CDAO Financial Services & Insurance UK 2026 schedule

During this 5-minute networking session, the aim of the game is to go and meet two people you don't already know.  Have fun! 

• Why many AI initiatives in financial services fail to move beyond pilot stage and the common organisational and data maturity barriers preventing enterprise scale.
• What separates AI leaders from laggards including differences in operating models, leadership alignment, and business ownership of AI initiatives.
• How governance is evolving from a control function into an AI enabler and what effective model risk and data governance looks like in AI-mature organisations.
• Where leading financial institutions are prioritising investment across data platforms, talent, operating models, and AI infrastructure to accelerate adoption.
• The critical foundations firms must fix first, whether that is data quality, architecture, skills, or business alignment, before AI can deliver measurable value.

• How the traditional CDO role is evolving in an AI-native financial services landscape. • Identifying the boundary between data leadership and AI leadership, and what overlaps require attention.
• New organisational expectations for CDAO/CAIO executives, including collaboration with business units, compliance, and technology teams.
• Key skills, mindset shifts, and leadership qualities required to succeed as a next-generation data and AI leader.
• Insights from early adopters who have successfully transformed their CDO role into a CAIO function.

• How UK FSI organisations are balancing innovation with regulatory scrutiny, including practical examples from retail banking, investment, and insurance sectors.
• Strategies for data leaders to manage AI risk while maintaining progress on digital transformation and AI adoption.
• Emerging governance models, frameworks, and board-level oversight structures that are proving effective in large-scale financial institutions.
• How boards are increasing expectations for accountability, explainability, and risk mitigation in AI deployment.
• Lessons learned from firms that have faced regulatory challenges or compliance audits around AI and data use.

Panelists:

Darren Goddard, Chief Data Officer – Blenheim

Simon Lewis-Jones, Chief Data Officer, Risk & Compliance – Standard Chartered Bank

Vinod Chawda, AI Governance Chief Strategy & Innovation Office – Deutsche Bank

Socrates Coudounaris, VP Head of Enterprise Risk Management, EMEA Region –Reinsurance Group of America

Vicente Carbonell, CIB Project Manager, Engineering UK & CE – BBVA

Meena Khan, Chief Strategy & Product Officer – Zindigi

• Where GenAI and agentic AI are delivering real, measurable value in banking operations today — beyond the pilots
• The infrastructure, data, and governance foundations needed before deploying AI agents at scale
• Managing risk and accountability when AI systems are making or influencing decisions
• Lessons from early implementations, what worked, what failed, and what teams wished they'd known sooner

Panellists:

Wayne Cavanough, Chief Information Officer– Jaja Finance

Yousra Aoudi, Vice President, Quantitative Analyst II – BNY

Manish Baphna, VP Engineering Manager / Java / Microservices – Citi Bank

Sharada Khanna, Global Lead Analytics and Commercialisation, Retail Banking Originations - HSBC

Elena Konovalova, Head of Data Analytics – Paysend

• Comparing approaches: which architecture is delivering tangible results in UK banks?
• Lessons from large-scale implementations: successes, pitfalls, and adoption challenges.
• Organisational readiness considerations: governance, team structure, and culture.
• Technology vs. culture: how to align data architecture strategy with operational and regulatory needs.

Facilitator roles: Available

Roxanne Howdle-Rowe, Managing Director, Group Head of Data & Analytics Office – British Business Bank

Elena Konovalova, Head of Data Analytics – Paysend

Tolu Awoyinfa, Managing Director – Argentil Group

• The strategic decision to migrate all data to AWS on an aggressive timeline — why now, and what drove the move away from on-prem infrastructure

• Building a partner ecosystem rather than going it alone.

• Rethinking data governance: putting ownership in the hands of the business rather than a central team, and what that means in practice

• Using AI as a pipeline accelerator from day one — how AI tooling is enabling clean, structured data rather than being bolted on later

• Honest lessons from a transformation still in progress — what's working, what wasn't anticipated, and what the road to 2027 looks like

• How insurers and MGAs are moving beyond AI experimentation to deploy solutions that are delivering measurable impact across the value chain
• Where AI is genuinely transforming underwriting decisions, risk selection, and pricing — and where human expertise remains non-negotiable
• The role of AI in claims: accelerating processing, reducing leakage, and improving fraud detection without sacrificing customer trust
• What good AI governance looks like in an insurance context: explainability, auditability, and keeping the regulator onside

Panellists: Available

Oksan Orhan, Chief Strategy, Transformation & Digital Officer – QNB Sigorta

Alan Strange, Head of Underwriting & Analytics – Sophro

Nikhil Chaudhari, AI Transformation Lead - Tokio Marine Kiln

Varalakshmi Venkatraman, Head of Data Engineering – Marshmallow

• How insurers are restructuring their data functions for modern analytics and AI initiatives.
• Operating model evolution: centralised, federated, or hybrid approaches.
• Balancing legacy systems with modern data platforms to achieve a unified customer view.
• Lessons from Lloyd’s and GI markets on prioritising value, compliance, and innovation.

