The Impact Of Using Machine Learning On Improving The Quality Of Financial Reports And Predicting Financial Distress, And Its Reflection On Unit Value

This research aims to demonstrate the role of higher education in accounting by improving the quality of financial reports prepared by economic units, the role of machine learning in predicting future financial distress and its impact on unit value, and the effect of using machine learning on unit value through improved report quality and predictive financial distress. To achieve the research objectives, solve the problems, and prove or disprove the research hypotheses, the researcher analyzed the financial reports of the units in the research sample from 2021 to 2024. Using quality improvement equations, predicting financial distress, and measuring unit value based on economic value added measures, as well as using statistical programs, the researcher reached a set of conclusions and recommendations, including that machine learning helped improve the quality of financial reports through the large volume of data and information that will be dealt with and presented in a timely manner, and that the use of machine learning also contributes to helping management and those interested in identifying and analyzing predicting financial distress by helping in analyzing a large set of data and information. The research identifies information that influences users and determines their ability to predict financial distress. Key recommendations include the need for management to enhance staff's machine learning skills to improve data and information processing capabilities, thereby improving the quality of financial reporting. Furthermore, it is essential to train decision-makers in using modern methods to identify the unit's strengths, weaknesses, and risks that could contribute to future financial difficulties.