Introduction:

Artificial intelligence is reshaping budgeting, financial forecasting, and financial planning by enabling central banks to enhance analytical capabilities, forecasting accuracy, resource allocation, and strategic decision making. Leading central banks are adopting AI within structured governance frameworks that emphasize data quality, model governance, transparency, cybersecurity, risk management, and responsible innovation while preserving financial integrity and institutional accountability. This training program examines international benchmarking practices, documented institutional case studies, and comparative analyses of AI adoption in budgeting and financial forecasting across selected central banks. It provides an institutional perspective on identifying transferable practices, evaluating organizational readiness, and developing strategic recommendations tailored to the operational context of the Central Bank of Nigeria.

Program Objectives:

By the end of this program, participants will be able to:

Analyze international approaches to AI adoption in budgeting and financial forecasting across central banks.
Evaluate governance frameworks, data management practices, and AI oversight models supporting financial planning.
Assess documented institutional case studies and comparative benchmarking methodologies relevant to central banking.
Examine opportunities, risks, and organizational considerations associated with AI-enabled budgeting and forecasting.
Develop strategic recommendations for strengthening budgeting and financial forecasting within the Central Bank of Nigeria.

Target Audience:

Budget and Financial Planning Managers.
Financial Planning and Analysis (FP&A) Professionals.
Central Bank Finance and Budget Officers.
Strategy and Performance Management Professionals.
Digital Transformation and AI Governance Professionals.

Program Outline:

Unit 1:

International Benchmarking of AI in Central Bank Budgeting:

International benchmarking methodologies for central banks.
Comparative budgeting and financial forecasting frameworks.
AI adoption trends across selected central banks.
Institutional benchmarking indicators and performance measures.
Comparative review of documented central bank practices.

Unit 2:

AI Governance and Financial Planning Frameworks:

Governance models for AI in financial planning.
Data governance and financial data quality frameworks.
AI model governance and institutional oversight.
Risk management and regulatory considerations.
Comparative analysis of governance practices adopted by central banks.

Unit 3:

Comparative Case Studies of AI in Budgeting and Forecasting:

Documented institutional case studies from selected central banks.
AI applications in budget formulation and expenditure forecasting.
AI supported financial forecasting and scenario analysis frameworks.
Comparative assessment of implementation approaches and outcomes.
Interactive discussion on lessons applicable to the Central Bank of Nigeria.

Unit 4:

Institutional Readiness and Strategic Assessment:

Organizational readiness for AI-enabled financial planning.
Comparative evaluation of technology, data, and governance capabilities.
Institutional challenges and critical success factors.
Responsible AI adoption within central banking environments.
Benchmarking frameworks for organizational capability assessment.

Unit 5:

Strategic Roadmap for the Central Bank of Nigeria:

Comparative analysis of transferable international practices.
Institutional opportunities for enhancing budgeting and forecasting.
Strategic priorities for AI integration within financial planning.
Governance recommendations for sustainable AI adoption.
Interactive executive discussion on institutional recommendations and future strategic direction.

International Benchmarking on AI in Budgeting and Financial Forecasting