Practical Directions for the Integration of Financial Big Data and Quantitative Risk Control - Hong, Xun (International Journal of Public Health and Medical Research, 2026)
With the all-round development of digital technology in recent years, so too have people’s risk-bearing habits changed. Systematically explore the practical directions of applying large-scale financial big data and high-end quantitative risk control frameworks in this paper. Utilise a large number of organised and unorganised alternative data to improve the accuracy of prediction and operation. Introduce new kinds of high-performance, frequently updated quantitative algorithms and move away from the old credit-scoring system. The first few practical applications are shown below: real-time fraud detection, dynamic credit assessment and comprehensive market risk monitoring. This paper will also discuss the issues that have arisen due to such a combination, such as data privacy laws, algorithmic transparency, and a large-scale computational infrastructure. In short, now that large-scale big data analysis and high-end quantitative methods have been introduced, all-encompassing risk management systems for large banks can be constructed to face the turbulence of the economy, credit losses due to defaults can be reduced, and continuous profit generation in the era of deep globalisation can be achieved.