69 points of view from our industry teams — filter by industry or topic, or search. Figures in each article are sourced or labelled.
Generative AI assistants made individual bankers faster. Agents that execute whole workflows under human supervision are what move the cost base — and they need a different operating model to do it safely.
The open-source record shows where AI in financial services is really being built: agent frameworks and finance reference agents, data connectors, and a thin but important layer of governance. Stars are loud; the signals that matter for banks are quieter.
Open models, agent frameworks and MCP connectors are now part of almost every bank’s AI stack. The licence, supply-chain and regulatory questions they raise are answerable — but only if intake is designed for AI, not just for software.
Banks spend heavily on financial-crime compliance and still catch only a sliver of illicit flows. Agentic AI offers a different shape for the work — squads of narrow agents assembling evidence, with investigators making the call.
Generative AI is shortening credit memos and cash-flow data is widening who can be scored. The rules on explaining and testing those decisions are shifting on both sides of the Atlantic, but the obligations have not gone away.
The rulebook is moving in two directions at once — EU deadlines deferred, US model-risk guidance rewritten, Asian supervisors naming AI agents explicitly. Banks that build one control framework for models and agents will not need to rebuild it for each regulator.
Virtual assistants now handle a large share of routine banking conversations. The public record shows that the programmes that last are those that measure outcomes, not deflection, and make it easy to reach a person.
The Bank of England, the ECB and the EBA are all reshaping how they collect data from banks. The banks that benefit will be the ones that pair common data definitions with traceable lineage and AI that interprets, reconciles and explains their returns.