April 2026
Intermediate
586 pages
17h 40m
French
Content preview from Apprendre AutoML (French Edition)
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Chapitre 14. Étudede cas n° 1 : Services financiers — Détection des fraudesen temps réel chez GlobalBank
Cet ouvrage a été traduit à l'aide de l'IA. Tes réactions et tes commentaires sont les bienvenus : translation-feedback@oreilly.com
L'introduction du chapitre 13 décrivait l'écart entre les modèles de notebook et les systèmes de production. On va maintenant voir exactement comment GlobalBank a comblé cet écart — et les surprises qu'elle a rencontrées en cours de route.
Remarque
Tous les exemples de code de ce chapitre ont été testés et validés avec AutoGluon 1.4.0. Pour les implémentations complètes, les résultats détaillés, les variantes expérimentales et les techniques avancées supplémentaires, consulte le notebook Jupyter d'accompagnement Supplementary_Code_A_Fraud_Detection.ipynb.
AutoGluon est en cours de développement. Rends-toi sur le site web d'AutoGluon pour obtenir la documentation la plus récente et les instructions d'installation.
Problématique et contexte
En 2023, les pertes liées à la fraude aux paiements à l’échelle mondiale ont dépassé les 32 milliards de dollars, la fraude aux transactions en ligne représentant le segment qui connaît la croissance la plus rapide. Pour GlobalBank, une grande institution financière traitant 50 millions de transactions par jour, la détection de la fraude ne visait pas seulement à prévenir les pertes : elle était fondamentale pour la survie de l’entreprise.
GlobalBank était confrontée à une véritable tempête de défis :
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