Big Data in Financial Risk Management: Predictive Modeling, Real-Time Assessment and Emerging Challenges

The fast development of big data technologies has greatly changed how financial risk is managed, helping institutions make quicker and more accurate decisions based on data. This paper looks at how big data is used in financial risk management, focusing on three main areas: predictive modelling, real-time risk assessment, and ways to deal with new challenges. Predictive modelling uses machine learning to predict risks like market changes, lack of liquidity, and credit problems, giving companies tools to act before issues happen. Real-time assessment systems, powered by streaming analytics, help spot and stop risks such as fraud and system breakdowns before they get worse. The paper also explores new challenges in using big data, including problems with data quality and how to combine different data sources, making models easier to understand, following regulations, dealing with cyber threats, and finding enough skilled workers in advanced analytics. Future trends like quantum computing, blockchain, explainable AI, and using alternative data such as satellite images and ESG metrics are also discussed for their possible impact on financial risk management. The results show that while big data can greatly improve resilience and efficiency, its proper use needs a balance between innovation and good governance, transparency, and ethics. By handling these challenges, financial institutions can better predict risks, stay compliant, and build strong frameworks for long-term growth in a data-driven world.