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
In the murky world of AI on Kubernetes, shadows and light converge to reveal opportunities and vulnerabilities. Explore how to attack, monitor, and defend AI systems on Kubernetes from an attacker’s perspective, and discover what the community is building and repurposing to secure the next generation of technologies.
By understanding supply chain vulnerabilities unique to AI on Kubernetes, we can better secure our AI assets while harnessing the agility and scalability of cloud native systems. This talk provides actionable insights to enhance security measures in the AI lifecycle on Kubernetes, as we:
•Examine layers of risk between data collection and model inference
•Analyze the attack vectors associated with model training data within Kubernetes
•Conduct threat modeling specific to machine learning topologies on Kubernetes
•Propose cloud-native solutions to enhance data integrity and model security
