AI in Finance Summit schedule

• Trends in responsible AI adoption across UK banks and fintech's 

• Bridging AI innovation with regulatory expectations (FCA, PRA, EU AI Act) 

• Strategic priorities for 2025: trust, governance, agility 

As financial institutions race to innovate with AI, they face a delicate dance: how to stay bold without overstepping the line. With regulators like the FCA, PRA, and the EU sharpening their focus, 2025 will demand a new level of accountability, agility, and alignment.  

• What do regulators really expect from AI leaders — and where’s the line between risk and reward? 

• How can firms scale AI innovation without getting tangled in governance gridlock? 

• Strategies for embedding explainability, fairness, and resilience into AI systems from day one 

• Can trust become your competitive edge in a market defined by scrutiny? 

• How can we move from vanity metrics to true business value? 

• What ROI frameworks are helping teams justify spend across AI use cases? 

• A practical toolkit for focusing your AI efforts where they count most. 

• Real-world examples of NLP in compliance monitoring, customer intelligence, and operational risk 

• How firms are integrating NLP into enterprise data pipelines and decisioning systems 

• Best practices for ensuring accuracy, explainability, and governance in NLP deployments 

• Lessons learned in scaling NLP from pilot projects to production environments 

• How financial firms are designing intelligent customer interactions that feel human, relevant, and secure 

• The trade-offs between automation, personalization, and ethical considerations in high-stakes contexts 

• Practical approaches to deploying real-time AI decisioning while maintaining transparency, fairness, and control

• Which monitoring and validation tools deliver compliance confidence? 

• How do risk, compliance, and data science collaborate in practice? 

• Takeaways on building audit-ready AI systems that evolve over time. 

• How can firms audit ESG models to ensure accountability and avoid greenwashing? 

• What AI tools are enabling meaningful analysis of unstructured ESG data? 

• Explore how finance can deliver both performance and purpose. 

• What causes AI initiatives to stall — and how can teams move beyond pilot purgatory? 

• How do successful firms balance innovation speed with risk appetite? 

• Get first-hand insights on navigating the production journey with confidence. 

• How can adaptive AI models stay ahead of evolving fraud patterns? 

• What trade-offs exist between detection speed and explainability? 

• Actionable approaches to AI model governance in transaction monitoring. 

• What are the latest architectural strategies for high-performance AI? 

• How can financial firms avoid infrastructure bottlenecks as AI scales? 

• Learn how cloud-native MLOps pipelines are enabling more agile innovation. 

As AI capabilities advance, financial institutions are moving beyond predictive models into agentic systems—autonomous agents that can interact, decide, and act. From intelligent copilots supporting analysts to AI agents executing operational tasks in the background, this shift is reshaping efficiency, compliance, and customer engagement. 

• Early use cases of agentic AI in trading, operations, and customer service 

• Risk boundaries and governance frameworks for autonomous systems 

• The role of human oversight, explainability, and accountability in agentic design 

• How teams are integrating these tools with existing workflows 

Since synthetic data is no longer a workaround and it is becoming a strategic asset, financial firms are using it to overcome privacy barriers, reduce bias, and boost model resilience.  

• When synthetic data outperforms real data — and why it matters.  

• Use cases in fraud detection, compliance, and fairness testing. 

• How to deploy synthetic data at scale without compromising trust?