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"]

Finance Train Premium
Accelerate your finance career with cutting-edge data skills.
Join Finance Train Premium for unlimited access to a growing library of ebooks, projects and code examples covering financial modeling, data analysis, data science, machine learning, algorithmic trading strategies, and more applied to real-world finance scenarios.
I WANT TO JOIN
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.

Saylient AI Logo

Accelerate your finance career with cutting-edge data skills.

Join Finance Train Premium for unlimited access to a growing library of ebooks, projects and code examples covering financial modeling, data analysis, data science, machine learning, algorithmic trading strategies, and more applied to real-world finance scenarios.