CDAO Financial Services & Insurance UK 2024 schedule

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

• • Transparent Decision-Making: Model explainability ensures that financial services professionals can understand and trust AI-driven decisions, which is crucial for regulatory compliance and informed decision-making. 
  • Building Stakeholder Confidence: Effectively communicating model insights and outcomes to stakeholders to facilitate better integration of AI solutions within financial services operation 

• In the fast-paced world of investment banking and finance, maintaining accurate and reliable transaction records is paramount. GiQ revolutionizes transaction reconciliation by leveraging neural graph data structures and fuzzy matching algorithms to associate and verify transaction data across multiple systems seamlessly, reducing manual intervention and ensuring comprehensive data integrity. Join us to discover how GiQ’s innovative approach empowers financial institutions to uphold trust and integrity in their transaction records 

• Redefining data leadership in finance, fostering a data-driven culture that fuels better decision-making and risk management 

• Identifying the most impactful uses of AI in financial services, leading to increased efficiency and innovation  

• Providing strategies for the CDO to ensure the data infrastructure is robust and scalable for AI workloads, preventing bottlenecks  

• Achieving more accurate and effective AI models for financial institutions due to robust data quality measures.  

• Unlocking the true potential of AI in financial institutions, propelling them towards a sharper competitive edge in the market 

Panellists: 

Janthana Kaenprakhamroy, CEO  and Founder, Tapoly 

Dario Morelli, Head of Advanced Analytics and AI, Admiral Group   

Bhushan Kokate,Data Analytics & AI Lead, MS Amlin

Moderated by: FiveTran

• Conquering top risks and compliance challenges with innovative solutions 

• Leveraging modern data architectures for speed, reduced risk, and data-driven decisions 

• Unlocking superior legal entity, counterparty risk management, data quality, and lineage management 

• Tips for building predictive analytics and data-driven insights through democratization and sharing 

In one of the EU's most traditional retail banks, we embarked on a transformative journey to enable 60 teams to collaborate effectively on data products using a data mesh architecture. This session provides a detailed, step-by-step guide on how we implemented self-service data capabilities in a governed environment, overcoming significant roadblocks and resistance to change. Learn how we disrupted the status quo, empowered teams to innovate, and fostered a culture of data-driven collaboration. Join us to explore practical strategies, lessons learned, and insights that can be applied to drive similar transformations in your organization. 

Key takeaways: 

• Practical Implementation: Understand the step-by-step process of implementing a data mesh architecture in a traditional retail bank, enabling seamless cross-team collaboration on data products. 

• Overcoming Resistance: Gain insights into strategies for overcoming roadblocks and resistance to innovation within established, conservative organizations. 

• Governed Innovation: Learn how to balance governance with self-service capabilities to empower teams while maintaining data security and compliance. 

Joanne Biggadike, Head of Data Governance , Schroders

Chris Butler, Head of Data and Analytics HSBC UK 

Boosting forecasting accuracy for better decision-making on everything from loan approvals to market trends  

• Identifying potential risks with greater precision, ultimately strengthening your risk management strategies 

• Unlocking new products and services for to enhance customer experience and open the door for innovative banking solutions  

• Spotting fraudulent activity early on, virtual assistants resolving customer inquiries swiftly, and automated reports ensuring seamless compliance 

• Harnessing the Power of NLG (Natural Language Generation) for increased efficiency and accuracy  

• • Creating a navigable map of data  
  • Employing AI as a powerful weapon to sniff out fraudsters faster and more effectively 
  • Combatting deep fakes 
  • Creating data ready organisation to support Economic Crime Prevention 
  • AI designs for an organisation's tech and data landscape 
  • Walking the thin line : Ethics, hallucinations, and consent 
  • Catching global changes impact early 
  • Accelerating with data : Speed to market and meeting regulations. 

• • AI-Driven Personalization: How AI enhances personalized customer services in finance. 
  • Risk and Compliance: Using AI for real-time risk management and regulatory compliance. 
  • Operational Efficiency: AI’s role in automating processes and boosting efficiency. 
  • Innovation: AI-driven development of new financial products and services. 

