IDEAS home Printed from https://ideas.repec.org/a/vra/journl/v11y2022i2p97-104.html
   My bibliography  Save this article

Artificial Intelligence - a Key Success Factor for Wealth Management Industry

Author

Listed:
  • Plamen Dzhaparov

    (University of Economics - Varna, Varna, Bulgaria)

Abstract

The Private Banking & Wealth Management (PWM) industry is generally seen as embodying traditional, old-fashioned and even archaic values. Upheld for centuries, its business model, which is based on intensive, comprehensive and discreet personal interactions between financial advisors and wealthy clients, is put to the test today. In today's dynamic and highly connected world, a large number of HNWIs (High Net Worth Individuals) want faster and more convenient value propositions and a cutting-edge digital experience - a trend that the pandemic has amplified many times over. In order to meet the increased expectations of this clientele, private banks and other institutions in the sector are increasingly investing in a number of new technologies and tools, artificial intelligence (AI) taking a leading place among them. In addition to enabling a more complete and qualitative satisfaction of user needs, AI promises benefits for PWM companies in a number of other areas: risk management, compliance, cost reduction, etc.

Suggested Citation

  • Plamen Dzhaparov, 2022. "Artificial Intelligence - a Key Success Factor for Wealth Management Industry," Izvestia Journal of the Union of Scientists - Varna. Economic Sciences Series, Union of Scientists - Varna, Economic Sciences Section, vol. 11(2), pages 97-104, August.
  • Handle: RePEc:vra:journl:v:11:y:2022:i:2:p:97-104
    as

    Download full text from publisher

    File URL: http://su-varna.org/journal/IJUSV-ESS/2022.11.2/97-104.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Krassimira Naydenova, 2018. "Built-In Problems in the New European Regulations for the Bulgarian Capital Market," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 5, pages 106-134.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      More about this item

      Keywords

      Artificial Intelligence; Machine learning; Wealth Management; Private Banking.;
      All these keywords.

      JEL classification:

      • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
      • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General

      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:vra:journl:v:11:y:2022:i:2:p:97-104. 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.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Pavel Petrov (email available below). General contact details of provider: https://edirc.repec.org/data/uevecea.html .

      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.