Treasury teams are increasingly adopting AI to refine risk analytics and execute dynamic hedging in volatile markets. This course equips treasurers with AI-driven methods—spanning sentiment-infused hedging, time-series forecasting, and reinforcement learning—to monitor exposures and adjust hedge strategies in real-time. Participants will combine machine learning insights with policy governance to create resilient hedging frameworks aligned with organizational risk appetites.
- AI-powered risk analytics for FX, interest rate, and commodity exposures
- Dynamic hedging strategies with LLM‑driven sentiment and news feeds
- Machine learning in time-series volatility forecasting & stress scenarios
- Reinforcement learning techniques for hedge timing and sizing
- Explainable AI and governance for compliance in hedging
- Real-time alerts: integrating AI outputs into TMS workflows
- Multi-asset risk analytics: cross-asset LLM monitoring
- Ethical considerations: systemic AI risk and human oversight
- Performance measurement: hedge effectiveness tracking and attribution
- Apply AI models to measure and forecast treasury risk exposures
- Design dynamic hedging strategies using NLP and sentiment signals
- Use machine learning to support time-series forecasting under volatility
- Implement reinforcement learning to optimize hedge timing and sizing
- Interpret model outputs for governance and audit transparency
- Automate real-time risk alerts within treasury management systems
- Perform cross-asset risk monitoring via LLMs and real-time data feeds
- Assess and mitigate ethical and systemic risks posed by AI in treasury
- Corporate treasurers and risk managers
- FX and derivatives analysts within treasury teams
- Enterprise risk governance specialists
- Treasury and TMS technology leads
- Finance transformation and digitalization professionals
- Compliance and audit officers in treasury environments
- Keynote insight sessions on global treasury AI and hedging trends
- Live demos: sentiment-based hedge instruments and ML models
- Hands-on labs: building hedging strategies with LSTM, RL, and Prophet
- Scenario stress-testing with real market data
- Explainable AI tutorials for hedge rationale and regulatory compliance
- Interactive group workshops: risk policy refinement and model governance
- Review of AI use in FX hedging and liquidity
- Risk analytics frameworks: volatility, sensitivity, exposure mapping
- Lab: exposure model with LSTM/ARIMA
- Case study: volatility forecasting and hedging effectiveness
- LLM-driven sentiment & news analytics for hedging
- Automated recommendations: hedge ratios & timing algorithms
- Lab: sentiment signal integration for FX decisioning
- Discussion: AI as decision support vs automated execution
- Reinforcement Learning framework introduction
- Lab: simulate RL agent for hedge entry timing
- Comparative analysis: RL vs static hedging
- Review: performance metrics and execution risk alignment
- Regulatory transparency: leveraging explainable AI
- Lab: generating explanations for ML hedge decisions
- Integration: AI alerts within TMS workflow
- Workshop: defining boundaries for human oversight
- Cross-asset risk monitoring with LLM tools
- Systemic risk & ethical triggers in automated systems
- Lab: integrated hedge performance analytics dashboard
- Group exercise: policy roadmap for AI-driven risk frameworks
Group & Corporate Discounts: Available for companies enrolling multiple participants to help maximize ROI. Individual Discounts: Offered to self-sponsored participants who pay in full and upfront. Registration Process: Corporate nominations must go through the client’s HR or Training department. Self-nominations must be prepaid via the “payment by self” option. Confirmation: All registrations are subject to DIXONTECH’s approval and seat availability. Refunds: Provided in case of course cancellation or no seat availability. Tax Responsibility: Clients are responsible for any local taxes in their country.