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| Élément Dublin Core | Valeur | Langue |
|---|---|---|
| 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 |
| Collection(s) : | Département Génie industriel : Data Science_Intelligence Artificielle | |
Fichier(s) constituant ce document :
| Fichier | Description | Taille | Format | |
|---|---|---|---|---|
| pfe.2025.DSIA.ABRANE.Nardjes-KACHA.Selsebil.pdf | PI00225 | 5.4 MB | Adobe PDF | Voir/Ouvrir |
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