Clara Durodié is an internationally recognized AI strategist, keynote speaker, and author specializing in the governance, ethics, and strategic adoption of artificial intelligence in financial services. With over 25 years in finance and fintech, she has advised the World Economic Forum, IEEE, parliaments, and global banks on how to integrate AI responsibly while driving profitability.
As author of Decoding AI in Financial Services the first C-level guide to AI adoption she translates complex technical landscapes into actionable boardroom strategies. Clara is frequently invited to lecture at Oxford, MIT, and London Business School, and she chairs the UK Non-Executive Directors committee on AI best practices.
Her insights bridge technology, compliance, and geopolitics, empowering leaders to innovate responsibly while safeguarding trust and resilience.
Clara Durodié is a technology strategist specializing in the business and governance of artificial intelligence (AI) in financial services.
She is internationally recognized for her expertise, advising Boards of leading financial institutions, think-tanks, governments, and academia. She also mentors AI startups on governance, funding and growth strategy.
She is consistently named on numerous global lists of leading women in fintech, and was the Chair of the Non-Executive Directors Boards’ special committee on Best Practice for AI Adoption (UK) and a member of IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems (USA).
Clara is a frequent presenter at leading conferences around the globe, including SIBOS, Money 2020, Innovate Finance Global Summit, AI World and at London Business School where she addressed an audience from leading universities around the world such as Harvard, Stanford, MIT and Cambridge.
She has been invited as guest lecturer by leading universities like the University of Oxford’s Executive MBA. London Business School and Massachusetts Institute of Technology. Clara was also invited to teach at the Oxford Artificial Intelligence course, organised by the University of Oxford.
Clara Durodie is the author of the highly acclaimed Decoding AI in Financial Services – Business Implications for Boards and Professionals the first guidebook on AI in financial services for C-level, Boards and decision makers. The book’s Japanese translation will be published in Japan in 2021. Clara also writes DecodingAI® Newsletter, a popular weekly digital publication which examines the business and governance of AI in financial services.
Clara Durodie is a member of the Chartered Institute for Securities and Investment (UK), has a Certificate in Investment Management (UK) and holds a Master’s degree from the University of Oxford.
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Clara Durodié explains how AI builds trust in financial services through personalization, governance, and practical applications that drive both customer confidence and business growth.
Clara Durodié opens by positioning herself as both a practitioner and entrepreneur, bridging asset management experience with AI strategy. She frames trust as the foundation of financial services, built on reliability, consistency, transparency, and competence. AI, she argues, can strengthen trust through personalization at scale—delivering services that meet individual client needs while maintaining efficiency and accuracy.
She contrasts general AI (still theoretical) with narrow AI, the practical tools already transforming finance, such as fraud detection, cybersecurity, automated claims processing, and chatbots. Clara highlights quick wins where deep learning detects suspicious ATM activity or generates insurance quotes from photos.
She emphasizes that AI is already embedded across customer service, investment management, and risk monitoring, and adoption must be deliberate. Common mistakes include fragmented strategies, uninformed boards, and poor process design. She stresses the need for trusted advisors and robust governance to avoid costly errors.
Her key conclusions: AI is not a fad but a structural shift essential for business survival; algorithms require ethical oversight; and boards must embrace AI to secure long-term competitiveness.
15:22 – Introduction: Clara’s background in finance, neuroscience, and AI
18:05 – Trust defined: reliability, consistency, transparency, competence
20:30 – AI basics: general vs. narrow AI explained simply
23:10 – Case studies: personalization at scale (Ant Financial, quick wins with deep learning)
27:00 – Practical AI use cases in finance: chatbots, fraud detection, natural language generation
30:15 – Adoption framework: educate boards, assess, roadmap, implement with long-term vision
34:40 – Common mistakes: lack of strategy, poor vendor choices, uninformed boards, bad processes
38:10 – Final takeaways: AI as new alchemy for growth, ethics essential, boards must lead adoption
A fast, plain-English panel where Clara Durodié leads three experts through bullish and bearish AI predictions, practical wins, trust, data strategy, and governance.
Clara Durodié welcomes the audience and positions the session as a practical debate on real applications of AI in financial services. Each panelist shares one bullish prediction and one skeptical take. Andrew argues for AI-powered financial fitness that unifies scattered personal finance data and for employee superpowers through internal copilots that surface policy and regulatory guidance in real time. He is skeptical that robots will replace most jobs soon and reframes futurism as shaping preferred outcomes rather than predicting destiny.
Natalie focuses on customer service, noting voice recognition accuracy and the harder challenge of intent understanding. She expects narrow, domain-specific AI to transform service and insights discovery across big data. She stresses human-in-the-loop design, clear problem statements, and cross-functional data collection that includes data engineers, scientists, and business owners.
Kanak highlights deployed bank use cases that generate economic value, including digital KYC, behavioral fraud detection, risk profiling, and NLP-driven customer messaging. He is cautious on fully direct-to-consumer robo-advice at scale due to trust, acquisition costs, and untested black swan behavior.
In the moderated debate, Clara drives discussion on building trust using reliability, consistency, transparency, and competence. The group tackles open banking incentives, data quality, and enterprise content architecture. They share real cases of insider and market abuse detection and agree that explainability, audit, and bias testing are essential to meet regulatory expectations. On talent, they advocate socializing innovation, reskilling, and framing AI as augmentation. Clara closes by underscoring personalized service at scale as a first-time opportunity for finance and invites continued dialogue on practical deployments.
14:10 Debate begins: aligning bank incentives with customer interests in open banking.
16:00 Clara anchors the four trust pillars for financial services.
17:00 Data strategy: quality, ownership, and cross-functional collection practices.
19:00 Case examples: insider trading and fraud detection with human-in-the-loop workflows.
22:00 Governance and regulation: explainability, bias testing, and model audits.
24:30 Talent and culture: socialize innovation, reskill, and emphasize augmentation.
27:30 Audience questions and live poll instructions.
30:00 Panel challenges and rebuttals on customer centricity and personalization.
34:00 Closing insights: personalization at scale as a durable advantage.
39:30 Voting results, next-session promos, and wrap.
A: Both — practical governance for product teams and policy-level guidance for leaders.
A: Legal, compliance, AI leads, and senior executives.
A: Current AI use case summaries help tailor recommendations.
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