 Socrates Coudounaris, VP Head of Enterprise Risk Management, EMEA Region – Reinsurance Group of America RGA  

• Why legacy core systems remain both the biggest barrier and the most underutilised asset in financial institutions' AI strategies
• How leading banks and insurers are extracting and delivering data from mainframe and on-prem systems to modern cloud and analytics platforms without disrupting critical operations
• Balancing the need for speed with risk, compliance, and operational continuity when modernising data infrastructure in regulated environments
• Building trusted, governed, AI-ready data pipelines from legacy sources — what good looks like and how to get there incrementally

• Turning regulatory mandates into strategic opportunities for data sharing and innovation.
• Open Finance use cases: enabling financial inclusion, better customer insights, and new revenue streams.
• Data sharing strategies: APIs, consent management, and security considerations.
• Leveraging ecosystems for competitive advantage while maintaining regulatory compliance.

• Why the shift from traditional machine learning to large language models demands a fundamentally different approach to data architecture and data foundations
• The talent challenge: what skills organisations actually need now versus what recruiters and the market think they need
• How to approach data chunking, preparation, and structuring so that LLMs can consume and process it effectively
• Managing hallucination risk in enterprise AI deployments — governance, validation, and keeping humans in the loop
  
  

• How the role of data and AI leaders is evolving as organisations move from data transformation into enterprise-wide AI adoption and cloud modernisation
• Leading through constant change: balancing delivery pressure, long-term strategy, and stakeholder expectations across complex financial institutions
• Building modern data organisations that combine technology, governance, people, and culture — not just platforms and tooling
• The leadership skills becoming most important in the next generation of data executives, including influence, adaptability, communication, and cross-functional collaboration
• Navigating leadership journeys in data and AI today, including visibility, mentorship, and creating more diverse leadership pipelines within financial services

Poojya Manjunath, (Chief Data and AI Office) Senior Data Lead – Lloyds Banking Group

Yousra Aoudi, Vice President, Quantitative Analyst II – BNY

Sally Bashuan, SVP Global Head of Data Governance – Federated Hermes

• A forward-looking debate on the governance, oversight, and accountability frameworks financial institutions need before deploying semi-autonomous AI systems. Where does human judgment stay in the loop, and where should it?

  • Where FS firms are safely testing agentic AI today and what safeguards they are putting in place before scaling.
  • Who ultimately owns AI-driven decisions, the CDO, CIO, risk function, or the business? How firms are structuring accountability models for AI agents.
  • Where financial institutions are drawing the line between full automation and human oversight, and how this varies across use cases like fraud, customer servicing, and operations.
  • The cultural, technical, and process changes organisations must make before trusting AI with higher levels of decision authority.

  Moderator: Mayank Bhundia, Partner, Financial Services Digital & Data SME – PA Consulting
  

  Sally Bashuan, SVP Global Head of Data Governance – Federated Hermes

  Cristina Gil Sevilla, Head of Client Group Insights & AI – Schroders

  Calum-Conejo Watt, Head of Data Governance & Quality - Lombard Odier Investment Managers

  Aditya Sharma, Head of Engineering, Data Platform & Analytics – TIDE

  John Hill, Head of Data Analytics, Automation & Systems – Aberdeen

   

• Why the CDO operating model is evolving as organisations shift from governance-focused data teams toward value creation, AI enablement, and business alignment.
• The rise of data products and how FS firms are introducing product ownership, SLAs, and internal customer thinking into their data functions.
• Centralised vs federated data ownership and how institutions are balancing domain accountability with enterprise governance and regulatory control.
• How leading firms are funding data teams and moving from cost-centre models toward value-driven investment tied to business outcomes.
• Designing data organisations for AI scale including the skills, structures, and collaboration models needed to support enterprise AI adoption

Panelists:

Lashan Bartholomuez, Vice President Design Authority Corporate Bank Data Services – Deutsche Bank

Meena Khan, Chief Strategy & Product Officer – Zindigi

Sally Bashuan, SVP Global Head of Data Governance – Federated Hermes

Aravinth Baskaran, Head of Data Science – Foresters Financial

• How leading FS organisations are quantifying ROI from data and AI investments.
• Moving from technical KPIs to business impact metrics executives care about.
• Practical frameworks for measuring value across revenue growth, efficiency, and risk reduction.
• Communicating data value in financial terms boards understand.
• Examples of successful monetisation and value-realisation strategies.
• How CDAOs can secure continued investment in difficult budget environments

An honest, reflective close on the human side of the CDAO role — the pressure, the pivots, and the mindset shifts required to lead data and AI transformation in one of the most regulated, scrutinised industries in the world.

• Managing the gap between board expectations and operational reality — and how to hold that tension without losing credibility
• Making high-stakes decisions under uncertainty in a fast-moving regulatory and technology landscape
• How to course-correct when AI strategies don't go to plan without losing stakeholder confidence
• What separates data and AI leaders who are thriving from those who are treading water