Archives: Agenda

In a landscape where the expectations for Generative AI are rapidly evolving, this session will delve into the critical role Graph technology plays in advancing Retrieval-Augmented Generation (RAG). As recognized by Gartner and other leading analysts, Graphs are now pivotal in enhancing the accuracy and reliability of Generative AI.
Attendees will learn how the integration of Graph technology with RAG not only addresses the inherent limitations of traditional models but also establishes a solid foundation for developing enterprise-grade Generative AI solutions that are precise, trustworthy, and capable of meeting the demands of today’s data-driven world in highly regulated industries.

• Uncovering how the government’s approach to AI assurance and risk management impacts business decision and operations
• Aligning government responsible AI initiatives and streamlining AI frameworks to enhance performance
• Examining implementations of new risk management principles in Finance
• Ensuring AI governance aligns with regulatory commitments

To maximise the benefits of AI, business leaders must ensure investments are well-placed to drive value and competitive differentiation. Given its budgetary, security, and ethical implications, AI has rapidly become a board-level priority. It’s essential to reposition AI from being solely an IT responsibility to a collaborative effort across the entire organisation.

This session will explore the successful business outcomes that a unified strategy around AI adoption can deliver for organisations, particularly how to accelerate their AI journeys from proof of concept to production while also reducing risk.

• Join SAS and Nationwide Building Society in exploring transformative potential of Generative Adversarial Networks (GANs) and synthetic data in revolutionising risk analytics.
• Collaborating uses GANs to enhance personal loan model accuracy by augmenting default cases with synthetic data.
• How do these technologies boost model performance through a case study comparing outcomes with and without synthetic data; highlighting realistic improvements in risk analytics; and helping to discover the future of risk with generative AI.

• Evaluating challenges in enhancing APIs and ChatGPT in finance
• Discussing measures to manage data risks to protect customers?
• Investigating compensation claims and the scope to improve CX platforms

• Investigating opportunities and encouraging women working in AI
• How can we improve an imbalance in diversity and inclusion within Finance?
• Building from the past; how can we learn from others to present greater opportunities to women in AI?
• Shaping innovation and culture in AI for a better future

As AI takes centre stage, the discourse surrounding it often falls prey to oversimplification, portraying it as a singular entity. However, AI is a multifaceted array of distinct capabilities, ranging from automation to personalisation and beyond. By unpacking AI and exploring the individual capabilities behind it, we are able to better exploit it in the process of design.

In this presentation we will share our AI-Capability-Design-Pack which we developed to identify, assess and articulate design opportunities along any given customer journey. Our framework helps speed the process of identifying where AI can improve a customer journey as well as surface unexpected opportunities.

•GenAI use cases with the largest impact in financial institutions
•GenAI adoption path for financial institutions
•GenAI technologies: Software and Hardware
•Conducting PoC for GenAI technologies