Big Data Analytics and Its Role in Financial Risk Management

Abstract

The increasing complexity of global financial systems and the exponential growth of data have positioned Big Data Analytics (BDA) as a transformative tool in financial risk management. This study examines how BDA enhances the identification, prediction, and mitigation of diverse financial risks, including credit, market, operational, and systemic risks. Using a mixed-method approach, the research integrates quantitative analysis of credit risk datasets, fraud detection models, and volatility forecasts with qualitative insights from case studies of financial institutions and fintech firms adopting big data frameworks. The results show that BDA significantly improves predictive accuracy in credit default modeling, enhances the detection of fraudulent transactions, and strengthens stress testing through real-time integration of structured and unstructured data. Moreover, systemic risk indicators derived from big data network analysis demonstrate the potential of BDA to map contagion effects and provide early warnings to regulators and policymakers. However, the findings also highlight challenges related to algorithmic transparency, interpretability, data privacy, and disproportionate compliance costs faced by smaller firms. The discussion emphasizes that while BDA offers substantial benefits, its long-term impact will depend on robust governance frameworks, ethical safeguards, and regulatory alignment. Overall, the study concludes that BDA represents not only a technological advancement but also a paradigm shift in financial risk management, redefining how risks are understood and managed across the financial ecosystem.