أثر تطبيقات الذكاء الإصطناعي على تطوير النشاط الإقتصادي

Abstract

"Commercial banks are constantly seeking to minimize banking risks, particularly credit risk. They strive to find the most effective ways to assess and mitigate this risk. Due to limitations in classical methods for accurately evaluating the risks associated with bank loans, banks have turned to a new and modern statistical method, widely adopted in developed countries, known as credit scoring.

This study aimed to shed light on artificial intelligence and its impact on enhancing economic activity through its modern applications and innovative mechanisms. It employed advanced statistical models used in developed economies specifically, the discriminant analysis method applied by the Banque Extérieure d'Algérie, which served as the case study. The focus was on credit scoring, a method that primarily relies on a statistical technique called Multiple Discriminant Analysis (MDA). Based on this analysis, the decision to grant or deny a loan is made in the best interest of the bank. To demonstrate the process, a randomly selected client (Client A) was used as a sample case.

By analyzing the outcomes produced by the credit scoring model, decisions were made more quickly, and the model proved to be effective in assessing the client's financial suitability. This, in turn, helps financial institutions in Algeria better manage and control credit risks"