Ahmed Abd Zaid Abdulwahid Abedi

Abstract: This article presents a comprehensive automated model for analyzing and forecasting the financial condition of an oil production enterprise. The proposed model integrates complex financial analysis with predictive capabilities to facilitate data-driven decision-making. The model relies on key financial indicators to provide a robust assessment of a company's financial health, offering insights into its competitiveness, profitability, liquidity, financial stability, and business activity. The automation of calculations improves accuracy and efficiency, while predictive features enable foresight into the company's financial trajectory. However, the model effectiveness is contingent on accurate input data and does not fully account for unpredictable external influences. Despite these limitations, the model can significantly enhance financial management strategies within the volatile landscape of the oil industry, supporting sustainable growth. Key words: automated model, financial analysis, forecasting, oil production enterprise, financial condition, predictive analytics, financial management, business decision-making, financial indicators.

The article is devoted to the issues of determining the indicators characterizing the effectiveness of the constructed business process model of an oil production enterprise. The author concludes that the priority approach to assessing the effectiveness of the business process model is coefficient analysis.

The impact of dependent variables of non-cash transactions in digital tourism on consumer behavior is of interest to scientists and practitioners in many countries. The purpose of the study was to test the factors of electronic satisfaction when making non-cash transactions based on the model proposed in 2000. The study attempts to validate the electronic satisfaction model for non-cash transactions in the field of digital tourism. Factors that positively affect the implementation of non-cash payments were divided into four categories, defined in previous studies. This research is of an analytical and descriptive type. Primary data obtained from an online survey were used for processing. Correlation and regression analysis was used to test hypotheses. It is shown that convenience, merchandising, site design and financial security play a key role in consumer satisfaction when making non-cash transactions when purchasing goods in e-tourism. The validity of the electronic satisfaction model has been confirmed for Russian digital tourism.

The oil industry is one of the main key points in the economies of Russia and Iraq, therefore, the state, social and economic life of the country depends on its productivity, functioning, and stability. The oil industry has several tasks: improving energy efficiency, modernizing equipment, and innovative developments. The oil industry has experienced ups and downs over the years, but the 2020 crisis hit the industry harder than in all years. The long-term decline in oil demand paints highly unfavorable prospects for the future. The Covid 19 crisis was the worst in the history of the oil industry. As companies and entire industries recoil from its effects, oil prices continue to fall. The pandemic has reduced the demand for oil to such an extent that the industry is unable to adapt to such a sharp decline. And the buildup of oil reserves further lowers fuel prices. This article is devoted to the reflection of the algorithm and the substantiation of the practical application of the developed automated model of analysis and forecasting of the financial condition of an enterprise using regression analysis in assessing the financial indicators of an oil production enterprise. In the course of the research, the methods of logical, statistical analysis were used. The developed prototype of an automated model for complex analysis and forecasting of the financial condition of oil production enterprises is a good basis for creating a full-fledged commercial system for the oil production business. It is concluded that the new automated model of analysis and forecasting provides an opportunity to analyze the financial condition of an oil production enterprise, including the identification of its financial difficulties and the study of financial attractiveness, as well as to predict the financial condition of an oil production enterprise within almost any time interval, determining ways to overcome its financial difficulties and increasing financial attractiveness.

The digitization of the economy in the modern world has become an inevitable process that objectively affects all areas of society and determines the direction of its development. The importance of the study is evidenced by the high degree of modern development combined with the constant transformation. In the process of informing the economy, the implementation of the principles of digitizing the economy by an organization is impossible without the use of tools that can ensure the continuous work on making changes to the current economic system. These tools are (acting) projects. Which called for classifying projects (representation) into the five groups. The numerical procedures for project risk (representation) are defined from three scenarios in which each scenario and its risks are identified. The evaluation of the assessed factors of the digital (representation) project was developed on a scale of 3 weight points for each risk, as well as calculating the importance of risks regarding the not optimal level for a fraction of the cost of the project and Implementing (feasibility) the project in decision-making of choose the project (representation) or Non-continuity of the project (representation) during implementation. Which indicates the importance and importance of this risk to the project and to the financial stability of the company as a whole

The forecasting financial condition of the enterprise aims to assess financial prospects for the future period, based on the results obtained, plan future actions, and recommendations for improving financial performance. As such, there are no separate approaches to forecasting exactly the financial condition of an oil-producing company, there are only separate methods, and there are 4 groups of them (expert estimation methods, deterministic, stochastic, trend extrapolation methods). A method for predicting the financial condition of an oil-producing enterprise is proposed. Of the existing methods, a regression analysis method was chosen because it considers the forecast and changes in the environment and industry in the long term.