Problem:

The financial turmoil in the last few years have led and are still leading to strict regulatory requirements for Risk Management in Banking. For assessing the impacts of various economic and political worst case scenarios banks are forced to establish a stress-testing framework for their overall risk position including credit risk, market risk, liquidity risk, operational risk and other risks (such as: strategic risk, reputational risk, equity risk and earnings risk). A typical stress-testing scenario is to assess the impacts of a significant rise in interest rates on the overall risk position of a bank. However, banks face various challenges when trying to establish a stress-testing framework:

  1. The risk calculation for each risk type (credit risk, market risk, etc.) takes place in different risk systems (often where there is a lack of integration).
  2. Due to the mass of data, information in analytical risk management systems must be aggregated to sustain performance for theses systems. This forces the developers to make decisions about what the end user wants to know.
  3. Proactive steering on base of stress testing results impeded through long calculation processing time.

Solution:

The use of In-Memory technology delivered by fraXses can help banks to face regulatory challenges and to improve risk management and risk steering capabilities.

fraXses offers the capabilities to

  • Break the boundaries between different systems for different risk types – share all risk type data on one integrated fraXses Database
  • Handle mass risk data without the need to aggregate – detailed analysis of stress testing results
  • Enable real-time analysis of stress testing results – faster and more precise risk measure can be taken

Benefits:

  • Remove the need for aggregation of data
    • Thus enable self-service
  • Enabled stress testing for the whole banking portfolio
  • Improved contingency plans for capital management initiatives
  • Revealed interdependencies between different risk types
  • Enable the users to analyse the data they way they see fit with the self-service capabilities of fraXses.