Credit risk prediction using behavioral features in an AI chatbot interface

Nidhal Ziadi Ellouze ()

International Journal of Innovative Research and Scientific Studies, 2025, vol. 8, issue 6, 2099-2120

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.

Keywords: AI Chatbot; Behavioral Scoring; Credit Risk; Explainable AI; Machine Learning; Random Forest; SHAP; Streamlit; XGBoost. (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:aac:ijirss:v:8:y:2025:i:6:p:2099-2120:id:10083

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