Big Data Analytics and Its Impact on Risk Management in Financial Investments

Abstract

Today, the growth of technology and staying ahead of competitors heavily rely on Financial Information Systems (FIS) and Big Data Analytics (BDA). Increasingly, big data is being used to extract valuable insights from large datasets. In sectors such as e-commerce, data analytics tools improve product recommendations and operational efficiency. Internet businesses often turn to Flink to efficiently manage their vast data centers. This study examines the technological revolution through the IBMQSD framework, distributed computing, and deep learning methods for detailed financial analyses such as stock market forecasting and risk management. It starts with data cleaning and standardization, then uses domain-specific analysis to extract key features related to transportation, HR analytics, and energy trends. Regression Modeling uses tools like the Phillips-Perron test for financial behavior analysis. The Deep Learning Model uses Long Short-Term Memory (LSTM) and Conventional Neural Network (CNN) for financial risk prediction, while Econometric Modeling analyzes investment-risk relationships for proactive risk mitigation and portfolio optimization. The High-Resolution Imaging Process applies complex algorithms for imaging, and Big Data Processing Platforms, such as Hadoop and Swift, enable fast, secure data management. Data integrity is maintained through reliable storage solutions with the latest encryption features. The result indicates accuracy, loss, data speed and write, resource utilization, precision, recall, and F1-score.