AI for Monetary Policy Forecasting & Inflation Modeling

This immersive 5day program equips policy analysts and central bank teams with stateofthe‑art AI tools—LLMs, ensemble ML, and nowcasting methods—for inflation modeling and rate‑path forecasting. Delegates will learn to harness unstructured data, generative AI, and neural forecasting models to refine policy signals, monitor volatility, and improve macroeconomic scenario planning in today’s high‑uncertainty global environment.

TRAINING TOPICS

• LLMbased inflation nowcasting and short‑term forecasting
• Ensemble methods (random forests, XGBoost) vs classical econometrics
• Transformer neural nets for inflation modeling
• Integrating highfrequency and alternative data (payments, satellite, text sentiment)
• Agent‑based and DSGE models augmented with reinforcement learning
• Generative AI workflows in central banking (e.g., BIS Project Spectrum)
• Monitoring polycrisis with bigdata LLM classifiers
• Governance, explainability, and AI model risk frameworks

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

• Implement LLMbased nowcasting pipelines using realtime data
• Develop ensemble and transformer AI models for inflation forecasting
• Incorporate alternative data (payment flows, satellite, text) into forecasts
• Embed agents and RL mechanisms in DSGE or policy simulation frameworks
• Use AIdriven macroeconomic dashboards for volatility and risk monitoring
• Evaluate model performance, bias, explainability, and central bank governance standards
• Design AIassisted policy scenarios accommodating polycrisis and structural uncertainty
• Build interdepartment workflows combining AI with standard econometric tools

TRAINING IS TAILORED TO

• Central bank economists and monetary policy analysts
• Research and forecasting teams in finance ministries and international institutions
• IMF, BIS, OECD, BIS‑IH, SUERF, and financial policy think‑tank professionals
• Financial risk and data science teams in sovereign funds, multilateral banks

TRAINING METHODOLOGY

• Expertled sessions with live AI model builds and dataset ingest
• Case studies from ECB, Fed, BoE, BIS (Project Spectrum, PaLM nowcasts)
• Handson labs: Python, R, TensorFlow, HuggingFace Transformers
• Working groups: scenario design, polycrisis stresstesting
• Model governance workshops on explainability and regulation
• Final capstone: deploy inflation forecasting toolkit with real central bank data

DAY ONE:

LLMDRIVEN INFLATION NOWCASTING

• The evolving central bank data landscape: satellite, transaction, text streams
• Introduction to LLMs (PaLM, GPT) for macroeconomic nowcasting
• Handson: PaLM/GPT prompts to forecast inflation
• Benchmarking vs Survey of Professional Forecasters (SPF)
• Governance: hallucination risk and model validation

DAY TWO:

MACHINE LEARNING & ENSEMBLE FORECASTING

• Random forests, XGBoost, and neural nets for inflation prediction
• Transformer architectures for macro time series
• Lab: Build and compare forest, transformer, and ARIMA models
• Explainability with Shapley values and LIME

DAY THREE:

ALTERNATIVE DATA FOR MACRO NOWCASTING

• Payment system datasets and realtime indicators
• Webscraped prices and sentiment signals
• Satellite imagery for input‑output dynamics
• Lab: Fuse highfrequency data into inflation models
• Discussion: Data governance and privacy

DAY FOUR:

AI IN POLICY SIMULATION & DSGE/ACE MODELS

• Agentbased & reinforcement learning in policy simulations
• Augmenting DSGE models with AI agents
• Lab: Implement a simple policy simulation with RL
• Scenario workshops: polycrisis, supplychain shocks, energy transitions

DAY FIVE:

GOVERNANCE, DEPLOYMENT & FUTURE AI IN POLICY

• Governance frameworks at central banks
• Platforms: HuggingFace, Azure, Fed's ARI tools
• Lab: Build a forecasting dashboard with Python/BI tools
• Case study: BIS Project Spectrum & ECB generative nowcasts
• Plenary: Action plans, integration strategies, collaboration roadmap