Analysis of Financial Reports in Companies Using Machine Learning

The article aims to develop new algorithms for the automated analysis of financial reports based on machine learning algorithms, which increase the efficiency and accuracy of converting financial information into a text form. In this context, special attention is paid to deep learning methods and neural networks that contribute to automating and analyzing financial reports and their further interpretation. The article examines the problems of generating text data from financial statements, describes the general characteristics of this process, and systematizes the technologies used to solve the task of developing text data and available methods of machine learning. Specific technologies of text generation using neural networks were analyzed, and the potential and prospects of machine learning in the creation of text data based on the analysis of financial reports were investigated. The process of developing a module intended for automated analysis of financial statements is described in detail, a technical task is created, which is necessary to solve the given task, and the structure and functionality of the developed module in the automated system are described. The result is a developed module for automated analysis of financial reports. Given that the module is created using Python, it can be easily integrated into different systems or function as an independent system, for example, a website or an application for a personal computer. The results of the developed automated module are demonstrated in the example of the analysis of financial reports of the companies Microsoft, Alphabet, and Apple.