Predictive Analytics in Financial Markets A Data Mining Perspective with AI Integration

Abstract

Financial markets have become too complex and volatile to the point that predictive analytics became essential for investors traders and financial institutions. Traditional financial forecasting models break down when identifying new patterns and trends since they base their analysis on both statistical models and historical data records. The research in Artificial Intelligence (AI), Machine Learning (ML) and Data Mining allows enhanced accurate and dynamic financial predictions through the uncovering of concealed patterns found in massive datasets. The research delivers an analysis of AI predictive analytics combined with data mining tools in financial market prediction as they operate through cloud platforms to process data instantly and execute decisions. Market trend prediction benefits from supervised and unsupervised learning algorithms and their main methods are Random Forest alongside Support Vector Machines (SVM) and Long Short-Term Memory (LSTM) networks as well as Gradient Boosting (XGBoost). NLP serves as an analytic tool to evaluate financial news with sentiment analysis and social media trends to add qualitative data in forecasting markets. The study evaluates the importance of cloud solutions for running scalable financial data assessments which let organizations effortlessly connect structured and unstructured information across different platforms. The research evaluates data reliability challenges as well as machine learning overfitting barriers besides defining limitations for real-time decision automation and explains market irregularities. A study compares AI-based analysis methods with traditional financial models like ARIMA together with GARCH in order to determine performance effectiveness.