Abstract
The integration of artificial intelligence (AI) and big data analytics into financial systems has paved the way for intelligent decision-making tools. This paper introduces a Smart Financial Decision-Making System designed to support individuals and institutions in managing investment, credit, and savings decisions. Utilizing machine learning algorithms and predictive modeling, the system offers real-time suggestions based on historical data, behavioral patterns, and economic indicators. The model is trained on diversified financial datasets to ensure accuracy, scalability, and contextual adaptation. Our findings indicate that the smart system improves financial outcomes on average across multiple user groups when compared to traditional advisory methods. We conclude by discussing future integration opportunities and challenges in deploying such AI-driven solutions. In addition to financial recommendation capabilities, the system is also designed to detect early signs of crises, fraudulent behavior, and abrupt market shifts. By integrating Business Process Model and Notation (BPMN), the system can automatically respond to anomalies—such as reallocating funds or notifying relevant departments. It leverages time series analysis and over 50 years of historical asset data, including stock and commodity prices, to enhance the reliability of forecasts and support strategic financial planning.