AI in banking is changing more than customer service, fraud detection, and decisioning. It is also changing how banking software is conceived, built, and released. Requirements can be refined faster. Code can be generated sooner. Delivery teams can push more changes... View
Banking software delivery has become too fast and interconnected for quality to depend on manual validation alone. Agile, DevOps, and AI-assisted development have compressed release cycles, while the systems underneath those releases still span products, business... View
Banking software delivery has already moved beyond the pace that traditional quality engineering was designed to support. Agile, DevOps, and AI-assisted development are compressing release cycles, while banking systems themselves remain highly interconnected. In that... View
Banking technology is moving faster than the testing models built to protect it. Agile delivery, DevOps practices, and AI-assisted coding have compressed release cycles, while the systems behind those releases remain deeply interconnected. A seemingly small change in... View
Most banks can describe their transformation strategy clearly. Far fewer have a structured, repeatable way to assess whether a specific program is actually ready to execute it which means readiness gets judged informally, inconsistently, and often only after a program... 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 […]