Effective Risk Data Aggregation and Risk Reporting

The recent financial crisis revealed that many banks, including global systemically important banks (G-SIBs), were unable to aggregate risk exposures fully and quickly. This meant that banks' ability to take risk decisions in a timely fashion was seriously impaired with wide-ranging consequences for individual banks and the stability of the financial system as a whole.

Here, risk data aggregation means defining, gathering and processing risk data according to the bank’s risk reporting requirements to enable the bank to measure its performance against its risk tolerance/appetite. This includes sorting, merging or breaking down sets of data.

The Basel Committee's has proposed the principles for effective risk data aggregation and risk reporting. These principles are intended to strengthen banks' risk management capabilities. This should ensure banks are better prepared to cope with stress, hence reducing the potential recourse to tax-payers.

[gview file="http://www.bis.org/publ/bcbs222.pdf" save="1"]

Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

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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.