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Advances in Risk Analysis with Big Data.

Tsan-Ming Choi1, James H Lambert2

  • 1The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.

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Summary
This summary is machine-generated.

Big data analytics are revolutionizing risk analysis across various sectors. Leveraging vast datasets enhances decision-making, reduces risks, and identifies future research directions in fields like finance and transportation.

Keywords:
Big datadata analyticsrisk analysisrisk assessmentrisk communicationrisk managementsecurity

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Area of Science:

  • Data Science
  • Risk Management
  • Information Technology

Background:

  • Organizations now access unprecedented data volumes and varieties via cloud computing, IoT, and social media.
  • Existing risk analysis methods are evolving to incorporate these big data capabilities.
  • Real-world applications, such as traffic safety and financial decision-making, demonstrate the value of big data in risk assessment.

Purpose of the Study:

  • To define the implications of big data for risk analysis.
  • To review recent advancements in risk analysis utilizing big data.
  • To highlight key research articles within a special issue on this topic.

Main Methods:

  • Conceptual review and synthesis of big data applications in risk analysis.
  • Identification and introduction of relevant research papers from a special issue.
  • Discussion of emerging trends and future research opportunities.

Main Results:

  • Big data offers transformative potential for risk analysis across diverse domains.
  • Specific examples illustrate successful big data integration in transportation and finance.
  • The study identifies key areas of progress and future research needs.

Conclusions:

  • Big data analytics are crucial for advancing modern risk analysis methodologies.
  • Continued research is essential to fully exploit big data's potential in risk management.
  • Future opportunities lie in developing novel analytical techniques and interdisciplinary applications.