Applying artificial intelligence in finance and asset management: A discussion of status quo and the way forward

Juergen Dr. Rahmel ()
Additional contact information
Juergen Dr. Rahmel: HSBC Germany, Postal: Koenigsallee 21/23, 40212 Duesseldorf

Journal of Financial Transformation, 2020, vol. 51, 67-74

Abstract: Artificial intelligence (AI) and machine learning (ML) are gaining more and more traction in finance and asset management. But AI/ML is a complex tool that requires specific skills to be created, trained, and interpreted well for a given task. In this paper, we discuss some of the context parameters to be considered in order to apply AI beneficially in financial settings. We explore a matrix of use-cases, following the lifecycle of asset management and structured by the type of underlying AI technology. As AI requires human setup and interpretation, we briefly review the role of us “humans-in-the-loop” of AI implementations. Finally, the emerging field of asset tokenization promises to disrupt the conventional markets and market practices, opening up for a new field of AI applications to tackle the new way of trading and servicing securities. The AI game is on in asset management. Not to play is not an option.

Keywords: Artificial Intelligence; Risk; Risk Mining; Asset Tokenization; Explainability (search for similar items in EconPapers)
JEL-codes: G15 G21 (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ris:jofitr:1647

Access Statistics for this article

Journal of Financial Transformation is currently edited by Prof. Shahin Shojai

More articles in Journal of Financial Transformation from Capco Institute 77 Water Street, 10th Floor, New York NY 10005.
Bibliographic data for series maintained by Prof. Shahin Shojai ( this e-mail address is bad, please contact ).