Gestion des risques d’un crédit en utilisant les chaines Markov au sein de la banque

Abstract

This research aims to examine the impact of using Markov chains in improving credit risk management within banking institutions, given the crucial importance of such risks in the stability of the financial system. With the advancement of information and communication technologies, Algerian banks are facing challenges related to the need to modernize their tools and enhance their predictive performance, making the use of quantitative models—such as Markov chains indispensable. A mixed methodology was adopted, combining a theoretical analysis of Markov chains with their practical application to the case of the BEA agency in Maghnia, over the period from 2020 to 2024. Transition matrices were constructed and analyzed to estimate the probabilities of shifts between different credit risk states. The results demonstrated the effectiveness of this model in forecasting the evolution of loan statuses and distinguishing borrower behavior patterns. The study concluded that integrating Markov chains into risk management mechanisms contributes to improving prediction accura cy and enhances the efficiency of credit decision-making and loan portfolio assessment—representing a key step toward the digital transformation of the banking system in Algeria