AI Agents Your Bank Can Trust | Banking Transformation Summit
Build AI Agents Your Bank Can Actually Trust | Banking Transformation Summit 2026
Banks lack a path from pilot to production to measurable value at scale. At the Banking Transformation Summit in London, Skan AI took the stage with Lloyds Banking Group from a leading European bank to unpack the real blocker, illustrated by a claims agent that was quick to build but needed deep refinement against real-world context before it was ready to serve customers at their most vulnerable moments.
Key insights
1. Probabilistic AI meets deterministic banking
AI works in probabilities. Banking runs on fixed rules, strict controls and zero tolerance for error. That structural mismatch is where most pilots stall.
2. The context graph bridges the gap
A context graph built from real execution, not from SOPs, shows agents how banking work actually happens, including every exception and control point, so they can operate within the rules.
3. Accountability shifts when AI does the work
Once agents execute real transactions, models of process ownership and governance have to evolve. The session lays out the governance model banking CIOs now need.
Trust your banking AI in production
Skan AI is privacy-first by design. It captures process patterns, not personal data, and operates entirely within your compliance perimeter, aligned with GDPR, FCA, AML and KYC requirements.
Get a tailored demo of building a context foundation your AI agents and regulators can trust.
Frequently asked questions
AI is probabilistic, while banking processes are deterministic and tightly controlled. Without precise context on how work actually runs, agents can't reliably meet the standards banking requires.
A context graph can serve as a model of how banking work happens, with context captured from real execution rather than documentation. Once built, this kind of context graph can give AI agents the rules, exceptions, and decision points they need to operate safely.
As agents take on real work, banks need clear ownership, continuous monitoring, and a full audit trail of agent decisions. Skan AI supports this with traceable, observed process context.
Skan AI captures process patterns rather than personal data and runs within the bank's compliance perimeter, in line with GDPR, FCA, AML, and KYC requirements.
