In the age of AI, RBI doubles down on relationship banking
Artificial intelligence is changing how corporate and institutional clients interact with their banks. At Raiffeisen Bank International (RBI), Sabine Zucker, Head of Group Transaction Banking, and Elitza Kavrakova, Group Head Institutional Clients, explain why AI will enhance rather than replace relationship banking.
AI is rapidly changing banking. What are the biggest challenges and opportunities for clients and financial institutions?
Elitza Kavrakova: The biggest challenge is no longer whether organizations should adopt AI, but how they can do so responsibly and effectively. Financial institutions and corporates need to strike the right balance between innovation, regulatory compliance, cybersecurity and data governance.
At the same time, there is a genuine risk of becoming overly reliant on AI. While AI can significantly improve efficiency and decision-making, it should remain a co-pilot rather than an autopilot. Human judgment, accountability and critical thinking remain indispensable, especially in areas such as risk management and compliance.
Despite rapid digitalization, clients continue to value human interaction. Technology can enhance the client experience, but trust-based relationships remain at the core of banking.
How are client expectations changing and what does this mean for relationship banking?
Elitza Kavrakova: Client expectations have shifted significantly. Clients increasingly expect real-time access to information, faster execution, greater transparency and more proactive services from their banking partners.
As a result, clients are looking for more than information. They increasingly expect actionable intelligence that helps them anticipate developments and make better decisions. This is where AI has the potential to fundamentally change the role of relationship managers. AI will elevate relationship managers from information providers to strategic advisors. Rather than spending time gathering and processing information, they can focus on interpreting AI-generated insights, understanding client needs and helping clients navigate increasingly complex environments.
At RBI, we already use data-driven insights to support client engagement. AI can help relationship managers better understand client needs, identify opportunities earlier and provide more relevant solutions. This can include next-best-offer recommendations, data-driven pricing decisions, more effective meeting preparation and stronger post-sales engagement.
AI can also help identify relevant developments earlier, enabling relationship managers to engage with clients in a more informed and meaningful way.
Technology can strengthen client interactions, but trusted personal relationships, experience and human judgment remain irreplaceable.
How is technology transforming transaction banking and client connectivity?
Sabine Zucker: Customers are increasingly interested in a smooth and seamless interaction with the bank. This requires strong connectivity between banking platforms and clients' treasury or bookkeeping systems. APIs play a major role here, as they provide the basis for straight-through connectivity and support increasingly tailored client experiences.Looking ahead, the integration of AI into transaction banking could drive a new wave of innovation, including smarter liquidity management, predictive cash-flow forecasting and advanced fraud prevention capabilities.Client expectations are also changing rapidly. A few years ago, instant payments were only rarely used by corporates. Today, companies increasingly expect instant information on their liquidity position at any point in time, enabling them to make faster and better-informed decisions.
What will define successful AI adoption in the years ahead?
Sabine Zucker: AI will for sure change the way we work. The challenge will be to use all the benefits AI offers while at the same time preventing potential negative consequences it may have.
Overall, I think AI can support us a lot in our daily work. However, checking results with the human brain may not be a disadvantage.
Elitza Kavrakova: Successful adoption requires robust governance frameworks that address transparency, accountability, data protection, model validation and human oversight. AI can support decision-making, but responsibility must always remain with people.
We are particularly excited about developments in generative AI, predictive analytics, ecosystem banking through APIs and the continued digitization of trade finance and cross-border transactions. These technologies are helping institutions move from reactive to predictive service models.
Ultimately, the institutions that will be most successful are those that combine innovation with trust, responsible leadership and a strong understanding of clients' needs. The winning model will be the combination of human judgment, trusted relationships and AI-powered intelligence.