AI transformation in banking, applied to onboarding and servicing, means moving customer processes from self-service, in which the customer performs the data entry a teller once handled, to zero-service, in which the bank’s AI proactively assembles what it needs... View
AI-led automation has proved its value across banking. Customer service teams resolve enquiries faster, lending operations process applications more efficiently, fraud teams identify suspicious activity sooner, and payments move with less manual intervention. Yet the... View
AI in financial services covers every use of artificial intelligence across banking, lending, wealth management, and capital markets, but the institutions that convert AI investment into business results are the ones that treat it as a core system change, not a... View
**For most of the industry’s digital history, trust in a banking system was something certified once, at a launch review or a control sign-off, and left largely unexamined thereafter. That model no longer holds for AI. Trust in an AI-first bank is tested... View
There is a regulatory reality about AI-driven credit decisions that is consistently underestimated by institutions deploying them at scale: automating the lending decision does not reduce the explanation obligation. In most regulatory frameworks, it increases the... 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 […]