METHODS, TOOLS AND RISKS OF INVESTMENT CONTROLLING OF IT COMPANIES IN CONDITIONS OF ECONOMIC INSTABILITY
DOI:
https://doi.org/10.32782/2311-844X/2025-1-6Keywords:
controlling, investments, methodological tools, economic uncertainty and instability, investment controlling, risksAbstract
The article analyzes key scientific approaches to defining the essence of Tools controlling and the classification of its methods and instruments. It is established that investment controlling refers to a system of measures aimed at ensuring control over the processes of planning, monitoring, and adjusting investment activities in order to minimize risks, increase project effectiveness, and accumulate the financial resources needed for economic operations and the sustainable development of a business entity. The study reveals the nature of investment controlling as an effective strategic management tool that enhances the efficiency of investment resource usage, reduces risks, and strengthens financial stability. It examines modern methods and tools of investment controlling that are adapted to the specific features of IT companies operating under economic instability and strategic uncertainty. It is found that the most effective methods for IT companies include Sensitivity Analysis (efficiency score: 4.8), Net Present Value (NPV) (efficiency score: 4.7), and Scenario Analysis (efficiency score: 4.5). The most commonly used investment controlling Tools in IT companies are identified as BI systems, MS Excel, financial calculators, analytical scripts, ERP systems, Google Data Studio, and Power BI dashboards. The study substantiates the need to adapt classical approaches to investment controlling, taking into account the dynamic IT environment, the high share of intangible assets, and external instability. The main directions for improving the effectiveness of investment controlling are proposed, including the development of digital technologies, the enhancement of analytical competencies among management personnel, the strengthening of intellectual capital, and ensuring the transparency of investment projects. Further research prospects include the development of industry-specific controlling models for the IT sector and the integration of artificial intelligence and machine learning into controlling systems.
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