IDEAS home Printed from https://ideas.repec.org/a/aza/jfc000/y2020v4i1p53-75.html
   My bibliography  Save this article

Target quality review of internal risk models and how to inspect them

Author

Listed:
  • Orgeldinger, Jörg

    (Economist and Data Scientist, Germany)

Abstract

The European Central Bank performs a targeted review of internal models with the objective of reducing the variability in risk-weighted assets (RWAs). This will be accomplished by harmonising practices and checking the compliance of Pillar 1 internal models of credit risk (CR), market risk (MR) and counterparty credit risk (CCR) with regular requirements. The paper will help assessment teams with guidance on which situations should trigger findings, covering a selection of mandatory key variables and allowing for additional tests to be performed. Specific attention will be paid to the different core banking systems and data sources that are used through to the (historical) risk database identified in the inspection. Although the fact that the information technology architecture and infrastructure for the credit rating systems are mode-specific, a simplified process will be outlined throughout the paper to illustrate the main process steps, systems and datasets. Targets are the retail and corporate small medium enterprises (SMEs) portfolios, including information based on personal experiences of the author. For the future, more sophisticated methods like artificial intelligence and machine learning, as described in literature, need to be found and applied.

Suggested Citation

  • Orgeldinger, Jörg, 2020. "Target quality review of internal risk models and how to inspect them," Journal of Financial Compliance, Henry Stewart Publications, vol. 4(1), pages 53-75, September.
  • Handle: RePEc:aza:jfc000:y:2020:v:4:i:1:p:53-75
    as

    Download full text from publisher

    File URL: https://hstalks.com/article/5906/download/
    Download Restriction: Requires a paid subscription for full access.

    File URL: https://hstalks.com/article/5906/
    Download Restriction: Requires a paid subscription for full access.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Keywords

    risk models; data quality; PD and LGD tests; market risk; collateral value;
    All these keywords.

    JEL classification:

    • G2 - Financial Economics - - Financial Institutions and Services
    • E5 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit
    • K2 - Law and Economics - - Regulation and Business Law

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aza:jfc000:y:2020:v:4:i:1:p:53-75. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Henry Stewart Talks (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.