Event Report
Delving into effective strategies and tools for secure and compliant AI integration in banking, emphasising the importance of open-source models, the role of generative AI, the advantages of synthetic data, and the influence of regulatory frameworks.
Integrating AI in banking involves more than just adopting new technologies; it requires aligning these technologies with specific banking use cases to maximise benefits and mitigate risks related to data security and compliance. Open-source models facilitate this transformation by offering a collaborative platform for innovation and transparency, essential for building trust and ensuring the safe use of AI in banking.
The journey from proof of concept (POC) to production in AI and machine learning is often lengthy and complex. This extended timeline highlights the challenges organisations face in integrating AI into their operations. Embracing new tools and technologies and learning to utilise them effectively is crucial for overcoming these challenges and applying AI to day-to-day operations, leading to significant benefits in efficiency and innovation.
When looking at other markets, the United States has become a hub for tech giants, focusing on fostering innovation, while regions like Japan remain cautious, reflecting their unique regulatory landscapes and societal risk appetites. This diversity in regulatory approaches presents both opportunities and challenges for firms operating globally, necessitating a nuanced understanding of each market’s unique dynamics.
This webinar report summarises the discussion of a Finextra webinar, hosted with Red Hat, by a panel of industry experts. Discover:
How AI is enhancing innovation, efficiency, and security;
Synthetic data and regulatory impact;
What factors are holding organisations back from fully adoption AI-driven services;
Balancing innovation and regulation;
And more.
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