This 5-day course empowers finance professionals across all functions with the skills to use Artificial Intelligence and Monte Carlo Simulation for enhanced decision-making. By integrating AI-driven analysis with advanced probabilistic modeling, participants will explore how to assess uncertainty, forecast outcomes, evaluate risk exposure, and optimize financial decisions in dynamic environments. Real-world applications across investment, treasury, and financial control functions are emphasized throughout.
- Monte Carlo simulation principles in financial modeling - AI integration into forecasting, scenario planning, and simulations - Risk quantification using probabilistic distributions and stress testing - AI-powered scenario generation (LLMs, GANs, diffusion models) - Treasury and liquidity forecasting under uncertainty - Value-at-Risk (VaR), CVaR, and tail-risk modelling - ESG, macroeconomic, and geopolitical scenario simulations - Cloud computing, Python/GPU tools for large-scale simulation - AI in credit risk, cash flow modeling, and insurance underwriting - Governance, model risk management, and ethical AI in finance
- Apply Monte Carlo simulation to support financial, treasury, and investment decisions - Use AI models to generate dynamic scenarios and improve forecast accuracy - Quantify risk and optimize portfolios using AI-enhanced metrics - Simulate ESG and macroeconomic shocks to assess resilience - Build financial dashboards integrating simulation outputs - Use Python as a user or AI platforms for simulation and automation - Evaluate simulation assumptions, transparency, and regulatory compliance - Implement AI models that align with internal audit and risk governance policies
- Corporate finance, investment, and treasury teams - Risk, compliance, and internal audit professionals - Financial planning, FP&A, and budgeting managers - Credit analysts, insurance and actuarial departments - Financial controllers and CFO office advisors - Fintech and digital transformation leaders in finance
- Expert-led interactive sessions blending theory and hands-on practice - Real-life case studies from global financial institutions - Guided labs using Python, Excel, and AI tools - Group simulations and peer collaboration - Scenario-based forecasting challenges - Interactive dashboards and visualization building - Live demonstrations of AI & Monte Carlo integration - Post-course toolkit with models and templates
- Key financial decisions under uncertainty
- Monte Carlo principles: distributions, iterations, randomness
- Introduction to AI tools in forecasting and decision support
- Risk vs uncertainty: finance applications
- Hands-on: Building a basic Monte Carlo model in Excel
- Use cases in budgeting, capital planning, credit risk
- Scenario generation: macro, market, and business drivers
- Applications in liquidity risk, project valuation, and FX exposure
- Value-at-Risk, Expected Shortfall, and tail event modelling
- Monte Carlo in insurance claims and credit stress testing
- Forecasting balance sheet and P&L scenarios
- Hands-on: Multi-variable Monte Carlo simulation using Python or Excel
- Generative AI models for scenario planning (ChatGPT, GANs, etc.)
- Using AI to forecast revenue, expenses, and working capital
- AI-assisted portfolio risk-adjusted returns and stress testing
- Automating simulations with AI pipelines and tools
- Use of AI in audit sampling, anomaly detection, and controls testing
- Hands-on: Use an AI platform (no-code/low-code) to run forecasts
- Monte Carlo for liquidity forecasting and treasury cash flow stress tests
- Corporate finance: AI-driven project appraisal under uncertainty
- Integration into enterprise risk management (ERM) frameworks
- ESG, geopolitical risk and supply chain uncertainty simulation
- Real-life case studies across sectors (banking, corporates, insurance)
- Hands-on: Stress testing financial KPIs with AI & simulations
- Governance: assumptions, transparency, ethics in AI-driven models
- Dashboarding and communicating risk visually to executives
- Regulatory alignment: IFRS, Basel, Solvency, ESG reporting
- Group project: build and present your own AI + simulation model
- Review of tools: Python, Excel, Knime, Power BI, ChatGPT
- Strategic roadmap for AI adoption in your financial department
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.