Development of a data-driven strategy for bank expenses optimization

dc.contributor.authorAbrane, Nardjes
dc.contributor.authorKacha, Selsebile
dc.contributor.otherArki, Oussama, Directeur de thèse
dc.contributor.otherMessaoui, Kahina, Directeur de thèse
dc.date.accessioned2025-11-11T13:49:38Z
dc.date.available2025-11-11T13:49:38Z
dc.date.issued2025
dc.descriptionMémoire de Projet de Fin d’Études : Génie Industriel. Data Science et Intelligence Artificielle : Alger, École Nationale Polytechnique : 2025fr_FR
dc.description.abstractThis 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.identifier.otherEP00971
dc.identifier.urihttps://repository.enp.edu.dz/handle/123456789/11321
dc.language.isoenfr_FR
dc.subjectDigital transformationfr_FR
dc.subjectArtificial intelligencefr_FR
dc.subjectInvoice processingfr_FR
dc.subjectPredictive analyticfr_FR
dc.titleDevelopment of a data-driven strategy for bank expenses optimizationfr_FR
dc.typeThesisfr_FR

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