With rapid shifts in market dynamics, treasurers are turning to AI for intelligent portfolio rebalancing and process automation. This course focuses on AI-driven techniques for liquidity optimization, asset allocation, and automated trade execution. Participants will learn how to apply machine learning and intelligent agents to ensure their portfolios are agile, compliant, and optimized for risk-adjusted returns in real-time.
- AI-based portfolio optimization techniques for treasury environments
- Real-time asset allocation using machine learning models
- Event-driven rebalancing with market intelligence integration
- Application of reinforcement learning to portfolio rebalancing
- Trade automation through APIs and intelligent agents
- Liquidity monitoring and cash visibility enhancement via AI
- Risk-adjusted return maximization using predictive analytics
- Treasury compliance automation with rule-based AI systems
- Monitoring asset correlation and volatility shifts using AI
- Construct and rebalance treasury portfolios using AI models
- Automate treasury trades based on machine learning triggers
- Monitor liquidity and rebalance based on risk-return thresholds
- Apply reinforcement learning to develop adaptive strategies
- Deploy rule-based AI tools for compliance in portfolio governance
- Forecast volatility shifts and their impact on portfolio strategy
- Integrate AI with TMS for seamless decision-execution workflow
- Assess the performance of AI-driven vs traditional rebalancing
- Corporate treasury professionals
- Portfolio and liquidity managers
- Treasury operations specialists
- Finance transformation consultants
- Digital strategy officers in banking/finance
- Risk and compliance analysts
- Practical case-based sessions with international benchmarks
- Live AI portfolio simulations with market conditions
- Demonstration of AI APIs for trade execution
- Group rebalancing scenario challenges
- Toolkits for deploying AI in TMS workflows
- Interactive workshops with hands-on labs
- Treasury portfolio structures and rebalancing basics
- Introduction to machine learning models in asset allocation
- Comparison between rule-based and AI-driven strategies
- Case study: traditional vs AI-enabled portfolio adjustment
- Building ML models to predict risk-adjusted returns
- Correlation analysis using AI tools
- Rebalancing portfolios based on predictive analytics
- Scenario lab: shifting liquidity and hedging needs
- Overview of RL and Q-learning in portfolio decisioning
- Simulating agent-based rebalancing logic
- Lab: reinforcement learning to time portfolio changes
- Performance tracking and adaptive strategy refinement
- APIs and smart contracts for automated trade execution
- Treasury compliance rules integrated into AI logic
- Demo: Auto-trade trigger based on volatility spike
- Dashboard for audit-trail & regulatory transparency
- Linking AI rebalancing tools with TMS platforms
- Alert system for deviation and drift from portfolio targets
- End-to-end simulation: execution + compliance pipeline
- Wrap-up: ROI from automation & post-implementation review
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.