Treasurers now leverage AI to predict cash flows more accurately and optimize liquidity amidst volatile markets. This course blends machine learning forecasting models, real-time analytics, and agentic treasury tools to drive proactive treasury decision-making. Participants will learn to integrate AI with ERPs and banking systems, fortify liquidity buffer strategies, and apply best practices for transparent, efficient treasury management.
- AI-enhanced cash flow forecasting with time-series and predictive analytics
- Integration of real-time data from ERP and banking systems
- Liquidity optimization via machine learning and funding strategies
- Rolling forecasting and scenario simulation frameworks
- Explainable AI for treasury control and regulatory compliance
- Fraud detection and anomaly monitoring in payments
- Agentic AI in TMS for proactive treasury operations
- Dashboard design: liquidity KPIs, variance, and accuracy metrics
- Ethical and governance considerations in treasury AI
- Deploy AI and ML models to stream and forecast cash flows
- Interface treasury models with real-time ERP and bank data
- Optimize liquidity funding and working capital strategies
- Build rolling forecasts and simulate adverse scenarios
- Interpret AI decisions to comply with treasury governance
- Detect potential fraud and anomalies in real-time
- Leverage agent-led TMS for strategic treasury operations
- Design dashboards to monitor treasury KPIs and forecast accuracy
- Corporate treasurers and liquidity managers
- Finance analysts and risk officers
- ERP and treasury system implementers
- FP&A professionals & treasury technologists
- Compliance and internal audit personnel
- CFOs and finance leadership teams
- Keynotes on global treasury AI trends
- Demo: real-time cash forecasting tools (e.g., Nilus, Kyriba)
- Hands‑on labs: ML model building with time-series data
- Case simulations: AI-driven liquidity optimization
- Dashboard creation: KPI variance and predictive accuracy
- Peer group reviews & strategic roadmap exercises
- Traditional vs AI-driven forecasting: evolution & benefits
- Predictive analytics methods: ARIMA, ML, deep learning
- Lab: build your first cash forecast model in Python/R
- Walkthrough: historic vs rolling vs scenario forecasts
- Connecting ERPs and bank portals for streaming data
- Feature engineering: interest rates, AR/AP, FX
- Lab: ingest live data into forecast models
- Case exercise: treasury optimization using Nilus/Kyriba
- Liquidity buffer optimization using predictive AI
- Scenario modelling: stress-test cash positions
- Lab: build Monte Carlo & 'what-if' liquidity models
- Discussion: AI for fee optimization and working capital
- Explainable AI: transparent treasury decisioning
- Detecting anomalies and fraud in payment flows
- Lab: anomaly detection using Autoencoders
- Demo: agentic TMS handling liquidity alerts
- Treasury dashboards: real-time KPIs and variance analysis
- Governance: audit trails, control frameworks
- Lab: build a Power BI/TMS cash forecast dashboard
- Final group presentations: AI-enhanced treasury roadmap
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.