IDEAS home Printed from https://ideas.repec.org/h/spr/lnichp/978-3-030-47355-6_10.html
   My bibliography  Save this book chapter

Improving Invoice Allocation in Accounting—An Account Recommender Case Study Applying Machine Learning

In: Digital Business Transformation

Author

Listed:
  • Markus Esswein

    (University of Duisburg-Essen)

  • Joerg H. Mayer

    (Darmstadt University of Technology)

  • Diana Sedneva

    (University of Duisburg-Essen)

  • Daniel Pagels
  • Jean-Paul Albers

Abstract

Covering transactions between buyers and sellers, invoices are essential. However, not all invoices can be directly matched to a purchase order due to missing order numbers, differences in terms of the invoice amount, quantity and/or quality. Following design science research (DSR) in information systems (IS), the objective of this article is to propose a new kind of an account recommender by applying machine learning. We take a chemical company as our case study and build a prototype that today handles more than 500,000 invoices without purchase order per year more accurately and efficiently than manual work did before. Finally, we propose five design guidelines to drive future research as follows: (1) Truly understand the business need; (2) More data can only get you so far; (3) Give the machine a good starting position; (4) Computing power is crucial; (5) Do not burn your bridges yet (manual intervention).

Suggested Citation

  • Markus Esswein & Joerg H. Mayer & Diana Sedneva & Daniel Pagels & Jean-Paul Albers, 2020. "Improving Invoice Allocation in Accounting—An Account Recommender Case Study Applying Machine Learning," Lecture Notes in Information Systems and Organization, in: Rocco Agrifoglio & Rita Lamboglia & Daniela Mancini & Francesca Ricciardi (ed.), Digital Business Transformation, pages 137-153, Springer.
  • Handle: RePEc:spr:lnichp:978-3-030-47355-6_10
    DOI: 10.1007/978-3-030-47355-6_10
    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 search for a similarly titled item that would be available.

    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:lnichp:978-3-030-47355-6_10. 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.