Banks are sitting on one of their most valuable, yet least exploited resources: transaction data. Millions of bookings provide daily insights into consumer behaviour, life situations, risks and opportunities — but mostly in a form that is not directly usable. “Transaction Data Enrichment” describes the process of systematically refining this raw data: through AI, classification
How intelligent AI agents are transforming the front-end of banks – and why the new book “Digital Dimensions in the Financial Industry” is being published at exactly the right time. The financial sector is in the midst of a decisive transformation. The new book “Digital Dimensions in the Financial Industry” from Frankfurt School confirms this
The First 90 Days Decide Whether Banking Succeeds or Stalls In modern retail banking, the first 90 days determine whether a new card or account customer becomes an active, profitable user — or disappears into inactivity after initial excitement. This phase, known internationally as EMOB – Early Months on Books, is central to lifecycle frameworks
Within the Customer Lifecycle Management (CLM) of banks, the question is no longer what is communicated – but how. While many institutions still rely heavily on email as their primary customer communication channel, real-world behavior tells a very different story: each generation interacts with banks through different channels, with different expectations and levels of tolerance.
In many markets, the credit card business has reached a point of saturation. The number of issued cards continues to grow, but the real growth potential no longer lies in issuing more cards. It lies in how actively those cards are used. For credit card issuers, competitive advantage today is no longer defined by reach,
Banks have invested heavily in digital transformation. Yet one of the most powerful personalization assets is often underutilized: transaction data. Every bank account transaction, every credit card payment and every merchant category provides direct insight into customer behavior, needs and life situations. From Raw Transactions to Transaction Intelligence Transaction data is not just accounting information.
Artificial Intelligence is no longer a future promise in banking — it is ready for production. Yet many institutions remain stuck between pilot projects and real-world deployment. The reason is rarely technology. It is security, compliance, and governance. While GenAI and Agentic AI offer massive efficiency gains, banks hesitate because the regulatory framework feels unclear
Hardly any boardroom discussion in banking today takes place without mentioning artificial intelligence. Expectations are high: more efficient processes, better customer experiences, smarter decisions. But how far along are banks really? And where does the gap between ambition and execution lie? A look at the recent Cofinpro study “AI in Banking” reveals: the potential is
By Michael Altendorf, CEO of Acceleraid. Gartner has released its list of the top 10 technology trends for 2026. For many organizations, terms such as Multi-agent Systems or Confidential Computing still sound like a distant future. At Acceleraid, they are already part of our daily operations. Gartner’s predictions can be structured into three strategic pillars:
The Core Problem: Good Ideas Fail Due to Integration Challenges Banks and insurers have long recognized where AI can add value: relieving customer service, enhancing digital channels, increasing conversion rates, and streamlining internal processes. What often holds back these initiatives isn't the will to act – it's the assumption that every AI solution requires a