Upon completion of this course, participants will be able to:

• Evaluate the limitations of traditional time-series models (e.g., ARIMA) and identify where **AI/ML** can enhance cash forecasting accuracy.
• Apply advanced **Machine Learning models** (e.g., Random Forests, Gradient Boosting) to incorporate a wide range of external factors (exogenous variables).
• Build and utilize **Deep Learning models** (e.g., LSTMs, Neural Networks) to capture complex, non-linear patterns in cash demand.
• Design a comprehensive **data pipeline and feature engineering strategy** for high-frequency transactional and economic data inputs.
• Establish a robust **Model Risk Governance (MRG)** framework for validating, deploying, and continuously monitoring AI/ML forecasting models.
• Develop a protocol for measuring, attributing, and mitigating common AI/ML challenges (e.g., concept drift, overfitting, lack of interpretability).
• Formulate a strategic roadmap for integrating AI-driven forecasts into the final currency ordering and issuance decisions.
• Analyze the impact of different forecasting horizons (daily, weekly, annual) on model selection and required accuracy.

• Quantitative Analysts and Data Scientists in Cash Management and Treasury.
• Cash Forecasting and Inventory Control Managers.
• Currency Policy and Strategic Planning Specialists.
• IT and Analytics Infrastructure Managers.
• Model Risk Governance and Validation Teams.
• Economists focused on Payment Systems and Money Demand.

• Hands-on Python/Jupyter Notebook Workshops on LSTM and ML Time-Series Forecasting
• Model Risk Governance Workshop: Developing a Validation Checklist for an AI Forecast
• Group Activities on Feature Engineering and Data Pipeline Design for Cash Data
• Expert Lectures on MLOps and Model Monitoring in a Financial Institution
• Case Studies on Improving Forecast Accuracy and Reducing Inventory Costs with AI
• Individual Exercises on Measuring Forecast Error and Attribution Analysis

Unit 1: Foundations and the AI Forecasting Imperative

• Review of traditional cash forecasting models and their limitations (seasonality, non-linearity).
• Introduction to AI/ML concepts: supervised vs. unsupervised learning for time-series data.
• The value proposition of AI: speed, accuracy, and the ability to integrate diverse data sources.
• Defining the data requirements: historical demand, macroeconomic variables, ATM/CRS transaction data.
• The concept of **Model Risk** and the need for rigorous governance in high-stakes forecasting.

Unit 2: Advanced ML for Exogenous Variables

• Applying ML regression models (e.g., Lasso, XGBoost) to predict cash demand based on exogenous factors.
• Mastering **Feature Engineering**: transforming raw data into high-value predictors (e.g., holiday effects, weather, news sentiment).
• Techniques for managing multicollinearity and selecting the optimal set of input variables.
• Model selection, hyperparameter tuning, and cross-validation techniques for ML time-series.
• Interpreting ML model outputs (e.g., feature importance) to understand demand drivers.

Unit 3: Deep Learning and Sequence Modeling

• Fundamentals of **Recurrent Neural Networks (RNNs)** and **Long Short-Term Memory (LSTMs)** for sequential data.
• Designing deep learning architectures for multi-step ahead, granular cash demand forecasting.
• Managing the challenges of data preparation, computational intensity, and training stability for DL models.
• Utilizing external libraries (e.g., Prophet, sktime) for rapid prototyping and deployment.
• Advanced techniques for capturing complex, non-linear dependencies in the cash cycle.

Unit 4: Model Risk Governance and Deployment

• Establishing a formal **Model Risk Governance (MRG)** framework for AI forecasting models (validation, documentation).
• Protocols for **continuous monitoring** of model performance, data drift, and concept drift in production.
• Strategies for addressing the "black box" problem: using Explainable AI (XAI) for interpretability.
• Designing the MLOps pipeline for automated deployment, retraining, and version control.
• Protocols for auditability and regulatory compliance of AI-driven forecasts.

Unit 5: Strategic Integration and Continuous Improvement

• Integrating AI forecasts into the final issuance and ordering decision-making process.
• Analyzing and mitigating the financial impact of forecast errors (overs and shorts).
• Developing a continuous feedback loop between operational outcomes and model refinement.
• The role of AI in forecasting the impact of new policy measures (e.g., new banknote series, CBDC).
• Future trends: using AI for anomaly detection and real-time intervention in the cash cycle.