The Role of Artificial Intelligence in Financial Analysis and Forecasting: Using Data and Algorithms

Abstract

Introduction: This study explores the role of Artificial Intelligence (AI) in financial analysis and forecasting, focusing on its application in the banking sector. AI's ability to process large datasets and enhance prediction accuracy is critical for improving financial decision-making, particularly in forecasting stock prices, currency rates, and market trends. Methods: The research employed traditional statistical methods such as ARIMA models and machine learning algorithms like Gradient Boosting Machines and Random Forests. These methods were applied to financial data sets to assess the impact of AI on forecasting accuracy and risk assessment. Data preprocessing and model training were conducted using R statistical software. Results: Integrating AI models improved forecasting accuracy by 30% compared to traditional methods, and risk assessment accuracy increased by 20%. Gradient Boosting Machines outperformed other models in identifying investment portfolio risks, while Random Forests provided robust predictions of trading volumes. Conclusions: AI has the potential to revolutionize financial analysis by increasing the efficiency and accuracy of forecasts. However, data privacy, algorithmic bias, and ethical concerns must be addressed to ensure fair and responsible AI use in finance. Collaboration among researchers, financial experts, and policymakers is essential for maximizing AI's benefits while mitigating risks