Why This Article Matters This is the synthesis article – the one that steps back from individual institutional failure patterns and examines what the banking industry’s relationship with AI looks like as a whole, from the outside. The 80%/12% gap is not a... View
The Definitive Framework for Banking Technology Leaders Trusted AI in banking is not a feature, a compliance posture, or a product category. It is an engineering discipline and the competitive divide that will define the next decade of financial services The Defining... View
Why This Article Matters Most conversations about AI in banking start with the wrong question. They ask: ‘Are we using AI?’ The answer is almost universally yes. The question that defines competitive outcomes is different: ‘Can we trust what our AI... View
For the last few years, banks globally have been in a race to automate customer interactions. The rapid rise of generative AI accelerated this push, with a clear objective: faster service, lower operating costs, and greater personalisation at scale. We saw... View
Let me tell you about a conversation I had last month with a Chief Risk Officer at a mid-sized bank. His team had just deployed an AI-powered loan decisioning system. Six months of development, millions in investment, cutting-edge machine learning models. It worked... 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 […]