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Securities market automation from standards to self-learning machines: Current state and future perspectives

Author

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  • Ehrenfeld, Jonathan

    (Strategy Director, SWIFT, Belgium)

Abstract

Over the last two decades, key aspects of the financial industry have been automated to a substantial degree. While most progress in automation has come from traditional technologies, recent advances in machine learning, artificial intelligence and robotics are likely to accelerate the pace. In such a context, Distributed Ledger Technologies, Robo-Advisors and cognitive tools are creating a foundation for solving major problems faced by the industry. This paper provides an overview of the capabilities and limitations of these technologies and the challenges that await market participants who want to embrace and implement them. It draws attention to the importance of collaboration, governance, standards and market practice harmonisation in order to successfully deploy these technologies in a multi-party, globalised network environment.

Suggested Citation

  • Ehrenfeld, Jonathan, 2017. "Securities market automation from standards to self-learning machines: Current state and future perspectives," Journal of Securities Operations & Custody, Henry Stewart Publications, vol. 9(3), pages 245-251, July.
    • Handle: RePEc:aza:jsoc00:y:2017:v:9:i:3:p:245-251
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    More about this item

    Keywords

    securities; technology; artificial intelligence; distributed ledger technologies (DLTs); robotics; cognitive computing; standards;
    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
    • K22 - Law and Economics - - Regulation and Business Law - - - Business and Securities Law

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