Risk Impact Assessment Methods And Techniques Training

Objectives:
• Define risk impact assessment and its importance in organisational decision-making.
• Identify the key principles and frameworks underlying risk assessment.
• Differentiate between risk identification, analysis, and evaluation.
• Explain the role of risk impact assessment within enterprise risk management.

Topics:
• Overview of risk management standards (ISO 31000, COSO).
• Key terminology: hazard, risk, likelihood, consequence, impact.
• The risk assessment process: context, identification, analysis, evaluation.
• Legal and regulatory requirements in South Africa.

Objectives:
• Apply various risk identification methods to uncover potential risks.
• Use brainstorming, checklists, and structured interviews effectively.
• Document identified risks in a risk register.
• Recognise biases that affect risk identification.

Topics:
• Brainstorming and nominal group technique.
• Delphi method and expert judgment.
• SWOT analysis and PESTLE analysis.
• Developing a risk register template.

Objectives:
• Understand qualitative risk analysis and its application.
• Use risk probability and impact matrices.
• Prioritise risks using qualitative scales.
• Apply risk rating criteria consistently.

Topics:
• Probability and impact scales (ordinal, descriptive).
• Risk matrix construction and interpretation.
• Risk ranking and prioritisation.
• Subjectivity and calibration in qualitative analysis.

Objectives:
• Differentiate between qualitative and semi-quantitative approaches.
• Use weighted scoring systems for risk evaluation.
• Convert qualitative scales into numerical values.
• Apply risk indices and heat maps.

Topics:
• Weighted risk scoring methods.
• Risk indices (e.g., risk priority number).
• Heat maps for visual representation.
• Case studies: semi-quantitative applications.

Objectives:
• Understand probability distributions used in risk analysis.
• Apply normal, lognormal, and triangular distributions.
• Use expected value and variance calculations.
• Interpret probability density functions.

Topics:
• Types of probability distributions.
• Parameter estimation from historical data.
• Monte Carlo simulation basics.
• Expected monetary value (EMV).

Objectives:
• Build a simple Monte Carlo simulation model.
• Interpret simulation outputs (histograms, cumulative curves).
• Perform sensitivity analysis to identify key drivers.
• Use software tools for simulation (e.g., @RISK, Crystal Ball).

Topics:
• Steps in Monte Carlo simulation.
• Model inputs: assumptions and correlations.
• Simulation output analysis: percentiles, probability distributions.
• Sensitivity analysis: tornado charts.

Objectives:
• Construct decision trees for sequential decisions.
• Calculate expected values and fold back decision trees.
• Incorporate probabilities and payoffs.
• Evaluate risk attitudes in decision-making.

Topics:
• Decision tree components: nodes, branches, payoffs.
• Expected value calculation and rollback.
• Sensitivity analysis on probabilities.
• Utility theory and risk tolerance.

Objectives:
• Identify financial risks: market, credit, liquidity, operational.
• Apply value at risk (VaR) and conditional VaR.
• Use scenario analysis for financial exposures.
• Assess risk impact on financial statements.

Topics:
• Financial risk categories.
• Value at Risk (VaR) calculation methods.
• Stress testing and scenario analysis.
• Impact on cash flow and profitability.

Objectives:
• Apply risk assessment to operational processes.
• Use failure mode and effects analysis (FMEA).
• Assess project risks using PERT and critical path analysis.
• Integrate risk impact into project schedules.

Topics:
• FMEA methodology and risk priority numbers.
• Bow-tie analysis for operational risks.
• Project risk assessment: PERT estimation.
• Risk-adjusted project scheduling.

Objectives:
• Understand Bayesian updating for risk assessment.
• Construct influence diagrams for complex risks.
• Combine expert judgment with data.
• Apply Bayesian networks in risk modelling.

Topics:
• Bayes' theorem and prior/posterior probabilities.
• Influence diagrams: nodes and arcs.
• Bayesian network construction.
• Case study: Bayesian risk assessment.

Objectives:
• Evaluate risk levels against risk criteria.
• Compare risk treatment options (avoid, reduce, transfer, accept).
• Perform cost-benefit analysis of treatment measures.
• Develop risk treatment plans.

Topics:
• Risk evaluation criteria and risk appetite.
• Risk treatment options and controls.
• Cost-benefit analysis for risk mitigation.
• Residual risk and risk acceptance.

Objectives:
• Consolidate risk assessment outputs into a comprehensive report.
• Communicate risk findings to stakeholders.
• Establish risk monitoring and review processes.
• Apply continuous improvement to risk assessment practices.

Topics:
• Risk report structure and key elements.
• Risk communication and escalation.
• Monitoring indicators and triggers.
• Lessons learned and updating risk assessments.