| 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 |