IDEAS home Printed from https://ideas.repec.org/a/aac/ijirss/v8y2025i6p2099-2120id10083.html
   My bibliography  Save this article

Credit risk prediction using behavioral features in an AI chatbot interface

Author

Listed:
  • Nidhal Ziadi Ellouze

Abstract

The following article presents an innovative credit risk assessment system that combines artificial intelligence with real-time interaction through a Streamlit-deployed chatbot. The aims of this article are demonstrated using a dataset of 3,080 clients from Tunisian banks in 2024, including financial and behavioral variables. Random Forest and XGBoost models were trained to predict loan defaults with up to 90% accuracy. The focus was on detecting high-risk profiles, achieving perfect recall (100%) and 76% precision for this class. The SHAP method ensures decision transparency by identifying key predictive variables. This system enables bank advisors to instantly obtain a risk score with personalized explanations, enhancing the speed, efficiency, and trustworthiness of credit evaluations, while outperforming traditional approaches.

Suggested Citation

  • Nidhal Ziadi Ellouze, 2025. "Credit risk prediction using behavioral features in an AI chatbot interface," International Journal of Innovative Research and Scientific Studies, Innovative Research Publishing, vol. 8(6), pages 2099-2120.
    • Handle: RePEc:aac:ijirss:v:8:y:2025:i:6:p:2099-2120:id:10083
    as

    Download full text from publisher

    File URL: https://ijirss.com/index.php/ijirss/article/view/10083/2318
    Download Restriction: no
    ---><---

    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:aac:ijirss:v:8:y:2025:i:6:p:2099-2120:id:10083. 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: Natalie Jean (email available below). General contact details of provider: https://ijirss.com/index.php/ijirss/ .

    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.