MODELS AND SOFTWARE TOOLS FOR FORECASTING AND MANAGING FINANCIAL RISKS

Анотація

The section presents modern models and software tools for analysis, forecasting and management of financial risks. Mathematical approaches such as regression analysis, time series models (ARIMA, GARCH), scenario analysis, stress testing and portfolio optimization using the Markowitz model are considered. Special attention is paid to machine learning methods, including Random Forest, Gradient Boosting and neural networks, which provide high accuracy of forecasts and detection of hidden dependencies in data. The use of modular and microservice architectures is proposed for the development of systems that integrate big data analysis, optimization and visualization tools. The stages of data processing, the application of containerization (Docker, Kubernetes), as well as deployment automation (CI/CD) using cloud platforms (AWS, Azure, Google Cloud) are described in detail. Special attention is paid to security issues, including data encryption (AES-256), access control (RBAC, MFA) and system monitoring. The presented quantitative and qualitative assessments of the effectiveness of methods and models demonstrate the possibilities of reducing risks and increasing the resilience of financial systems. The chapter can become a basis for researchers, analysts and developers involved in creating financial modeling software, as well as for developing risk management strategies in changing economic conditions. The purpose of this research is to create an integrated approach to forecasting and managing financial risks by developing modern mathematical models, applying machine learning methods and implementing advanced software engineering tools. The research is aimed at improving tools for time series analysis, portfolio optimization, risk classification and forecasting using algorithms such as Random Forest, neural networks and GARCH. Particular attention is paid to the development of scalable software systems with modular and microservice architecture, integrated with cloud platforms to provide real-time processing of big data. An important aspect is ensuring the security of financial data through encryption, access control and monitoring, as well as the creation of interactive visualization tools to support decision-making. The results of the research are aimed at increasing the accuracy, efficiency and reliability of solutions in the field of financial modeling. The methodology presented in this article is based on a review of current technical and software solutions for controlling unmanned aerial vehicles (UAVs), focusing on hardware platforms, sensors, communication systems, and software. Comparative analysis of hardware platforms (FPGA, ARM, Atmel, Raspberry Pi) on key parameters: performance, flexibility, power consumption, complexity and cost. Software evaluations that include open platforms (ArduPilot, PX4, LibrePilot) and high-level control systems (Aerostack2, GAAS). Integration of sensor data using machine learning algorithms, such as the Kalman filter, to improve navigation accuracy and flight stability. Modeling of UAV energy consumption taking into account cargo weight, route length and quadratic growth due to aerodynamic drag. Analysis of multi-agent systems for drone group coordination, including trajectory modeling and motion synchronization. A graphical representation of the data that demonstrates a comparison of platforms, trajectories and energy consumption patterns The scientific novelty of the research lies in the integration of modern mathematical methods, machine learning algorithms and software engineering to solve the problems of forecasting and managing financial risks. A combination of traditional models, such as ARIMA, GARCH and regression analysis, with deep learning methods is proposed, which provides increased forecasting accuracy and adaptability to market changes. The use of microservice architecture and cloud platforms allows you to create scalable systems for processing large amounts of data in real time. An innovative approach to financial data protection has been implemented, including encryption, access control and monitoring, and interactive visualization tools have been developed that facilitate rapid analysis of results and decision-making. The research offers a comprehensive approach to risk management, focused on solving current problems in the financial sector using advanced technologies. Results. The research achieved a number of significant results that ensure increased efficiency and accuracy of financial risk forecasting. Mathematical models, in particular ARIMA and GARCH, were developed and integrated, allowing for detailed analysis of time series and assessment of financial indicator volatility. The use of machine learning algorithms, such as Random Forest, Gradient Boosting, and neural networks, ensured forecasting accuracy of up to 90% when analyzing risk portfolios and scenarios. Microservice architecture became the basis for creating scalable systems that easily adapt to changing market conditions, and cloud platforms (AWS, Azure, Google Cloud) provided high computing power and availability. Containerization using Docker and Kubernetes significantly simplified the management of system components and their integration. Interactive visualization tools, such as risk heat maps and interactive dashboards, were developed to simplify the analysis of complex financial data and facilitate informed decision-making. Ensuring data security through the implementation of encryption (AES-256), multi-level access (RBAC) and monitoring systems has increased the security of confidential information. Stress testing and scenario analysis have allowed us to assess the impact of extreme events on financial systems, develop strategies to reduce losses and ensure the stability of portfolios. The developed methods have demonstrated effectiveness in changing market conditions, confirming their value for analysts, financial institutions and software developers.

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