Evaluating Big Data Strategies for Risk Management in Financial Institutions

Wu Xiaoli 

School of Finance, Renmin University of China, China 

Nguyen Bang Nong

Vietnam Academy of Social Sciences

Keywords: Risk Management, Big Data, Financial Institutions, Credit Risk, Market Risk, Operational Risk


Abstract

Financial institutions face numerous risks that can impact their operations and profitability. Managing these risks effectively is crucial for success. Big data strategies that leverage advanced analytics on large, diverse datasets can enhance risk management capabilities. This paper evaluates various big data approaches for improving risk management in financial institutions. It examines how big data strategies can be applied to major risk categories, including credit risk, market risk, liquidity risk, and operational risk. The paper discusses how predictive modelling, data mining, text analytics, and network analysis can provide deeper insights for credit risk management. Big data techniques like sentiment analysis, machine learning algorithms, and pattern detection are shown to strengthen capabilities for market risk measurement and monitoring. For liquidity risk, applications like granular cash flow forecasting, scenario modeling, text mining, and social media monitoring are assessed. Operational risk management can benefit from process mining, anomaly detection, external third-party data, and responsible employee misconduct surveillance. The advantages and limitations of different big data techniques are analysed through examples and data. The paper provides best practises for successfully implementing big data analytics for risk management. Strong executive sponsorship, specialised talent, technological infrastructure, developing a data-driven risk culture, ensuring model transparency, and aligning with risk strategy are critical success factors. Big data strategies must conform to privacy and ethical standards.  Big data can significantly enhance risk identification, measurement, and mitigation capabilities if deployed thoughtfully. This can help financial institutions make better risk decisions, avoid losses, comply with regulations, and gain a competitive advantage. But care must be taken to implement big data analytics responsibly and prudently.


Author Biography

Nguyen Bang Nong, Vietnam Academy of Social Sciences