Banks have spent years automating compliance. Transaction monitoring systems process huge volumes of activity. Credit decisioning engines assess applications faster. Regulatory reporting tools aggregate data, validate fields, and reduce manual effort. On paper, this... View
AI for AML compliance works by giving transaction monitoring models access to the unstructured context, including narratives, notes, and counterparty details, that structured data fields have always excluded, so the model can distinguish transactions that look... View
AI-enabled core banking modernization is the shift from systems of record to systems of intelligence: banking cores that can generate customer- and transaction-specific data access, validation logic, and decisioning on demand, instead of relying only on predefined... View
Generative AI in banking replaces static, human-built analytical models with a dynamic orchestration engine capable of real-time conceptualization, letting a bank identify and act on a customer opportunity the moment it emerges instead of waiting for the next... View
AI risk in banking is the set of exposures created when a model’s decisions can’t be explained, reproduced, or defended under regulatory examination, ranging from unfair credit outcomes to compliance failures that surface only after a customer or examiner... 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 […]