Moderated by: Michael Down, Head of Financial Services Technology - NEO4J

Dario Morelli, Head of Advanced Analytics and AI, Admiral Group   

Euan Wielewski, Head of Applied AI - NATWEST 

• Utilize OpenAI models for automation, data analysis, and risk assessment to enhance underwriting accuracy, claims processing efficiency, and CX 

• Establish clear guidelines, industry standards, and regulatory frameworks to ensure transparency, fairness, and accountability in using OpenAI's generative AI models in insurance 

• Collaborate with regulators and providers to integrate the technology effectively, manage risk, and maintain customer trust  

Moderated by: Kjell Carlsson, Head of AI Strategy – DOMINO DATA LAB

Janthana Kaenprakhamroy, CEO and Founder- Tapoly 

Martin Lidl, Chief Data Officer - Admiral Pioneer 

• How a diverse workforce, with a wider range of experiences and perspectives fosters more creativity and innovation the financial institutions  

• Bridging the skills gap with training and initiatives that equip the workforce adequately to thrive alongside AI  

• Exploring potential challenges and strategies for ensuring ethical and responsible AI integration  

• Understanding the evolving role of human expertise in the financial sector, emphasizing strategic analysis, client relationship management and creative problem-solving  

• Exploring strategies for building a strong employer brand and fostering a culture of continuous learning and innovation  

Speakers:  

Gael Decoudu, Director Data Science, Chetwood FInancial Limited 

Philip Treleavan, Professor and Director of UK Financial Computing Centre, University College London

• Addressing regulatory requirements as the UK Financial Conduct Authority (FCA) emphasizes fairness and explainability in AI use 

• Implementing data analytics solutions to streamline regulatory reporting and ensure compliance with evolving financial regulations 

• Securing access to customer data, enabling personalized financial services and fostering collaboration across institutions with open banking  

• Utilizing Open Banking to securely access customer data with their consent, enabling personalized financial services and collaboration across institutions 

• Migrating data analytics infrastructure to the cloud for scalability, accessibility, and cost optimization 

• • Strategic Cloud Adoption: The importance of aligning cloud strategies with business goals to ensure that the transition to the cloud supports broader organizational objectives and maximizes AI potential. 
  • Data Management and Integration: Preparing for AI success involves robust data management practices, including integrating diverse data sources and ensuring data quality and accessibility  
  • Skill Development and Change Management: Investing in upskilling teams and managing the cultural shift towards cloud-based operations are critical for leveraging AI effectively 

• • Leveraging Predictive Analytics: Look at the use of AI-driven predictive analytics to enhance risk assessment and underwriting processes in the insurance industry   
  • Fraud Detection and Prevention: Consider AI's role in identifying and mitigating fraudulent activities through advanced pattern recognition and anomaly detection  
  • Customer Experience Enhancement: Learn how AI can transform customer interactions by providing personalized recommendations, automating claims processing, and improving overall service efficiency 

• • • How is the widespread availability of AI tools transforming the insurance industry, making advanced technology accessible to all professionals? 
    • How can AI tools help insurance professionals manage their daily tasks more efficiently, regardless of their technical expertise? 
    • How can AI improve collaboration and communication within insurance teams, especially in remote or hybrid work environments? 
    • How do provide people the skills to safely use these new technologies? 
    • What are the upcoming trends of AI in our industry? 
• Tom Clay, Chief Data Scientist, Covea Insurance 
• Seeta Halder, Head of Data Insights, Nottingham Building Society 

• Deploying machine learning systems in the finance sector presents a significant challenge due to the risk of inadequate performance once operational, which could lead to substantial losses or missed profits. This issue currently hampers the widespread use of potentially valuable ML-based algorithms and systems, many of which, as presently realised, are likely to suffer from suboptimal performance and inherent vulnerabilities. 

• The root of this problem lies in the absence of effective methods and tools for assuring the robustness of machine learning models such as decision trees and neural networks. While traditional software validation processes are well-established, ensuring the reliability of AI systems has so far remained problematic. 

• In this presentation, we will explore newly developed techniques in the realm of formal robustness verification for neural networks and decision trees. These innovative methods not only automatically detect vulnerabilities but also enhance the robustness of these systems, offering much-needed assurance in fields as critical as aviation. 

• Despite the unique complexities and challenges of the financial sector, adopting these robustness verification methods could be crucial. This approach would facilitate the rigorous validation of ML-based systems prior to their deployment, enhancing their reliability and performance in real-world financial environments, as well as enabling the rollout of innovative and effective solutions. 

• In what ways has data and analytics transformation improved operational efficiency within organizations? 

• How have data-driven tools and processes affected employee productivity? 

• How can improvements in customer experience and satisfaction be quantified following a data and analytics overhaul? 

• How do Financial Services and Insurance companies align their analytics initiatives with strategic business objectives to maximize long-term value? 

• With data democratization on the rise, how can financial services and insurance companies equip all employees, not just data specialists, to make data-driven decisions? How does this affect talent management and development? 

• As companies break down data silos through unification, what role does talent play in integrating diverse data sources to deliver actionable insights? 

• How can financial services and insurance organizations optimize their talent strategies to leverage edge analytics and real-time data for swift decision-making and operational adjustments?