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

• Evaluate the limitations of traditional econometric models for **intraday liquidity forecasting**.
• Apply Recurrent Neural Networks (RNNs) and **LSTMs** to model and predict high-frequency payment flows and liquidity demands.
• Utilize **Unsupervised Learning** techniques (e.g., clustering, autoencoders) for real-time anomaly detection in payment streams.
• Design a **Real-Time Data Pipeline** and feature engineering strategy for high-velocity payment data.
• Establish a **Model Risk Governance** framework for validating and monitoring AI-driven forecasting and detection models.
• Develop and implement strategies for proactive intervention based on AI-generated liquidity and anomaly signals.
• Understand the computational and infrastructural requirements for deploying real-time AI models (MLOps).
• Analyze the legal and ethical considerations of using AI for transaction monitoring and anomaly detection.

• Quantitative Analysts and Data Scientists in Financial Markets and Risk Departments.
• Liquidity Managers and Treasury Operations Staff at Central and Commercial Banks.
• Heads of Payments Systems Operations and Technology.
• Risk Management and Compliance Officers focused on Algorithmic Monitoring.
• IT Architects designing Data and Analytics Infrastructure.
• Internal Auditors focused on AI Model Validation.

• Hands-on Python/Jupyter Notebook Workshops on LSTM and Autoencoder Implementation
• Simulated Real-Time Anomaly Detection Exercises on Payment Flow Data
• Group Activities on Designing a Real-Time Data Pipeline Architecture
• Discussions on Model Risk Governance for AI and Ethical Monitoring
• Expert Lectures on MLOps and Cloud Deployment Strategies
• Individual Assignments on Feature Engineering for Liquidity Forecasting

Unit 1: The AI/ML Liquidity Forecasting Imperative

• Review of traditional liquidity forecasting models and their limitations with high-frequency data.
• Introduction to advanced time-series analysis: ARIMA, GARCH, and the need for non-linear models.
• Overview of Machine Learning (ML) techniques applicable to sequential financial data.
• Defining the features and data sources for intraday liquidity prediction (e.g., payment type, participant behavior).
• Challenges of **non-stationarity** and concept drift in payment flow modeling.

Unit 2: Deep Learning for Forecasting Accuracy

• Fundamentals of **Recurrent Neural Networks (RNNs)** and **Long Short-Term Memory (LSTMs)** for sequence prediction.
• Designing deep learning architectures for multi-step ahead liquidity forecasting.
• Hyperparameter tuning, validation, and managing the **overfitting** problem in time-series DL.
• Integrating exogenous variables (e.g., market events, OMO schedule) into the DL model.
• Performance attribution and interpretability of DL liquidity forecasts.

Unit 3: Unsupervised Anomaly Detection in Payments

• Conceptual understanding of **Anomaly Detection** and its application to payment systems.
• Implementing techniques like **Isolation Forest** and One-Class SVM to detect unusual transaction behavior.
• Using **Autoencoders** (a form of unsupervised deep learning) to identify deviations from normal flow patterns.
• Setting dynamic thresholds for anomaly alerting and managing false positives/negatives.
• Case studies on using AI to detect potential fraud, failed payments, or systemic liquidity hoarding.

Unit 4: Real-Time Data Pipeline and MLOps

• Designing a **Real-Time Data Ingestion** pipeline for high-velocity payment message data.
• Using streaming technologies (e.g., Apache Kafka) and cloud-native solutions for processing.
• **MLOps (Machine Learning Operations)** for automated deployment, retraining, and monitoring of AI models.
• The computational requirements (GPU/TPU) for running deep learning models in production.
• Data governance, quality control, and the ethics of real-time monitoring.

Unit 5: Governance and Policy Action

• Establishing a **Model Risk Governance (MRG)** framework for AI forecasting and detection models.
• Developing clear protocols for central bank intervention based on AI-generated liquidity stress signals.
• Legal and privacy considerations for the use of AI on granular payment data.
• Integrating AI alerts into the existing risk management and operational workflows.
• Translating complex AI model outputs into actionable policy recommendations for senior management.