Financial risk assessment and prediction have become critical components in modern banking, investment management, insurance, and financial decision-making systems. The increasing volume of digital financial transactions and market complexity has made traditional risk analysis techniques insufficient for identifying emerging financial threats and uncertainties. Machine learning and big data technologies provide intelligent, scalable, and data-driven solutions for improving financial risk management processes. This study examines the application of machine learning algorithms and big data analytics in predicting and assessing financial risks across various financial sectors. The proposed framework utilizes big data technologies to process large-scale structured and unstructured financial datasets while employing machine learning models such as Logistic Regression, Decision Trees, Random Forest, Support Vector Machines, and Deep Learning techniques for predictive analysis. Big data platforms enhance real-time data collection, storage, processing, and analytical efficiency, enabling financial institutions to make informed decisions. The research also discusses data preprocessing, feature engineering, predictive modeling, and risk evaluation methods used in intelligent financial systems. Furthermore, the study highlights the advantages and challenges associated with machine learning-based financial risk assessment, including scalability, accuracy, data privacy, computational complexity, and interpretability issues. The findings indicate that integrating machine learning with big data technologies significantly improves financial risk prediction accuracy and supports effective financial decision-making.
