Course Outline
Introduction to Generative AI for Government
- Overview of generative models and their relevance to financial operations in the public sector
- Types of generative models: Large Language Models (LLMs), Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs)
- Strengths and limitations in financial contexts for government
Generative Adversarial Networks (GANs) for Financial Applications in Government
- How GANs work: the interaction between generators and discriminators
- Applications in synthetic data generation and fraud simulation for government agencies
- Case study: generating realistic transaction data for testing financial systems
Large Language Models (LLMs) and Prompt Engineering for Government
- How LLMs understand and generate financial text relevant to public sector operations
- Designing prompts for forecasting and risk analysis in government contexts
- Use cases: summarization of financial reports, Know Your Customer (KYC) processes, detection of red flags
Financial Forecasting with Generative AI for Government
- Time series forecasting using hybrid LLM and machine learning models for government
- Scenario generation and stress testing in public sector financial planning
- Use case: revenue prediction leveraging structured and unstructured data sources
Fraud Detection and Anomaly Identification for Government
- Using GANs for anomaly detection in government transactions
- Identifying emerging fraud patterns through prompt-based LLM workflows in public sector operations
- Model evaluation: balancing false positives with true risk indicators in a governmental context
Regulatory and Ethical Implications for Government
- Ensuring explainability and transparency in generative AI outputs for government use
- Addressing the risk of model hallucination and bias in financial applications for government
- Compliance with regulatory expectations (e.g., GDPR, Basel guidelines) in public sector operations
Designing Generative AI Use Cases for Financial Institutions in Government
- Building business cases for internal adoption of generative AI in government agencies
- Balancing innovation with risk and compliance in public sector applications
- Establishing governance frameworks for responsible AI deployment in government
Summary and Next Steps for Government
Requirements
- An understanding of fundamental finance and risk management principles for government operations.
- Experience with spreadsheets or basic data analysis techniques.
- Familiarity with Python is beneficial but not mandatory.
Audience
- Risk managers in the public sector.
- Compliance analysts for government entities.
- Financial auditors working within governmental organizations.
Testimonials (3)
The background / theory of LLMs, the exercise
Joanne Wong - IPG HK Limited
Course - Applied AI for Financial Statement Analysis & Reporting
it has opened my mind to new tool that can help me in creating automation
Alessandra Parpajola - Advanced Bionics AG
Course - Machine Learning & AI for Finance Professionals
I very much appreciated the way the trainer presented everything. I understood everything even if Finance is not my area, he made sure that every participant was on the same page, while keeping up with the time left. The exercises were placed at good intervals. Communication with the participants was always there. The material was perfect, not too much, not too little. He elaborated very well on a bit more complicated subjects so that it can be understood by everyone.
