{"id":2895,"date":"2024-02-18T15:29:57","date_gmt":"2024-02-18T15:29:57","guid":{"rendered":"https:\/\/esisoc.com\/resource\/building-predictive-models-to-improve-debt-collection-process\/"},"modified":"2024-02-18T15:29:57","modified_gmt":"2024-02-18T15:29:57","slug":"building-predictive-models-to-improve-debt-collection-process","status":"publish","type":"resource","link":"https:\/\/esisoc.com\/fr\/resource\/construire-des-modeles-predictifs-pour-ameliorer-le-processus-de-recouvrement-des-creances\/","title":{"rendered":"Construire des mod\u00e8les pr\u00e9dictifs pour am\u00e9liorer le processus de recouvrement des cr\u00e9ances"},"content":{"rendered":"<h2 style=\"text-align: center;\">D\u00e9tails cl\u00e9s<\/h2>\n<p>Les recettes ont \u00e9t\u00e9 multipli\u00e9es par deux gr\u00e2ce \u00e0 une meilleure segmentation de la client\u00e8le.<\/p>\n<div>\n<ul>\n<li>\n<div>D\u00e9fi<\/div>\n<div>Am\u00e9liorer l'efficacit\u00e9 du recouvrement des cr\u00e9ances gr\u00e2ce \u00e0 l'analyse pr\u00e9dictive<\/div>\n<\/li>\n<li>\n<div>Solution<\/div>\n<div> Un mod\u00e8le d'apprentissage automatique pour pr\u00e9dire la probabilit\u00e9 d'une promesse de paiement<\/div>\n<\/li>\n<li>\n<div>Technologies et outils<\/div>\n<div>\u00e9cosyst\u00e8me d'analyse de donn\u00e9es Python, VPN Checkpoint, SQL Server, paquet Lightgbm<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<h2 style=\"text-align: center;\">Client<\/h2>\n<p>Le client est une agence de recouvrement de cr\u00e9ances qui recouvre des dettes aupr\u00e8s de divers secteurs et clients. Les principaux clients de l'agence sont les banques, <a href=\"https:\/\/essidsolutions.com\/industry\/ai-solutions-retail\">commerce de d\u00e9tail<\/a>les entreprises de t\u00e9l\u00e9communication, les entreprises publiques.<\/p>\n<h2 style=\"text-align: center;\">D\u00e9fi : am\u00e9liorer l'efficacit\u00e9 du recouvrement des cr\u00e9ances \u00e0 l'aide de l'analyse pr\u00e9dictive<\/h2>\n<p>Plus de 1500 agents de recouvrement \u00e0 travers le pays traitent environ 3,5 millions de d\u00e9biteurs par mois et contactent environ 2 millions d'entre eux chaque mois.<\/p>\n<p>Le processus de recouvrement de cr\u00e9ances comprend les \u00e9tapes suivantes :<\/p>\n<ol>\n<li>se connecter avec un compte<\/li>\n<li>v\u00e9rification du compte<\/li>\n<li>promesse de paiement<\/li>\n<li>collection<\/li>\n<\/ol>\n<p>En collaboration avec le responsable de la science des donn\u00e9es de l'entreprise, dont le d\u00e9partement avait d\u00e9j\u00e0 commenc\u00e9 \u00e0 mettre en \u0153uvre l'apprentissage automatique pour am\u00e9liorer la prise de d\u00e9cision tout au long du cycle de vie des collections, il a \u00e9t\u00e9 d\u00e9cid\u00e9 que <a href=\"https:\/\/essidsolutions.com\/\">Solutions ESSID<\/a> explorerait le potentiel des <a href=\"http:\/\/localhost\/essidsolutions\/service\/predictive-analytics\">analyse pr\u00e9dictive<\/a> pour identifier les clients les plus susceptibles de rembourser.<\/p>\n<p>La condition indispensable de la mission \u00e9tait de permettre l'ex\u00e9cution des pr\u00e9dictions sur l'infrastructure MS SQL existante du client.<\/p>\n<h2 style=\"text-align: center;\">Solution : mod\u00e8le d'apprentissage automatique pour pr\u00e9dire la probabilit\u00e9 d'une promesse de paiement<\/h2>\n<p>ESSID Solutions a commenc\u00e9 \u00e0 travailler sur un <a href=\"http:\/\/localhost\/essidsolutions\/service\/machine-learning-consulting\">mod\u00e8le d'apprentissage automatique<\/a> pour pr\u00e9dire la probabilit\u00e9 d'une promesse de paiement de la part des comptes v\u00e9rifi\u00e9s. Des pr\u00e9visions pr\u00e9cises devraient permettre de mieux cibler les comptes et donc d'am\u00e9liorer les taux de recouvrement et de r\u00e9duire les co\u00fbts.<\/p>\n<p>Le d\u00e9veloppement du mod\u00e8le pr\u00e9dictif a comport\u00e9 quelques \u00e9tapes majeures, telles que la construction d'un pipeline pour le traitement des donn\u00e9es et la cr\u00e9ation de caract\u00e9ristiques dans SQL Server, l'entra\u00eenement du mod\u00e8le pr\u00e9dictif bas\u00e9 sur lightgbm, la construction d'un pipeline pour l'obtention de pr\u00e9dictions.