Journal of Data Mining and Knowledge Engineering ISSN: 3139-5341 (Online)

Abstract

Financial markets generate huge amount of structured and unstructured data every second. The increasing complexity of global financial systems makes traditional statistical approaches less sufficient for accurate forecasting. Predictive analytics combined with data mining techniques has emerged as an effective solution to analyze financial datasets and generate actionable insights. This paper presents a comprehensive review of predictive analytics methods applied to financial data using data mining approaches. Various techniques such as classification, regression, clustering, neural networks, support vector machines, and ensemble learning are discussed. Applications including stock price prediction, credit risk assessment, fraud detection, and portfolio management are examined. The study also compares different algorithms based on accuracy, interpretability, and computational cost. Challenges such as data volatility, overfitting, and model interpretability are highlighted. The paper concludes that while predictive analytics significantly enhances financial decision making, proper feature engineering and risk management are still essential for reliable forecasting.

Keywords: Predictive Analytics, Financial Data Mining, Stock Market Prediction, Credit Risk Analysis, Machine Learning in Finance, Fraud Detection, Time Series Forecasting, Big Data Analytics.