Observations and Developments in Risk Appetite Frameworks and IT Infrastructure

This reading is a part of the syllabus for FRM Part 2 Exam in the section ‘Operational and Integrated Risk Management’.

The report summarizes the efforts of two SSG working groups to assess the progress that financial institutions have made in developing risk appetite frameworks and building robust information technology infrastructures.

The observations in this report indicate that while most firms have made progress in developing risk appetite frameworks and begun multiyear projects to improve IT infrastructure, financial institutions have considerably more work to do in order to strengthen these practices. In particular, it is observed that aggregation of risk data remains a challenge for institutions, despite its criticality to strategic planning, decision making, and risk management.

[gview file="http://www.fsa.gov.uk/pubs/other/ssg\_2010.pdf" save=1]

Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book includes PDFs, explanations, instructions, data files, and R code for all examples.

Get the Bundle for $29 (Regular $57)
JOIN 30,000 DATA PROFESSIONALS

Free Guides - Getting Started with R and Python

Enter your name and email address below and we will email you the guides for R programming and Python.

Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.