Development of a data-driven strategy for bank expenses optimization

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dc.contributor.author Abrane, Nardjes
dc.contributor.author Kacha, Selsebile
dc.contributor.other Arki, Oussama, Directeur de thèse
dc.contributor.other Messaoui, Kahina, Directeur de thèse
dc.date.accessioned 2025-11-11T13:49:38Z
dc.date.available 2025-11-11T13:49:38Z
dc.date.issued 2025
dc.identifier.other EP00971
dc.identifier.uri http://repository.enp.edu.dz/jspui/handle/123456789/11321
dc.description Mémoire de Projet de Fin d’Études : Génie Industriel.Date Science et intelligence artificiel : Alger, École Nationale Polytechnique : 2025 fr_FR
dc.description.abstract This project proposes a comprehensive digital transformation framework to address systemic challenges in Société Générale Algérie's Finance Division, including manual process dependencies and poor system integration. The solution integrates four interconnected projects: a comprehensive procurement analytics dashboard providing strategic insights, an AI-powered intelligent invoice processing system for process automation, a knowledge management system leveraging historical data, and a predictive budget optimization system using machine learning algorithms for enhanced financial planning. This transformation aims to shift from reactive operational models to intelligent, predictive management, improving straight-through processing and enhancing operational efficiency, accuracy, and financial control. fr_FR
dc.language.iso en fr_FR
dc.subject Digital transformation fr_FR
dc.subject Artificial intelligence fr_FR
dc.subject Invoice processing fr_FR
dc.subject Predictive analytic fr_FR
dc.title Development of a data-driven strategy for bank expenses optimization fr_FR
dc.type Thesis fr_FR


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