{"id":428,"date":"2018-03-01T00:00:00","date_gmt":"2018-02-28T23:00:00","guid":{"rendered":"https:\/\/blog.deepki.com\/2018\/03\/01\/banques-data-analytics\/"},"modified":"2023-04-26T16:58:15","modified_gmt":"2023-04-26T14:58:15","slug":"banques-data-analytics","status":"publish","type":"post","link":"https:\/\/www.deepki.com\/fr\/blog\/banques-data-analytics\/","title":{"rendered":"Comment les banques utilisent le data analytics pour mieux g\u00e9rer leur r\u00e9seau d\u2019agences : 3 exemples"},"content":{"rendered":"

S<\/span>i les data analytics<\/a> sont tr\u00e8s utilis\u00e9s dans le secteur bancaire pour am\u00e9liorer l\u2019exp\u00e9rience client et faciliter les prises de d\u00e9cisions strat\u00e9giques, ils peuvent \u00e9galement s\u2019av\u00e9rer tr\u00e8s int\u00e9ressants pour optimiser les co\u00fbts de fonctionnement des agences bancaires. D\u00e9couvrez dans cet article trois exemples concrets d\u2019utilisation de la data mise au service de l\u2019optimisation de la gestion de parc immobilier.<\/strong><\/span><\/p>\n

1\/ D\u00e9tecter des erreurs de facturation automatiquement<\/strong><\/h2>\n

Il est question ici des factures<\/a> concernant les charges de consommation d\u2019\u00e9nergie et d\u2019eau dans les agences bancaires.<\/p>\n

Avant
<\/strong>Les factures \u00e9taient collect\u00e9es et saisies par les services de comptabilit\u00e9 manuellement pour \u00eatre trait\u00e9es ensuite dans des logiciels. Laborieux et source d\u2019erreurs, ce contr\u00f4le de la facturation permettait difficilement d\u2019avoir une vision enti\u00e8re des d\u00e9penses. Les bilans comptables descendant rarement sous l\u2019\u00e9chelle annuelle, il \u00e9tait impossible de d\u00e9tecter des erreurs et d\u2019optimiser les co\u00fbts plus finement sur l\u2019ensemble des charges.<\/p>\n

Apr\u00e8s
<\/strong>Gr\u00e2ce \u00e0 des technologies de collecte d\u2019information, la collecte des factures est d\u00e9sormais automatique directement depuis les espaces des fournisseurs. Le data-analytics permet ensuite d\u2019alerter en cas de factures manquantes, en cas d\u2019erreurs au sein des factures ou en cas de provision ou de rattrapage \u00e0 venir. Prenons l\u2019exemple des factures d\u2019\u00e9nergie, le taux d\u2019erreurs de facturation est estim\u00e9 aujourd\u2019hui \u00e0 1 pour 70 factures d\u2019\u00e9lectricit\u00e9 et monte jusqu\u2019\u00e0 1 pour 30 sur le gaz. C\u2019est le cas aujourd\u2019hui chez un r\u00e9seau d\u2019agences bancaires qui a d\u00e9tect\u00e9 plus de 150 erreurs dans ses factures en 2017 soit environ 2,5 k\u20ac HTVA de montant erron\u00e9 sur un an.<\/p>\n

\u00c0 lire \u00e9galement : Contr\u00f4le automatique des factures : algorithme VS r\u00e8gle, le match !<\/a><\/em><\/p>\n

2\/ Optimiser ses contrats d\u2019\u00e9nergie<\/strong><\/h2>\n

Avant
<\/strong>Il fallait attendre le bilan annuel du fournisseur pour d\u00e9terminer les optimisations \u00e9nerg\u00e9tiques \u00e0 entreprendre et r\u00e9duire ses co\u00fbts. \u00c0 condition que les fournisseurs d\u2019\u00e9nergie proposent ce service, ce qui n\u2019est pas syst\u00e9matique ! De plus, le bilan fourni \u00e9tait rarement complet et n\u2019arrivait pas toujours au bon moment pour l\u2019entreprise.<\/p>\n

Apr\u00e8s<\/strong>
Il est d\u00e9sormais possible d\u2019analyser toutes les variables de facturation pour chaque agence bancaire afin de d\u00e9tecter des optimisations tarifaires. Le principe est simple : un syst\u00e8me automatis\u00e9 r\u00e9cup\u00e8re l\u2019ensemble des variables de la brique de co\u00fbt, calcule le co\u00fbt th\u00e9orique, le compare \u00e0 la facturation r\u00e9elle et d\u00e9termine o\u00f9 sont les erreurs et comment optimiser chacune des variables. On peut par exemple v\u00e9rifier si la puissance souscrite est optimale, ou encore si l\u2019option tarifaire est adapt\u00e9e \u00e0 la consommation du site. La Caisse d\u2019Epargne Lorraine Champagne Ardennes a d\u00e9tect\u00e9 6 000 \u20ac d\u2019optimisation tarifaire<\/a> en moins de 10 minutes gr\u00e2ce \u00e0 un algorithme d\u2019analyse de ses donn\u00e9es de facturation.<\/p>\n

