The governance conversation around AI compliance in banking tends to operate at the wrong level of precision. It asks whether institutions have model validation frameworks, whether governance documentation exists, whether oversight processes were followed. These are... View
AI-first banking is the shift from using AI as a feature layered onto existing systems to embedding AI into a bank’s core systems, decisioning layers, and operations, so that intelligence, rather than automation alone, determines how the institution runs. For... View
There is a question worth asking of any banking technology leader who oversees operational AI governance: how many AI tools are in use across your back-office functions right now – not on the approved technology list, not through a formal deployment, not... View
Seven questions every banking CIO should be able to answer about their compliance AI estate – before the examiner asks them. There is a question that banking regulators increasingly ask when they examine an institution’s AI compliance programme. It is not... View
The efficiency case for AI-led automation in banking is compelling and well-documented. Fewer manual steps. Faster decisioning. Lower cost per transaction. The boards that approved these programmes did so on the basis of projections that were, in many cases, accurate... View
Regional banks are not behind on AI in software delivery. They are ahead. The real question is whether test automation moved with them. Most bank technology leaders have this backwards. The assumption, understandably, is that regional banks and credit unions are playing catch-up on AI, that megabanks with larger technology budgets are further along. The […]
Development moved faster this year. Most quality engineering functions did not move with it. Here is how to tell if yours is one of them. Ask most CIOs, CTOs, and Heads of Engineering at regional and challenger banks whether their development teams got faster this year, and the answer comes quickly: yes, obviously. Ask the […]
Banks increasingly consume AI through vendor APIs, SaaS platforms, cloud services, and embedded features. Proprietary architecture can limit visibility, but it does not transfer accountability. SR 26-2 states that model risk principles remain applicable to vendor products and calls for institution-specific understanding, validation, monitoring, and outcome analysis. A vendor model card is an input to […]