Archives: Agenda

• Turning analytics into frontline actions
• Integration with CRM and workflow tools
• Behavioural nudges and next best action
• Adoption challenges in sales teams

• What an AI native banking product looks like
• Moving from feature layers to embedded intelligence
• Product lifecycle in an AI environment
• Where traditional product design breaks

• Precision and recall trade-offs
• Investigator experience and explainability
• Governance and audit trail
• Measuring operational impact

• Classifying use cases and defining high risk in practice
• Documentation and monitoring that survive audit
• Third party AI risk and contractual obligations
• Translating regulation into operating controls

• Maintenance and model updates
• Governance and compliance overhead
• Talent and organisational costs
• Vendor lock-in risks

• Validation for probabilistic systems
• Robustness and bias testing
• Monitoring and re validation cadence
• Aligning traditional model governance and GenAI

• Use case selection and kill criteria
• Integration with legacy systems
• Human oversight and escalation paths
• Measured impact and post launch governance

As AI begins to act on behalf of humans, we explore how customer models, distribution and control of the interface are fundamentally changing. Machine customers and synthetic users: who owns the relationship?

• Rise of agent-mediated interactions and delegated decision-making
• Whether banks retain or lose control of the customer interface
• Detecting human versus machine behaviour in financial journeys
• Implications for product design, distribution and monetisation

We examine how firms are translating principles into enforceable controls, and where governance may be slowing adoption or shaping competitive advantage

• Defining and operationalising AI risk appetite
• Embedding controls at design time, build time and run time
• Trade-offs between speed, value and control
• What regulators expect and where firms are over-engineering