\u00c0 lire \u00e9galement : Comment le data-analytics vous aide dans la ma\u00eetrise et l’optimisation de vos contrats d’\u00e9lectricit\u00e9 ?<\/a><\/em><\/p>\n

3\/ R\u00e9aliser des \u00e9conomies d\u2019\u00e9nergie<\/strong><\/h2>\n

Avant
<\/strong>Lorsque la banque poss\u00e9dait une solution de gestion technique centralis\u00e9e (GTC) et \/ ou de gestion technique des b\u00e2timents (GTB), elle \u00e9tait la plupart du temps initialis\u00e9e par le fournisseur au d\u00e9marrage et les mainteneurs n\u2019y touchaient plus car leur formation \u00e0 cet instrument est rarement pr\u00e9vue dans leur contrat d\u2019exploitation. Non seulement cette supervision ne permettait pas toujours de tenir compte de l\u2019\u00e9volution des besoins dans le temps, mais elle \u00e9tait parfois m\u00eame en conflit avec les \u00e9quipements install\u00e9s par les facilities managers dans le cadre de leurs forfaits de maintenance.<\/p>\n

Apr\u00e8s
<\/strong>Il est d\u00e9sormais possible d\u2019acc\u00e9der aux donn\u00e9es des GTC\/GTB par un acc\u00e8s simple \u00e0 un connecteur ou un module web du constructeur. Ces donn\u00e9es peuvent alors \u00eatre crois\u00e9es avec les donn\u00e9es de facturation, d\u2019activit\u00e9 et de patrimoine. Elles sont analys\u00e9es par des algorithmes de d\u00e9tection d\u2019anomalies et permettent de d\u00e9tecter en continu des actions d\u2019\u00e9conomies d\u2019\u00e9nergie<\/a>\u00a0pr\u00e9cises. Il a ainsi pu \u00eatre identifi\u00e9 sur une agence bancaire du groupe BPCE \u00e9quip\u00e9e de tels \u00e9quipements un gain de 20% de sa facture d\u2019\u00e9nergie uniquement gr\u00e2ce \u00e0 l\u2019analyse de ces donn\u00e9es.<\/p>\n

\u00c0 lire \u00e9galement :\u00a0D\u00e9tecter des \u00e9conomies d\u2019\u00e9nergie sur 400 b\u00e2timents uniquement gr\u00e2ce \u00e0 l\u2019analyse de ses donn\u00e9es, c\u2019est possible ! Kiloutou t\u00e9moigne<\/a><\/em><\/p>\n

La consommation \u00e9nerg\u00e9tique du secteur des agences bancaires pass\u00e9e au crible :\u00a0<\/strong><\/h2>\n

\"\"<\/p>\n

Pour leurs activit\u00e9s financi\u00e8res comme dans leur gestion immobili\u00e8re, les banques peuvent tirer profit du data analytics. Vous voulez d\u00e9couvrir un cas concret d\u2019utilisation de ses donn\u00e9es afin d\u2019optimiser la gestion de son r\u00e9seau d\u2019agences bancaires ? T\u00e9l\u00e9charger le retour d’exp\u00e9rience de Swiss Life.<\/strong><\/p>\n\n\n

<\/p>\n\n\n\n

\"\"<\/a><\/figure>\n","protected":false},"excerpt":{"rendered":"

Si les data analytics sont tr\u00e8s utilis\u00e9s dans le secteur bancaire pour am\u00e9liorer l\u2019exp\u00e9rience client et faciliter les prises de d\u00e9cisions strat\u00e9giques, ils peuvent \u00e9galement s\u2019av\u00e9rer tr\u00e8s int\u00e9ressants pour optimiser les co\u00fbts de fonctionnement des agences bancaires. D\u00e9couvrez dans cet article trois exemples concrets d\u2019utilisation de la data mise au service de l\u2019optimisation de la […]<\/p>\n","protected":false},"author":43,"featured_media":74560,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[120],"tags":[],"class_list":["post-428","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-performance-environnementale"],"yoast_head":"\nComment les banques utilisent le data analytics pour mieux g\u00e9rer leur r\u00e9seau d\u2019agences : 3 exemples<\/title>\n<meta name=\"description\" content=\"D\u00e9couvrez trois exemples concrets d\u2019utilisation de la data mise au service de l\u2019optimisation de la gestion de parc immobilier.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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