IDEAS home Printed from https://ideas.repec.org/a/jbh/ijsrcs/v11y2025i2id1365.html

Transforming Financial Operations: The Impact of AI-Driven Invoice Processing Across Industries

Author

Listed:
  • Koteswara Rao Yarlagadda

Abstract

The integration of Artificial Intelligence in financial operations has revolutionized traditional invoice processing across multiple industries, introducing unprecedented levels of efficiency and accuracy. This transformation encompasses several key domains: procure-to-pay automation, supply chain integration, legal operations enhancement, compliance management, and operational excellence. AI-powered solutions fundamentally alter how organizations handle document processing, data validation, and workflow management. Implementing machine learning algorithms and advanced OCR technologies has enabled organizations to substantially improve processing speed, cost reduction, and error prevention. These technological advancements have benefited the financial services sector, where AI-driven systems have enhanced compliance monitoring, risk management, and decision-making capabilities. The adoption of microservices-based architectures and intelligent validation frameworks has established new benchmarks in operational efficiency while significantly reducing manual intervention requirements.

Suggested Citation

  • Koteswara Rao Yarlagadda, 2025. "Transforming Financial Operations: The Impact of AI-Driven Invoice Processing Across Industries," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11(2), pages 3181-3190, March.
    • Handle: RePEc:jbh:ijsrcs:v11:y2025:i2:id:1365
    DOI: 10.32628/CSEIT25112794
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT25112794
    as

    Download full text from publisher

    File URL: https://ijsrcseit.com/home/article/view/CSEIT25112794
    File Function: Article URL
    Download Restriction: no
    File URL: https://ijsrcseit.com/home/article/download/CSEIT25112794/CSEIT25112794
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.32628/CSEIT25112794?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:jbh:ijsrcs:v11:y2025:i2:id:1365. 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: Pankaj Sharma (USA) (email available below). General contact details of provider: https://ijsrcseit.com/home .

    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.