Consumers are beginning to imagine AI doing more than comparing financial products. Experian has published research, conducted for it by Forrester Consulting, in which 54% of surveyed consumers said they would be comfortable with an AI agent applying for credit on their behalf.

The survey covered 6,247 credit-active, digitally literate consumers across 13 EMEA and Asia-Pacific markets in July 2026. Eighty-two per cent said they trusted AI to compare loans across providers. But the autonomy numbers are more nuanced: 23% would allow an agent to act when pre-agreed rules were met, while only 5% said they would be comfortable giving it full autonomy.

That distinction matters. The findings are not evidence that consumers are ready to hand over autonomous control of their financial lives. They do suggest that a meaningful share of this digitally engaged sample is open to delegating defined parts of the lending journey to software.

Garbage In, Garbage Faster: Why Agentic AI Exposes Your Organisational Debt
If Agentic AI follows your documented processes, what happens when those processes don’t reflect reality? Most organisations assume AI will figure things out. Business Architect Laura Van Weegen argues the opposite: AI doesn’t create new problems — it removes your ability to ignore the ones that have existed forever and a day. Undocumented workflows, undefined decision ownership, and human workarounds masking broken systems all get amplified at machine speed. You’ll learn: • Why “garbage in, garbage faster” is the real Agentic AI risk • The critical difference between feeding AI data versus information • How process debt compounds the same way technical debt does • Why exception handling is the new decision design priority • What one conversation reveals more than most AI readiness assessments • How to build explainability in from day one Key topics: Agentic AI readiness • Information architecture • Process debt • Data vs information • Contextual blindness • Decision ownership • Explainability vs traceability • Semantic infrastructure • Exception handling • Organisational accountability • Workflow documentation • AI governance Essential viewing for CISOs, CIOs, CFOs, and Chief Legal Officers evaluating Agentic AI deployment — before the human safety net disappears.

For a bank, that changes the trust model. A conventional digital application is roughly person → identity → consent → application. Agentic finance adds another link: person → identity → agent → delegated authority → application. The institution may need evidence not only of who the customer is, but of what the agent was authorised to do, under which constraints and for how long.

Experian itself identifies identity verification, consent management, fraud prevention and explainability as critical requirements as agent-assisted customer journeys develop. It also reports that 75% of respondents would feel more comfortable using AI connected to a financial institution they already trust.

The next competitive question for banks may therefore be less about whether they launch an AI assistant and more about whether they can support secure transactions initiated by customer-side agents. If the agent can compare, apply and eventually negotiate, the bank needs a reliable way to distinguish legitimate delegation from fraud automation.


Conquered Your Data? - Now Combat Your AI
Souvik Choudhury, an AI and Data Governance Specialist at Fractal Analytics with a background spanning Infosys, HSBC and several startups, joins Stewart Tinson to unpack why data governance and AI governance can’t be treated as sequential problems, and why so many organisations discover the gap between them the hard way. Souvik argues that traditional data governance remains the foundation everything else is built on, and that AI agents amplify existing weaknesses rather than replacing the need for accountability, contextualisation and lineage. He walks through a real project example where an organisation believed it had solved data governance by using agents to generate column definitions, only to discover the definitions were pulled from generic internet knowledge rather than the organisation’s own policies, leaving a false sense of confidence behind a genuinely ungoverned dataset. The conversation covers where accountability actually sits when an autonomous agent makes a bad decision, why third-party models don’t dilute an organisation’s own responsibility for outcomes, and why Souvik pushes back on the idea that governance is an innovation-killing bureaucracy rather than the structural work that makes innovation possible in the first place. He also sets out a practical, staged approach to evaluating AI governance tooling rather than jumping straight to an enterprise platform, and offers a way to actually measure AI governance maturity using a weighted scoring model across multiple pillars. The discussion closes on an unexpected angle: the sustainability cost of AI infrastructure, and why Souvik believes environmental impact deserves a seat alongside profitability and productivity in any serious cost-benefit conversation about agentic AI.
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