Artificial Intelligence On The Administration Of Financial Markets

This research work analyzes the use of Artificial Intelligence (AI) in the administration of financial markets through an observation of its potential to improve efficiency, accuracy, and decision-making. It employs four machine learning algorithms: Decision Trees, Support Vector Machines, Neural Networks, and Ensemble Methods, for analyzing historical market data and making predictions for future financial trends. Our conclusions are that the Neural Network algorithm had the best performance at this task with a 92.4% accuracy rate on stock price prediction. The next was Ensemble Methods with an accuracy rate of 89.7%. The Decision Tree model showed precision at 85.3%, and Support Vector Machines achieved even an accuracy level at 84.5%. This study mainly focused on how the criteria for selecting the right models of AI based on relevant finance objectives and conditions may actually demonstrate the risk-aversion capability based on market volatility from AI technologies. The results also demonstrate how AI can handle big datasets by converting the data into actionable insights, thereby making operational processes in financial institutions more fluid. This contributes to the understanding of the role of AI in modern finance and comes with incentives for further understanding beyond ethics and the potential advancement of application across segments.”