• DocumentCode
    3722503
  • Title

    Meta Meta-Analytics for Risk Forecast Using Big Data Meta-Regression in Financial Industry

  • Author

    Hevel Jean-Baptiste;Meikang Qiu;Keke Gai;Lixin Tao

  • Author_Institution
    Dept. of Comput. Sci., Pace Univ., New York, NY, USA
  • fYear
    2015
  • Firstpage
    272
  • Lastpage
    277
  • Abstract
    The growing trend of the e-banking has driven the implementations of big data in financial industry. Data analytic is considered one of the most critical aspects in current economic development, which is broadly accepted in various financial domains, such as risk forecast and risk management. However, gaining an accurate risk prediction is still a challenging issue for current financial service institutions and the hazards can be caused in various perspectives. This paper proposes an approach using meta meta-analytics for risks forecast in big data. The proposed model is Meta Meta-Analytics Risk Forecast Model (MMA-RFM) with a crucial algorithm Regression with Meta Meta-Analytics Algorithm (RMMA). The proposed schema has been examined by the experimental evaluation in which it performs an optimized performance.
  • Keywords
    "Analytical models","Big data","Mathematical model","Prediction algorithms","Risk management","Predictive models","Reliability"
  • Publisher
    ieee
  • Conference_Titel
    Cyber Security and Cloud Computing (CSCloud), 2015 IEEE 2nd International Conference on
  • Type

    conf

  • DOI
    10.1109/CSCloud.2015.69
  • Filename
    7371493