Managing End Point Storage in Hybrid Data Strategies for Financial Services - with Yonas Yohannes of Oracle
Feb 4, 2025
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Yonas Yohannes, CTO of FinTech and FIS at Oracle, dives into the pivotal role of endpoint storage in enhancing AI functions in financial services. He demystifies AI's true potential beyond the buzz, shedding light on its infrastructure impact. Yonas discusses the necessity for transparency and trust in AI technologies, emphasizing bias mitigation. He also addresses the complexities of managing hybrid data strategies and the increasing focus on accountable data governance, crucial for maintaining regulatory compliance and integrity in financial institutions.
The podcast emphasizes the critical need for transparency and interpretability in AI to build trust and ensure regulatory compliance in financial services.
Yonas discusses the strategic importance of endpoint storage and hybrid data strategies for leveraging AI effectively within financial institutions.
Deep dives
The Shift from Hype to Real Applications of AI
The evolving role of artificial intelligence (AI) in various industries, particularly financial services, has begun to emphasize concrete applications over the previously dominating hype. Generative AI is identified as having immense potential to enhance problem-solving abilities across sectors, with notable advancements in banking and insurance. Specific use cases for AI include personalized account onboarding, loan origination, fraud detection, and improved automation of complex tasks. This emphasis on practical benefits indicates a shift from initial experimentation towards more serious integration and deployment of AI technologies within enterprises.
Transparency and Trust in AI Solutions
For AI solutions to gain traction, particularly in the financial sector, it is essential that they operate transparently and provide interpretability to users. The 'E-I-E' principle—encompassing transparency, interpretability, and explainability—was highlighted as crucial for ensuring that clients trust AI-driven products. The need for transparent algorithms stems from the potential consequences of biases imprinted during data training, which have led to significant legal challenges for companies. As organizations move towards implementing AI in public-facing applications, addressing these challenges is vital for maintaining customer faith and compliance with regulatory standards.
Infrastructure and Data Management Strategies
The discussion on AI integration highlights the need for robust infrastructure, particularly with respect to endpoint storage and hybrid strategies. Financial leaders are encouraged to prioritize data sovereignty, governance, and clear control measures over the data lifecycle to mitigate risks while leveraging AI solutions. A trend towards using hybrid models that combine proprietary and synthetic data was noted, particularly for reducing bias and safeguarding against legal repercussions. Financial institutions are increasingly signaling a focus on developing their own large language models (LLMs) to maintain control and ensure the integrity of their data management practices.
Today’s guest is Yonas Yohannes, CTO of FinTech and FIS at Oracle. An accomplished executive and author, Yonas joins us on today’s podcast to explain the evolving role of endpoint storage for driving new AI capabilities at the edge. He breaks down AI’s true value beyond the marketing hype, and its broader impact on infrastructure across industries, with a special focus on financial services. Throughout the episode, Yonas addresses the real challenges businesses face in adopting AI while ensuring transparency and avoiding regulatory risk. To access Emerj’s frameworks for AI readiness, ROI, and strategy, visit Emerj Plus at emerj.com/p1.
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