Enhancing banking governance: A machine learning-based credit risk classification

Karima Moumane, Ikram El Asri, Ilham Rharoubi, Hafida Ait Abderrahman, Sara Faqihi


Risk management in the banking sector has gained heightened significance following the 2008 Global Financial Crisis. With the advent of Machine Learning (ML) techniques, financial institutions are increasingly turning to Artificial Intelligence (AI) for enhanced risk assessment and management. This paper introduces a systematic protocol for implementing a decision tree classifier tailored for credit risk classification. Additionally, we develop a user-friendly web application utilizing the Flask framework and Python Pickle library. This application offers customers an intuitive interface to input their attributes and receive predictions regarding their credit risk classification. Our empirical findings demonstrate that the Support Vector Machine (SVM) achieves a commendable accuracy of 77% in classifying customers based on their banking data. Furthermore, the web application proves to be an effective means for customers to interact with the ML model, enhancing accessibility and user engagement. These outcomes underscore the substantial benefits that ML techniques can bring to the banking industry, enabling improved risk detection and management while concurrently enhancing customer service delivery.


banks; CNN architecture; credit risk; machine learning; Python

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