Integrating Big Data Analytics with Financial Risk Management: Challenges and Opportunities

The potential for improved risk assessment, fraud detection, and compliance monitoring are highlighted in this paper, which explores the integration of big data analytics in financial risk management. Insights gained from big data analytics may help financial organisations spot possible financial crimes, manage credit risk, and anticipate market volatility in real time. The article delves into the methodology and a tool used for data-driven risk management, while also shedding light on the obstacles, including issues with data quality, regulatory compliance, and the need of a strong data infrastructure. We also showcase case studies of financial organisations that have successfully used big data analytics to decrease risks, along with future inventive potential. A key barrier in improving decision-making processes and decreasing the risk-profile of the financial industry has emerged with the integration of BDA into FRM. As financial institutions navigate a constantly shifting landscape characterised by regulatory constraints, market uncertainty, and evolving consumer habits, BDA adoption presents both significant opportunities and challenges. This study delves into the topic of BDA and its potential applications in FRM, real-time decision-making, predictive analytics, and risk assessment. Regulatory compliance, analytics algorithm complexity, data quality and administration, and other challenges are among the many that arise when integrating BDA into existing FRM systems. Additionally, it is inherent to financial organisations to face challenges when attempting to use the vast amounts of data, structured and unstructured, generated across many platforms. This research highlights the need of building strong data governance systems and using creative analytical approaches to tackle problems. Financial institutions may get the most out of BDA if they follow these steps, which will help them reduce risk, plan ahead, and take advantage of possibilities in emerging markets. At the conclusion of the paper, we provide practitioners some recommendations for effective BDA use in FRM and identify several research gaps.