Sarcouncil Journal of Economics and Business Management

Abstract: This paper explores how data analytics can be incorporated in financial risk management and thus serve as a basis for effective financial risk management decision support and financial system resilience. Traditional risk assessment methods are less effective in the face of growing volumes, velocity and complexity of data, especially in pinpointing new systemic risks, interconnections, market shocks, and institutional stress. The study examines the use of machine learning, real-time monitoring, stress testing and early-warning systems in the detection, assessment, and mitigation of credit, market, liquidity, operational, and systemic risks through descriptive, predictive and prescriptive analytics. It takes a conceptual and analytical approach, connecting the dots between data quality, analytical capability, regulatory technology, governance and institutional coordination and the effectiveness of risk-based supervision. The analysis emphasizes how integrated data platforms can help the regulators and financial institutions get timely and data-driven insights, enhancing their classification of risks, scenario analysis, supervisory action and policy making. The research also indicates substantial implementation challenges, such as data silos, model risk, algorithmic bias, cyber-security risks, lack of interpretability, privacy concerns, and lack of analytical skills. It claims that data analytics for success needs solid data-governance standards, explainable and transparent models, regulatory oversight, interoperable information systems, and constant validation of the analytical tools. The study suggests that if used responsibly, the use of advanced analytics can enhance the regulatory responsiveness; can enable proactive financial-risk management and can enhance the capacity of financial systems to absorb and recover from economic and financial shocks.