Artificial Intelligence is revolutionizing treasury operations, but where does it truly add value? This session explores key optimization areas, the integration of AI-driven analytics with Microsoft Qlib, and real-world use cases demonstrating its potential in liquidity management, forecasting, and risk mitigation. In this session you will learn,

• How to identify treasury processes that benefit most from AI-driven automation and optimization
• How to leverage Microsoft Qlib for advanced forecasting, scenario analysis, and decision-making
• How to apply AI in real-world use cases, from cash flow predictions to fraud detection and FX risk management.

• Which treasury functions can AI optimize today, and where is human expertise still essential?
• How can Microsoft Qlib enhance cash flow forecasting and liquidity management?
• What are the biggest challenges in implementing AI-driven treasury solutions?
• How can AI improve risk management, from fraud detection to FX hedging?
• What are the key success factors for integrating AI into an existing treasury management system (TMS)?