This article analyzes modern approaches to forecasting regional socio-economic development, including the use of economic-mathematical models, artificial intelligence, and geoinformation technologies. The study highlights the significance of integrating targeted and empirical approaches with composite indicators to ensure sustainable development. Reducing interregional disparities, stimulating investment activity, and preparing reliable forecasts for policymaking are considered important scientific and practical tasks. Thus, the role of forecasting and planning processes as a scientific foundation for strategic governance is substantiated.

This article develops a multiple econometric model to assess the efficiency of managing state assets in the Republic of Uzbekistan. The dependent variable is the volume of valuation services performed (Y), while the explanatory variables include the number of privatized state assets (X1), the number of certified appraisers (X2), and the initial value of intangible assets (X3). The reliability of the model was verified using descriptive statistics, correlation analysis, VIF and ADF tests. The results demonstrate the significant impact of the selected factors on the volume of valuation services and confirm the model’s applicability for forecasting purposes

This article analyzes the development trends of the chemical industry in the Republic of Uzbekistan, the dynamics of production volumes, and the possibilities of modeling this process. The study empirically evaluates the elasticity of production volume in relation to investment and employment levels based on the Cobb–Douglas production function. A classical linear regression model was constructed using statistical data from 2010 to 2023, and analyses were conducted in the Stata software. Regression results indicate that the growth in production volume is more closely associated with the increase in labor resources. Additionally, forecast values for 2024–2028 were calculated using an autoregressive (AR) model, demonstrating a steady growth in chemical product output. The results of the study hold significant practical importance for strategic management of the chemical industry and for shaping investment policy.

Forecasting socio-economic development indicators plays a crucial role in modern economics and social spheres. This process is utilized by governments, businesses, and international organizations to develop long-term strategies, allocate resources efficiently, and reduce social inequality. The present research examines theoretical and methodological aspects of forecasting socio-economic development indicators.

The article presents an econometric analysis of trends in the development of industrial infrastructure in the Kashkadarya region. Based on the parameters of the production function, which expresses the dependence of the volume of industrial production on the cost of fixed assets and the amount of labor resources, an econometric model was created, and the reliability of the model was assessed. The efficiency indicators of the model factors and forecasts of industrial production volumes were calculated, conclusions and proposals were made

This article considers foreign experience as an important factor in ensuring the effectiveness and sustainable development of financial planning in enterprises. In developed countries, financial planning is carried out, first of all, in combination with market mechanisms, digital technologies and financial control systems. Efficient allocation and optimization of financial resources, the use of automated models for budgeting and forecasting, the implementation of modern risk management methods, compliance with international financial reporting standards, and ensuring financial transparency and accountability based on corporate governance principles are of great importance. At the same time, the experience of foreign enterprises shows the widespread use of innovative financing sources - venture capital, bonds, leasing and crowdfunding. The analysis of these experiences serves as an important methodological basis for Uzbek enterprises to improve the effectiveness of financial planning, strengthen competitiveness and accelerate the integration processes into world markets

This scientific study provides an in-depth analysis of the factors influencing the management efficiency of free economic zones (FEZs) in the Republic of Uzbekistan, assessing their contribution to socio-economic development using mathematical modeling. Using the Jizzakh Free Economic Zone as a case study, the research identifies key factors affecting production volume from 2015 to 2024 — namely, foreign investment and employment levels. The study employs the Cobb-Douglas production function to construct a classical linear regression model, along with an autoregressive (AR) model, correlation-regression analysis, and evaluation through Fisher’s criterion, Student’s t-test, and the Durbin-Watson statistic.

In recent years, the intensification of global financial instability has posed significant challenges to banking markets. In response, commercial banks have placed particular emphasis on enhancing their resilience to economic fluctuations. According to expert forecasts, activity in the global banking sector is expected to increase substantially in 2025–2026. Notably, inflation in the Eurozone in 2024 is projected to be around 2.3% instead of the previously forecast 2.7%. Financial stability, however, remains tied to moderate growth prospects. Various models typically chosen or approved by regulators are used to assess banks’ stability. Contemporary research focuses on improving existing methodologies and financial stability indicators in light of changes in the global macroeconomic environment, raising capital adequacy requirements for banks, and enhancing tools for forecasting their future performance. At the same time, the adoption of new technologies and financial instruments is accelerating banks’ digital transformation and the automation of their business processes. This article offers a comprehensive study of the means and mechanisms for ensuring the financial stability of commercial banks in Uzbekistan. Its primary objective is to analyze the state of the banking sector and identify ways to strengthen its resilience, drawing on global best practices and national specificities. To achieve this, both scientific-pedagogical methods (historical-comparative analysis, structural-logical research) and empirical approaches (statistics from regulatory documents and bank reports, expert surveys) are employed.

This article analyzes the development of the fisheries sector in the Republic of Uzbekistan, particularly in the Syrdarya region, using econometric methods. The study highlights the role of fish products in food security, public health, and economic diversification. The analysis is based on statistical data from 2010 to 2023 and evaluated using autocorrelation and the Durbin-Watson test. Forecast values for the next five years are developed, and practical conclusions are presented to further enhance the fisheries sector.

This study analyses historical data (2000–2024) to forecast Uzpromstroybank’s deposit base and total resource trends using simple OLS regression models. A linear time-trend model is estimated for both series to capture long-run growth. The regression results show strong, significant upward trends (high R², statistically significant coefficients) for deposits and resources, indicating robust growth. Forecasts generated by extrapolating these trends suggest continued expansion of the bank’s deposits and resources in the short term. The findings are relevant for bank management and policymakers, as they highlight the trajectory of funding sources in Uzbekistan’s banking sector. Limitations include the simplicity of the linear model and potential structural changes, nonetheless, the results provide a baseline projection and underline the importance of improving deposit mobilization and financial sector reforms.

The article examines the impact of digitalization on business planning in commercial banks. It analyzes artificial intelligence, Big Data, and cloud computing technologies that improve forecasting accuracy. Key digital solutions and their further development prospects in the banking sector are identified.