AIBased Predictive Analytics in Corporate Financial Management

Predictive analytics using Artificial Intelligence (AI) to enhance financial management of corporations with a special emphasis on increased forecasting accuracy, risk evaluation, and decision-making speed. The study also uses a systematic approach that includes data gathering, data pre-processing, feature engineering, and application of machine learning algorithms, including Linear Regression, Support Vector machine (SVM), and Extreme Gradient Boosting (XGBoost). Financial and macroeconomic variables are examined in order to come up with predictive models who can be used to recognize patterns and trends of corporate financial data. The findings show that AI-based models, in the XGBoost model, are far much better in terms of prediction accuracy and reliability as compared to traditional statistical models. Important determinations of financial performance as displayed by the feature importance analysis include revenue growth, capital structure, and operational efficiency. Also, the paper offers the importance of the predictive analytics in improving risk management such as giving a warning of the possible financial breakdown. The results imply that predictive analytics can be used to change the way companies run their finances in the future by offering them real-time data, automation, and forward-looking. Although there are issues associated with the quality of the data and the transparency of the models, the implementation of the AI technologies can have significant advantages to the organizations aiming to enhance their financial results and to remain competitive in the fast-changing environment of their businesses.