AI for Treasury Cash Flow Forecasting & Liquidity Optimization

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.

TRAINING TOPICS

• 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

BY THE END OF THIS TRAINING COURSE, DELEGATES WILL BE ABLE TO

• 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

TRAINING IS TAILORED TO

• 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

TRAINING METHODOLOGY

• 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

Day One:

FOUNDATIONS OF AI FOR CASH FLOW FORECASTING

• 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

Day Two:

REAL‑TIME DATA INTEGRATION & LIQUIDITY MODELLING

• 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

Day Three:

OPTIMIZATION & SCENARIO ANALYSIS

• 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

Day Four:

EXPLAINABILITY, FRAUD DETECTION & AI GUIDED TMS

• Explainable AI: transparent treasury decisioning
• Detecting anomalies and fraud in payment flows
• Lab: anomaly detection using Autoencoders
• Demo: agentic TMS handling liquidity alerts

Day Five:

DASHBOARDING, GOVERNANCE & FUTURE TRENDS

• 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