Centre for Actuarial and Financial Big Data Analytics (CAFBDA)

Bond University

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Computer monitors showing financial charts

Through the use of Big Data Analytics, CAFBDA aims to produces research contributions that lead to improved social and economic outcomes through better informed decision making. CAFBDA focuses on (i) analysing very large data sets to reveal valuable information and make meaningful conclusions and recommendations, and (ii) using cutting-edge big data techniques on data sets of varying sizes to reveal important new insights. A cross-disciplinary approach is critical to Big Data solutions because the field is at the intersection of numerous traditional disciplines. Consequently, members of the Centre have expertise in big data from a variety of disciplines: actuarial science, finance, statistics, economics and informatics. Centre members also have a proven track-record applying their expertise in application domains including fraud detection, medicine, demography, finance and climatology to list a few.

Website https://bond.edu.au/researchers/research-strengths/faculty-research-centres/centre-actuarial-and-financial-big-data

Key science sectors

Strengths and capabilities

  • Fraud detection models
  • Predictive analytics in business and Commerce
  • Big data analytics in health
  • Volatility modelling and other financial analytics
  • Financial literacy modelling
  • High-frequency trading analysis
  • Time series econometrics
  • Retirement planning
  • Analysis of financial misconduct
  • Machine learning and statistical learning

Facilities and major equipment

  • Triple-Screen Computers connected to Virtual Machine Cloud Technology
  • Dedicated Bloomberg Terminals

Number of research staff

Upto 20 research staff

Address

Building 2, 14 University Drive, Bond University, Robina, Queensland, 4226

Lead researchers

  • Professor Terry O'Neill—Chief Investigator on ARC Grant funded project for "The Importance of Being Politically Connected"
  • Dr Adrian Gepp—American Accounting Association Best Dissertation Award in Forensic Accounting Section

Organisation type

  • University Research Centre

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