Banks have always stored more intelligence than their systems could use. Every transaction record, payment description, compliance note, customer document, manual override, and explanation field contains signals about intent, context, risk, and behaviour. But for most... View
APIs changed core banking for the better. They helped banks break away from monolithic constraints, expose reusable services, and integrate digital channels with systems of record. For many institutions, API-led modernization became the practical foundation of digital... View
AI-driven banking decisions will not be trusted simply because they are faster, smarter, or more automated. They will be trusted when the institution can prove how the decision was made, what data it acted on, which validations were applied, and where the system found... View
The structural shift from retrospective compliance to compliant-by-design AI is not a philosophical preference. It is an engineering decision with direct regulatory consequences. Most AI governance in banking today operates after the fact. A model is trained,... View
For years, core banking modernization has been framed as a race from batch to real-time. The logic is clear. Real-time systems respond faster, support time-sensitive decisions, and help banks move away from the latency of legacy processing cycles. But for the AI-first... 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 […]