Application and Challenges of Data Science in Financial Risk Management
Shenghua Huang
University of California
DOI: https://doi.org/10.59429/bam.v6i3.7356
Keywords: data science; financial risk management; big data processing; advanced analytical models
Abstract
This paper delves into the extensive application of data science in financial risk management and the challenges it faces. Data science plays an increasingly crucial role in the financial sector due to its powerful capabilities in big data processing, advanced analytical models, and real-time dynamics. The ability to handle massive datasets through advanced storage technologies and distributed computing frameworks provides a solid foundation for financial institutions by enabling rapid data loading, storage, cleansing, integration, and preprocessing. Advanced analytical models leveraging machine learning, deep learning, and other cuttingedge technologies automatically extract valuable insights and patterns from data, significantly enhancing the accuracy and efficiency of risk management. The real-time and dynamic nature of data science facilitates realtime monitoring and dynamic adjustments in risk management through techniques such as real-time data stream analysis and online learning. However, the application of data science in financial risk management also faces challenges such as data security, privacy protection, and model interpretability.
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