Effective central bank communication is now powered by AI. This 5-day course empowers participants to harness natural language processing, sentiment analytics, and multimodal signal analysis to decode tone, forward guidance, market reactions, and even non-verbal cues. Through cutting-edge models, real-time dashboards, and policy case studies, delegates will gain actionable insights to elevate transparency, credibility, and public trust in central banking.
- LLM-driven sentiment classification of monetary policy statements
- Topic & stance extraction using fine-tuned models
- Event risk detection via NLP and news analytics
- Multimodal sentiment: text, tone, facial cues in speeches
- Nowcasting policy intent using forward-looking indicators
- Market-response models: AI predicting reactions to communications
- Explainable AI & governance: Fairness, bias, regulatory compliance
- Use–cases from ECB, Fed, RBI sentiment indicators
- Develop sentiment-scoring models for central bank announcements
- Parse policy tone dimensions (hawkish, dovish, neutral) with AI
- Integrate multimodal data—speech, facial expression, tone—for richer analysis
- Build forward-looking indicators for policy tonality
- Trace market response patterns to communication signals
- Use explainable AI techniques to ensure transparent modeling
- Set up automated real-time monitoring dashboards
- Draft AI-enhanced communication strategies with predictive insight
- Central bank communications, policy, and strategy teams - Financial market analysts and economic research teams - Central banking tech/digital innovation units - Monetary intelligence units in financial regulators - Strategy/trading desks in institutional finance
- Interactive lectures on NLP, multimodal AI, and explainability - Labs: LLM fine-tuning, speech-tone analysis, sentiment pipelines - Case studies: FedNLP, RBI and Bundesbank sentiment tools - Real-time sentiment dashboards using Python & BI tools - Governance clinics: bias detection, regulatory alignment - Capstone: create and present a live sentiment-analysis system
- LLM-based framework: topic, stance, sentiment
- Supervised classifiers (FinBERT, SVM) for central bank text
- Lab: Fine-tune a model on central bank press releases
- Benchmark: real vs model-derived communication tone
- Discussion: interpretability and explainable AI
- Nowcasting policy intent via forward-looking indicators
- News and macro-event scraping pipelines
- Lab: Build real-time NLP system to tag market fever lines
- AI-generated policy risk dashboards
- Speech tone and voice-modelling techniques
- Facial-expression encoding in speech analysis
- Lab: Train multimodal classifier for central banker speeches
- Case Review: “Emotions of Monetary Policy” ML study
- AI-driven models that predict market responses
- ECB/Bundesbank AI sentiment analyses
- Lab: Backtest announcement-to-market response model
- Discussion: speed vs accuracy in AI-driven reactions
- Fairness, bias & transparency in communication AI
- Dashboard deployment with Python/Power BI
- Capstone: Run and present your sentiment-AI pipeline
- Roadmap: Operationalizing AI-enhanced communications
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.