainable growth and resilience in the banking sector.