AI in Banking: Building Compliant and Safe Enterprise AI at Scale

The financial industry is at a turning point with AI adoption. While large language models (LLMs) like OpenAI’s ChatGPT have been around for years, many banks are still navigating the complexities of implementation. From data security and regulatory compliance to model accuracy and operational efficiency, banks face unique challenges in leveraging AI effectively. Join our expert panel as we explore how financial institutions can safely and strategically integrate AI into their operations. We’ll discuss: - The Role of Open-Source Models: How open AI frameworks can help banks address security and compliance concerns while fostering innovation. - Generative AI vs. Traditional AI: Key differences, business applications, and how banks can extract value from generative models. - Synthetic Data for AI Training: How privacy-enhancing synthetic data can improve model performance while reducing the risk of breaches. - Regulatory Landscape: The impact of the EU AI Act and other regulations on AI governance and risk management. - AI Agents in Banking: The potential of Agentic AI to automate operations while ensuring compliance and safety. We’ll also explore advanced techniques like Retrieval-Augmented Generation (RAG), which enhances LLMs by grounding responses in authoritative data, improving accuracy, and reducing hallucinations. Additionally, we’ll discuss strategies for scaling AI initiatives beyond pilot projects, drawing parallels to cloud adoption in banking. With the rise of foundational models like Meta’s Llama and Google’s Gemini, financial institutions must balance general-purpose AI with specialized, task-specific models. The key to success lies in effective deployment and governance, ensuring AI-driven innovation aligns with regulatory and operational needs.