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Irmukhamedova MD Master's student Higher School of Business and Entrepreneurship under the Cabinet of Ministers of the Republic of Uzbekistan
AUTOMATION OF ANALYTICAL PROCESSES IN CORPORATE FINANCE, PROBLEMS AND CHALLENGES
Abstract: The article considers issues related to the prospects for automating analysis processes in the corporate management system. The use of modern IT technologies, such as automation of financial analysis processes in combination with machine learning and artificial intelligence, allows companies to collect and process significant amounts of information, which leads to more accurate and informed financial decisions. The key aspects and prospects for automating analytical processes in corporate finance are considered, the main problematic issues and challenges associated with the automation of such processes are identified.
Keywords: automation, corporate finance, financial analysis, machine learning, artificial intelligence (ai), big data, digitalization, data security, financial reporting, cybersecurity.
Introduction: Modern digital technologies are rapidly changing the landscape of corporate finance and credit analysis. Automation of the processes of analysis and assessment of the creditworthiness of enterprises opens up new opportunities for increasing the efficiency of financial management, improving the accuracy of forecasting and optimizing business processes. This aspect of the use of information technology in corporate finance is one of the most important trends in the digital transformation of business. The use of digital technologies to automate the process of financial analysis using big data analysis, machine learning and artificial intelligence (AI) allows companies to collect and process huge amounts of information. In turn, the automation of analytical processes in business allows for more efficient, accurate and informed financial decisions. This paper discusses the key aspects and prospects for automating analytical processes in corporate finance. Research in this area shows that the use of digital technologies in the analysis of financial data opens up new horizons for forecasting key performance indicators of a company, such as profitability, profit and liquidity [1,2]. In particular, machine learning algorithms can automatically identify hidden patterns in data and predict changes in financial flows with high accuracy, which allows improving capital management, optimizing investment strategies and reducing financial risks. In addition, the use of artificial intelligence (AI) and big data also plays a significant role in financial analytics. By integrating data from various sources and processing it using automated systems or AI, companies can more accurately predict market
trends, identify hidden risks and determine the best moments for investment. These technologies significantly [3-6] reduce the time for analysis and minimize human errors, which in turn increases the company's competitiveness.
It should be noted that the automation of analytical processes and its implementation in the processes of processing financial data and reporting can significantly increase the efficiency and effectiveness of management decisions, which is especially important in the context of constantly changing market conditions and strict regulatory requirements. This, in turn, leads to increased confidence on the part of investors and other stakeholders in the current corporate governance system of the enterprise.
At the same time, despite the obvious advantages, the automation of the analysis process in corporate finance, risk assessment and creditworthiness faces the need to solve a number of problematic issues and challenges. The introduction of digital technologies requires significant investments in IT infrastructure and personnel training. Moreover, data security and privacy issues remain important, especially in the context of growing cyber threats.
Main part: Let us highlight the main problematic issues in the field of digitalization and automation of financial reporting analysis
1. Lack of a single data standard: In the process of digitalization of financial reporting, one of the key problems is the lack of unified data standards. Companies use different formats and structures to present financial information, which complicates automatic analysis and processing of data. This leads to additional integration costs and can create errors in data interpretation.
2. Limited interoperability of systems - there are various systems and platforms for automating financial reporting, and their integration can be difficult due to incompatibility of data formats, protocols and architectural solutions. Limited interoperability hinders the effective exchange of data between different departments and companies, and slows down the process of implementing digital technologies.
3. Data security issues: With the increase in the volume of data and its automatic processing, the risk of cyberattacks and leaks of confidential information increases. Data protection and ensuring its privacy remains a serious problem, especially in the context of using cloud technologies and remote access.
4. Resistance to changes in digitalization and lack of skills: The implementation of digital technologies requires significant changes in corporate culture and work processes. Resistance to change on the part of employees, as well as a shortage of specialists with the necessary skills and competencies, slow down the process of digitalization and automation of financial reporting analysis.
5. The issue of developing a common methodology for analyzing the financial condition of a company from the point of view of studying the dynamics of changes in its financial indicators, their "historical" analysis, and
obtaining the values of a complex indicator remains important. In this case, a complex indicator (CI) should be considered as a weighted sum of normalized values of the main financial ratios that characterize the state of the company. Such an indicator should combine several financial indicators into one numerical value for more convenient comparison with a similar indicator in other companies for the purpose of conducting a comparative analysis for making investment decisions.
Conclusion: The benefits that automation brings significantly exceed the costs in terms of reducing the labor and time costs of management for collecting, analyzing and preparing information for decision-making. It allows companies to more effectively manage their financial resources, reduce operating costs and minimize risks, which ultimately increases their competitiveness in the market.
References:
1. Jain, S., & Sharma, M.. "Data integration techniques for automated financial analysis: Challenges and opportunities". Journal of Financial Data Science.- 2021.
2. Lee, J., & Kim, H. "Machine learning-based frameworks for real-time financial data collection and normalization". Financial Innovation Journal. -2020
3. Wang, Y., & Zhang, L. "Automating financial statement analysis with machine learning". International Journal of Accounting Information Systems. -2022
4. Kuznetsov, A. V., and Smirnov, D. R. "Methods of automated analysis of financial statements: prospects and challenges". Accounting and analysis. -2021.
5. Rakhmatov, S. A. "Using machine learning algorithms in the analysis of financial statements of Uzbek enterprises". Economic Bulletin of Uzbekistan - 2021
6. M.T.Butaboev. Big data as the main resource of the digital economy. Bulletin of Kokand University. - ISSN: 2181-1695
