The Contribution Of The Application Of Artificial Intelligence Approaches To Predicting The Failure Of Borrowing Enterprises

Résumé: This study aims to develop a model that assists commercial banks in predicting the default of small and medium-sized enterprises (SMEs), with the goal of facilitating lending decisions. To achieve this, we constructed a database obtained from the National Center for Commercial Registration (CRNC) for a sample of 60 SMEs, evenly divided between healthy and distressed firms. The study data underwent various statistical tests using the SPSS software to determine descriptive characteristics of the sample and assess the suitability of this data for the credit risk measurement model employed in this study. The study’s findings revealed that the application of an artificial neural network model achieved accurate classification rates of up to 100% for the total classified institutions. This suggests that the proposed model could serve as an effective tool for commercial banks to optimize loan risk management and make sound credit decisions.

Publié dans la revue: مجلة التمويل والاستثمار والتنمية المستدامة