<\/p>\n<p>L'\u00e9quipe compos\u00e9e d'un ing\u00e9nieur de donn\u00e9es et d'un scientifique de donn\u00e9es a \u00e9t\u00e9 affect\u00e9e au projet, qui comprenait les \u00e9tapes suivantes :<\/p>\n<table style=\"height: 438px;\" width=\"711\">\n<tbody>\n<tr>\n<td>\n<p style=\"text-align: left;\"><strong>Stade<\/strong><\/p>\n<\/td>\n<td>\n<p style=\"text-align: left;\"><strong>Champ d'application <\/strong><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: left;\">1. Pr\u00e9paration des donn\u00e9es<\/td>\n<td style=\"text-align: left;\">\n<p style=\"text-align: left;\">Analyse des donn\u00e9es<\/p>\n<p>Nettoyage des donn\u00e9es<\/p>\n<p>Construction d'un pipeline de donn\u00e9es pour le traitement et l'agr\u00e9gation des donn\u00e9es<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: left;\">2. Mod\u00e9lisation<\/td>\n<td>\n<p style=\"text-align: left;\">D\u00e9veloppement et test de mod\u00e8les<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: left;\">3. D\u00e9ploiement<\/td>\n<td>\n<p style=\"text-align: left;\">D\u00e9ploiement dans MS SQL 2017, tests d'int\u00e9gration<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><span style=\"letter-spacing: 0.6px; -webkit-text-stroke-color: transparent;\">Dans le cadre de ce projet, nous avons fourni au client les produits suivants :<\/span><\/p>\n<ul>\n<li>Module Python d\u00e9ployable avec :<br \/> - Moteur de traitement des donn\u00e9es<br \/> - Moteur pr\u00e9dictif<\/li>\n<li>Module Python d\u00e9ploy\u00e9 dans MS SQL 2017 :<\/li>\n<li>Code source et documentation du projet.<\/li>\n<\/ul>\n<h2 style=\"text-align: center;\">R\u00e9sultat : am\u00e9lioration de l'efficacit\u00e9 du processus de recouvrement des cr\u00e9ances<\/h2>\n<p>Le mod\u00e8le pr\u00e9dictif fourni par ESSID Solutions pr\u00e9dit avec pr\u00e9cision la probabilit\u00e9 de promesse de paiement d'un compte.<\/p>\n<p>La performance du mod\u00e8le a \u00e9t\u00e9 mesur\u00e9e par le score ROC_AUC. Le score ROC_AUC a atteint \u22480,775, ce qui repr\u00e9sente une am\u00e9lioration significative pour le client.<\/p>\n<p>Le client a ainsi la possibilit\u00e9 d'optimiser le temps des agents de recouvrement, en leur permettant de cibler d'abord les comptes les plus prometteurs.<\/p>","protected":false},"excerpt":{"rendered":"<p>Principaux d\u00e9tails Les revenus ont \u00e9t\u00e9 multipli\u00e9s par deux gr\u00e2ce \u00e0 une meilleure segmentation de la client\u00e8le. D\u00e9fi Am\u00e9liorer l'efficacit\u00e9 du recouvrement de cr\u00e9ances \u00e0 l'aide de l'analyse pr\u00e9dictive Solution Un mod\u00e8le d'apprentissage automatique pour pr\u00e9dire la probabilit\u00e9 de promesse de paiement Technologies et outils Ecosyst\u00e8me d'analyse de donn\u00e9es Python, VPN Checkpoint, SQL Server, package Lightgbm Client Le client est une soci\u00e9t\u00e9 de recouvrement de cr\u00e9ances ... Lire plus <a title=\"Construire des mod\u00e8les pr\u00e9dictifs pour am\u00e9liorer le processus de recouvrement des cr\u00e9ances\" class=\"read-more\" href=\"https:\/\/esisoc.com\/fr\/resource\/construire-des-modeles-predictifs-pour-ameliorer-le-processus-de-recouvrement-des-creances\/\" aria-label=\"Read more about Building Predictive Models to Improve Debt Collection Process\">Lire plus<\/a><\/p>","protected":false},"featured_media":2896,"template":"","industry":[73],"expertise":[93,42,43],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.9 (Yoast SEO v21.9.1) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Building Predictive Models to Improve Debt Collection Process - ESISOC | ESSID Solutions<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/esisoc.com\/fr\/resource\/construire-des-modeles-predictifs-pour-ameliorer-le-processus-de-recouvrement-des-creances\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Building Predictive Models to Improve Debt Collection Process\" \/>\n<meta property=\"og:description\" content=\"Key Details Increased revenue 2x times due to improved customer segmentation. 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