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.
By the time warranty cost shows up in the P&L, the failure has been in the field for months. Joining claims, service reports, build records and supplier data turns warranty from an accounting line into an early-warning system — provided people keep the quality decisions.
In a hospital, a fluent but wrong answer is worse than no answer. The assistants worth deploying answer only from the record, show where every number came from, and leave the decision with a clinician or a named owner.
One wrong field on a shipping bill or a bill of entry can hold a container for days. Extraction, classification suggestions and pre-filing checks can catch most mismatches before submission — as long as a licensed broker still decides and files.
Brand, regulatory and competitor signals in India don't arrive in English alone. An LLM that scores every mention — sentiment, brand, region, impact — and joins it to sales in that state turns news into something a brand team can act on.
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.
Service desks measure SLAs after the fact. The same ticket data, read by a handful of well-chosen models, tells a team lead which tickets will breach while there is still time to act.
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.
The IndiaAI mission is expanding public AI capacity and the DPDP Act changes how personal data may be used. For AI that touches citizens, the governing rule is simple: agents prepare the work, a named official decides, and every step is on the record.
Real estate runs on money moving between buyers, contractors, lenders and regulated accounts. Agents can do much of the reading and reconciling, but the design has to start from what they must never do on their own — and from consent for every buyer's data.
When the operation breaks, recovery is still pieced together by phone and spreadsheet. Agents can assemble ranked options and rebook passengers within policy — but the duty manager must stay the one who decides.
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.
Hospitals do not need a separate rulebook for every AI tool. They need one framework: what each agent may do on its own, who owns it, what each role may see, and a record of every action.
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.
AI can take the re-keying out of underwriting and sharpen risk selection. In regulated lines it will be judged on whether every flag and recommendation can be explained, tested for fairness and overridden by an underwriter.