IDEAS home Printed from https://ideas.repec.org/a/kap/apfinm/v32y2025i3d10.1007_s10690-024-09470-x.html
   My bibliography  Save this article

Loan Frauds in the Indian Banking Industry: A New Approach to Fraud Prevention Using Natural Language Processing (NLP)

Author

Listed:
  • Smita Roy Trivedi

    (NIBM Campus, NIBM PO)

  • Dipali Krishnakumar

    (NIBM Campus, NIBM PO)

  • Richa Verma Bajaj

    (NIBM Campus, NIBM PO)

Abstract

Context/Motivation Non-identification of Early Warning Signals (EWS) or Red Flag Indicators (RFI) on time is an important reason behind the rising trend in credit frauds in the Indian banking industry. Literature suggests that for effective identification of EWS, it is not enough to have a set of EWS but it is essential to rank them and highlight the most important ones to look out for. In the Indian context, there is no ranking of EWS for credit frauds, which is a serious challenge to practicing bankers. Design/Methodology This paper therefore ranks the EWS for credit frauds using a novel Natural Language processing (NLP) approach and further analyses the most important EWS impacting frauds. Findings This paper finds that the presence of early warning signals from Diversion of Funds, Inter-Group/Concentration of Transactions, Issues in Primary/Collateral Security (COLSEC), makes it very likely that frauds would be in the high-value category. Originality First, this is the first Indian study which develops a ranking or scoring of either EWS/RFI on the basis of NLP tools. Secondly, we use a unique methodology for identification of EWS based on NLP techniques, which makes possible the harnessing of a rich source of data, not so far attempted.

Suggested Citation

  • Smita Roy Trivedi & Dipali Krishnakumar & Richa Verma Bajaj, 2025. "Loan Frauds in the Indian Banking Industry: A New Approach to Fraud Prevention Using Natural Language Processing (NLP)," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 32(3), pages 773-799, September.
    • Handle: RePEc:kap:apfinm:v:32:y:2025:i:3:d:10.1007_s10690-024-09470-x
    DOI: 10.1007/s10690-024-09470-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10690-024-09470-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10690-024-09470-x?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
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • M - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics

    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:kap:apfinm:v:32:y:2025:i:3:d:10.1007_s10690-024-09470-x. 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.