Introduction to the Minitrack on Machine Learning and Predictive Analytics in Accounting, Finance and Management

Peter Sarlin, Jozsef Mezei

    Research output: Chapter in Book/Conference proceedingConference contributionScientificpeer-review

    Abstract

    The use of advanced statistical models, predictive
    analytics and machine learning have been present in
    the fields of accounting, finance and management for
    several decades. However, recent years have seen an
    ever increasingly growing trend of utilizing these
    approaches, as a result of the rapid evolution of related
    technologies mathematical algorithms. These
    developments include, but are not limited to: (i) the
    widespread availability of large amount of data,
    specifically data streams in new domains, (ii) the
    commoditization of advanced machine learning (ML)
    and artificial intelligence (AI) algorithms, such as deep
    learning in open source programming packages, (iii)
    the decrease in costs and complexity of performing
    computationally extensive modelling. These trends are
    observable in both academia and industry: for
    organizations, using AI and ML is not a source of
    competitive advantage anymore, but rather a necessity
    to remain profitable. The minitrack aims at showcasing
    some of the most interesting application domains and
    novel machine learning techniques applied to both
    structured and unstructured data sources.
    Original languageEnglish
    Title of host publicationProceedings of the 53rd Hawaii International Conference on System Sciences
    PublisherUniversity of Hawai'i at Manoa
    ISBN (Print)978-0-9981331-3-3
    Publication statusPublished - 2020
    MoE publication typeA4 Article in a conference publication
    EventHAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES - Hawaii International Conference on System Sciences
    Duration: 7 Jan 202010 Jan 2020

    Conference

    ConferenceHAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES
    Period07/01/2010/01/20

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