IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-031-64273-9_23.html
   My bibliography  Save this book chapter

A Tweet Data Analysis for Detecting Emerging Operational Risks

In: Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author

Listed:
  • Davide Di Vincenzo

    (UniCredit S.p.A, Group Non Financial Risks)

  • Francesca Greselin

    (Univ. Milano Bicocca, Department of Statistics and Quantitative Methods)

  • Fabio Piacenza

    (UniCredit S.p.A, Group Non Financial Risks
    Univ. Milano Bicocca, Department of Statistics and Quantitative Methods)

  • Ričardas Zitikis

    (Western University, School of Mathematical and Statistical Sciences)

Abstract

Operational risk (OpRisk) is emerging as a crucial non financial consideration with widespread implications for financial institutions. Shifting away from traditional regulatory tasks, including data collection, capital requirement calculations, and report generation for managerial decisions, OpRisk functions are now adopting proactive strategies to prevent or mitigate risks. The integration of Artificial Intelligence techniques, increasingly essential for managerial insights, is utilized to glean additional information from data. This study propels the utilization of text analysis techniques in the context of OpRisk. A pioneering dimension involves examining pertinent tweet content from social media X for the continuous monitoring of the evolving risk landscape, aiming to identify early warnings about new types of potentially risky events.

Suggested Citation

  • Davide Di Vincenzo & Francesca Greselin & Fabio Piacenza & Ričardas Zitikis, 2024. "A Tweet Data Analysis for Detecting Emerging Operational Risks," Springer Books, in: Marco Corazza & Frédéric Gannon & Florence Legros & Claudio Pizzi & Vincent Touzé (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 136-142, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-64273-9_23
    DOI: 10.1007/978-3-031-64273-9_23
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    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:spr:sprchp:978-3-031-64273-9_23. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.