QUANTITATIVE RISK ASSESSMENT OF INVESTMENT PROJECTS USING DIGITAL TECHNOLOGIES

The use of digital technologies for risk management of investment projects is relevant and promising in accordance with the development trend of Industry 4.0. The purpose of the study is to apply the Monte Carlo method using software tools for quantitative risk assessment of construction investment projects on the example of organizations operating on the territory of St. Petersburg. The methodological basis of the study was the qualitative and quantitative methods, which assume: 1) formation of the risk register; 2) risk ranking in order to identify the most likely risk for construction projects; 3) the behavior of quantifying the risks that have the greatest impact using the functional add-in for MS Excel @Risk. The article analyzes the risks that affect the construction industry. The criteria for assessing the probability and degree of risk impact on investment projects in the construction industry are formed. The risks affecting investment construction projects are identified, which are divided into several groups: macroeconomic, industry, legal, operational, and financial. In this connection, a risk register has been formed that includes the following risks: rising inflation; regional risks; changes in consumer preferences or market trends; a shortage of land plots for new projects; stricter legislation; failure by subcontractors to fulfill their obligations; deterioration in the conditions for purchasing construction materials; inability to attract and retain key personnel; more frequent accidents at construction sites; changes in interest rates and capitalization conditions for project financing; changes in exchange rates; difficulties in raising capital; credit risk associated with customers; the risk of reduced liquidity. Using the constructed risk map for construction companies, the risk that poses the greatest threat to the organization was determined, both in terms of the degree of influence and the probability of risk realization – the risk of a decrease in liquidity. To perform the calculation using the @Risk add-in, the amount of damage from the implementation of the risk of reducing liquidity in three scenarios is determined. The distribution graph constructed in @Risk using the Monte Carlo method allowed us to determine that the random variable is most likely to be greater than the predicted value of the realistic scenario. In this regard, it is necessary to revise this value in the direction of increasing the damage from the implementatio