There is a pattern appearing across banking AI customer service deployments with enough consistency that it deserves a name. Call it the FCR plateau. It works like this: an institution deploys AI agent assist or an AI-driven customer service platform. First Call... View
There is a question most banking technology leaders have not been asked directly about their fraud detection AI. Not ‘is the model performing?’ – that one gets asked constantly, with accuracy metrics produced in response. The question that matters... View
AI is changing the economics of regulatory reporting. What once required days of manual data aggregation, validation, reconciliation, review, and submission can now be accelerated through intelligent automation. For banks, the appeal is obvious: faster reporting... View
AI is changing how banks evaluate credit. Applications can be assessed faster, more consistently, and at far greater scale than traditional manual underwriting allowed. For CIOs and credit transformation leaders, this creates a compelling opportunity: faster... View
For years, AML monitoring in banks followed a relatively familiar logic. A rule was defined, a transaction was screened, and an alert was generated when that rule was triggered. The explanation was embedded in the operation itself. A payment crossed a threshold. A... 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 […]