This white paper explores the dynamics between data, models, and teams within financial services organisations. Against a backdrop of regulatory and technological change, it provides insights into drivers of model development, techniques to make sense of big data, and the need for increased collaboration between departments.

Key Findings

  • 37% of survey participants consider risk management to be the main driver behind their model development.
  • 68% of respondents identify a lack of agility to respond to market changes as the biggest opportunity cost to the business of slow model development.
  • 31% report that their institution has implemented a project to react to a specific big data challenge.
  • Respondents identify "data quality" (36%) and "creating effective models" (36%) as the greatest challenges associated with big data in financial services.
  • More than half of the professionals have concerns about machine learning. For 45% of them, these concerns centre on over-fitting and fear of the implications of a black box approach, and 40% want to learn more about machine learning.

Current Drivers of Model Development

Drivers of current model development

The survey of 78 professionals was conducted at the MATLAB Computational Finance Conference in London in June 2014. Respondents span the buy-side and sell-side, in addition to consultants and representatives from central banks.

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