The seminar can be held online on the official International Business Academy platform. On completion of the training you will be given a link to the recording, which will be available for one month.
*dates are subject to additional confirmation
excluding VAT
* VAT of 16% will be added to the invoice
Programme goal:
Training managers and specialists in methods of conducting quantitative risk assessment in Excel using the @Risk add-in — the world's most widely used Monte Carlo simulation tool. The programme aims to build practical skills in analysing uncertainty, calculating probabilities and making risk-based management decisions.
The programme focuses on:
— Mastering the key methodologies of quantitative risk assessment (production, logistics, commercial, financial)
— Practising the skills of building financial and economic models and running Monte Carlo simulations
— Analysing the results of quantitative risk assessment, including determining the probability of achieving goals (KPIs, financial and project indicators)
— Applying quantitative methods to make management and corporate decisions better founded
Objectives:
— Study the basics of probability theory and statistics as applied to risk management
— Master methods of building quantitative risk assessment models in Excel (@Risk)
— Learn to analyse random variables and choose the optimal probability distributions
— Examine the key methods: Monte Carlo, Latin hypercube, the Tornado diagram, decision trees, scoring models
— Practise analysing business plans, investment projects and budgets taking risks into account
— Learn to use simulation results to choose the optimal strategy and evaluate the effectiveness of risk mitigation measures
— Practise decision-making using quantitative data
Skills developed:
— Applying Monte Carlo simulation in risk management
— Building and analysing financial and economic models under uncertainty
— Developing and interpreting risk scenarios (optimistic, pessimistic, most likely)
— Prioritising risks using quantitative methods
— Making decisions based on probability ranges rather than average values
— Visualising risks (Tornado diagrams, decision trees)
— Using AI and ready-made prompts for analysing and interpreting data
Criteria for participation in the programme:
Target audience and the value of participation for each group:
— Risk managers — mastering quantitative risk analysis tools and integrating them into the corporate risk register
— Financial and investment analysts — applying Monte Carlo, NPV and IRR methods to evaluate projects and budgets
— Economists and planning specialists — using quantitative methods in budgeting, procurement and maintenance
— Internal auditors — applying quantitative analysis when assessing how well decisions are founded
— Key employees of units — participating in building models and identifying risks within the internal control system
The basics of probability theory in risk management. Analysing random variables and the link to business risk analysis.
Introduction to quantitative risk assessment.
The concept of «probability». Formulas for calculating probability from classical probability theory. The concept of a «random variable».
Modelling a random variable.
The probability distributions that a random variable follows.
Continuous and discrete distributions.
Percentiles and VaR (Value at Risk), the most popular varieties of VaR.
The fundamentals of business risk analysis.
Risk modelling.
The behaviour of random variables and modelling it.
An algorithm for choosing the optimal distribution for random variables.
The balance between the plausibility of a model and its complexity.
Building risk assessment models, practical exercises.
The difference between modelling «from the goal affected by the risk» and modelling individual events that affect the achievement of the goal at risk.
Building a «goal-based» quantitative risk assessment model.
Principles of building the model.
Key assumptions and principles of accounting for assumptions in modelling.
The results of quantitative risk assessment, their interpretation and the basics of communicating results to business leaders.
Modelling the relationships between business plan risks.
Direct functional relationships and correlations (overview).
Risk analysis by simulation.
Details of the «Monte Carlo» and «Latin hypercube» simulation methods (the most widely used simulation methods in the world).
Interpreting the results of quantitative risk assessment in terms of impact on the business plan.
Analysing how stretched the business plan is under risk.
Determining the error of quantitative risk assessment.
Key details of the main methods of risk analysis and risk prioritisation.
Modelling correlation between risks (details).
Statistical and expert methods of determining correlations and accounting for them in models.
Moving from «goal-based» risk modelling to «event-based modelling».
The concept of a «random event» and determining its probability.
The difference between prior and conditional probabilities and the details of determining / accounting for them in modelling (based on classical probability theory).
Prioritising risks by quantitative methods.
Regression and correlation analysis (details and differences between the methods) for risk prioritisation.
Practical exercises on the main methods of risk modelling, obtaining results and prioritising risks.
Building tornado diagrams (bridges, waterfalls) as the world's most widely used ways of visualising the results of ranking / prioritising risks.
Moving from an «expert risk register» to quantitative risk assessment and modelling aggregate risk (the risk of the combined influence of all the random events whose impact on achieving the goals needs to be assessed).
Specifics of modelling over different planning horizons of the company's activity (quarter, year, long-term strategic period).
Practical exercises on using all available data and statistics in risk modelling.
Accounting for the expected frequency of events over the planning and quantitative risk assessment horizon.
The difference between the concepts of «probability» and «frequency».
Jointly modelling the frequency and probability of events, taking into account the different consequences when the same event occurs repeatedly.
Statistical models.
Analysing the behaviour of random variables based on available statistics (using cases of currency or price risk, or any other risk for which the class has statistics).
Selecting the most plausible probability distribution (based on available statistics) that describes the behaviour of the random variable, and accounting for it in quantitative risk assessment.
Practical exercises on decision-making based on the results of quantitative risk assessment.
Decision-making with the help of quantitative risk assessment.
Decision criteria when a quantitative assessment model is available.
Choosing the optimal strategy for implementing a business / project when future events may develop in many ways (using quantitative assessment of possible outcomes and options).
Measuring the sensitivity of the decisions made to the parameters built into the quantitative risk assessment model.
Modelling residual risk (assessing risk on condition that certain risk-reducing measures are implemented).
Quantitative assessment of the effectiveness of risk mitigation measures.
Setting and solving optimisation problems: within what limits must a risk or a set of risks lie for the business target indicator to reach its planned target value.
Answers to questions
Training completion
