{"id":11400,"date":"2018-11-02T19:17:47","date_gmt":"2018-11-02T18:17:47","guid":{"rendered":"https:\/\/www.riskinsight-wavestone.com\/?p=11400\/"},"modified":"2019-12-31T09:33:31","modified_gmt":"2019-12-31T08:33:31","slug":"quelle-perspective-pour-la-lutte-contre-la-fraude-22","status":"publish","type":"post","link":"https:\/\/www.riskinsight-wavestone.com\/en\/2018\/11\/quelle-perspective-pour-la-lutte-contre-la-fraude-22\/","title":{"rendered":"Revolution technologique : quelle perspective pour la lutte contre la fraude ? (2\/2)"},"content":{"rendered":"

Apr\u00e8s un premier article pr\u00e9sentant les nouvelles technologies que l’on trouve dans la lutte contre la fraude, ce deuxi\u00e8me article pr\u00e9sente comment int\u00e9grer ces technologies au mieux.<\/em><\/p>\n

Le dilemme de l’\u00e9volution des dispositifs antifraude : quels leviers pour int\u00e9grer ces technologies ?<\/h2>\n

Faisant \u00e9cho \u00e0 ces probl\u00e9matiques, l\u2019\u00e9cosyst\u00e8me des \u00e9diteurs s\u2019est organis\u00e9 pour proposer des solutions antifraude s\u2019appuyant sur ces technologies. Ainsi \u00e9diteurs et start-ups se sont tr\u00e8s largement d\u00e9velopp\u00e9s, partout dans le monde (plus de 150 fournisseurs ont \u00e9t\u00e9 recens\u00e9s au sein du radar \u00ab\u00a0Antifraude\u00a0\u00bb Wavestone). Le besoin de lutte antifraude a en effet par nature une dimension internationale, notamment dans la protection des flux mon\u00e9taires qui sont rarement limit\u00e9s \u00e0 un seul pays.<\/p>\n

\"\"<\/figure>\n

Figure 2 :<\/em>Exemple du radar des \u00e9diteurs antifraude Wavestone (extrait non exhaustif)<\/em><\/p>\n

M\u00eame si la lutte contre la fraude apparait comme un use case<\/em> de choix pour d\u00e9montrer le ROI du Machine Learning (r\u00e9duction du nombre de fraudes, automatisation de la d\u00e9tection\u2026) et au-del\u00e0 du choix de la strat\u00e9gie d\u2019outillage de lutte contre la fraude au regard de la maturit\u00e9 du march\u00e9, les questions \u00e0 se poser doivent rester celles d\u2019une solution SI \u00ab standard\u00a0\u00bb (exploitation, maintenance, \u00e9volutivit\u00e9\u2026).<\/p>\n

Si les co\u00fbts d\u2019infrastructures n\u00e9cessaires \u00e0 la mise en place d\u2019outils bas\u00e9s sur le Machine Learning et le big data ne sont pas n\u00e9gligeables, ils permettent de cr\u00e9er un environnement favorable \u00e0 l\u2019exploitation de la richesse des donn\u00e9es pour divers usages (maintenance pr\u00e9dictive des serveurs, connaissance client, etc.) en gardant \u00e0 l\u2019esprit les garde-fous mis en place par le RGPD.<\/p>\n

\"\"<\/figure>\n

Figure 3 : O\u00f9 peut-on agir avec le Machine Learning\u00a0: exemple d\u2019une banque<\/em><\/p>\n

Une nouvelle cible \u00e0 atteindre : une approche “sans couture” technologique et m\u00e9tier<\/h2>\n

Face aux nouveaux enjeux et l\u2019apport des technologies \u00e9mergentes, une nouvelle strat\u00e9gie antifraude doit \u00eatre d\u00e9sormais d\u00e9finie.<\/p>\n

La mise en place d\u2019un dispositif de d\u00e9tection globale de confiance qui devra respecter 5 grands principes.<\/strong><\/p>\n

    \n
  • L\u2019efficience et l\u2019automatisation : il b\u00e9n\u00e9ficiera d\u2019une d\u00e9tection \u00e0 plusieurs crit\u00e8res (moteur de r\u00e8gles et Machine Learning) et d\u2019une efficacit\u00e9 op\u00e9rationnelle optimis\u00e9e par l\u2019automatisation de mesures allant de l’augmentation du niveau d’authentification demand\u00e9 au gel d’un virement.<\/li>\n
  • L\u2019\u00e9volutivit\u00e9 et l\u2019omnicanal\u00a0: il int\u00e8grera plusieurs p\u00e9rim\u00e8tres dans la d\u00e9tection avec une logique \u00ab\u00a0sans couture\u00a0\u00bb entre le monde cyber et le monde \u00ab\u00a0hors cyber\u00a0\u00bb et sera con\u00e7u pour permettre l\u2019int\u00e9gration de nouvelles donn\u00e9es disponibles (ex\u00a0: donn\u00e9es de biom\u00e9trie comportementale).<\/li>\n
  • La visibilit\u00e9 et l\u2019exploitabilit\u00e9 : il fournira la visibilit\u00e9 (reporting<\/em>) et l\u2019explication des r\u00e9sultats de d\u00e9tection, aux \u00e9quipes antifraude, aux clients et \u00e9galement aux r\u00e9gulateurs.<\/li>\n
  • La conformit\u00e9 et la s\u00e9curisation\u00a0: il respectera les obligations en mati\u00e8re de d\u00e9tection ainsi que les r\u00e9glementations (RGPD), et traitera les risques inh\u00e9rents au Machine Learning (tentatives de poisoning<\/em>, compr\u00e9hension par l\u2019attaquant du mod\u00e8le\u2026).<\/li>\n
  • La gouvernance transverse cybers\u00e9curit\u00e9 et m\u00e9tier : une collaboration \u00e9troite des \u00e9quipes de d\u00e9tection de menaces cyber et m\u00e9tier antifraude, d\u00e9passant les silos encore trop pr\u00e9sents, permettra une r\u00e9ponse globale avec une vision 360 des menaces et fera le meilleur usage des donn\u00e9es disponibles.<\/li>\n<\/ul>\n

    Pour b\u00e9n\u00e9ficier de tous les atouts apport\u00e9s par cette nouvelle strat\u00e9gie de d\u00e9tection, il conviendra \u00e9galement de ne pas n\u00e9gliger les syst\u00e8mes d\u2019investigation et de r\u00e9action.<\/p>\n

    Une d\u00e9centralisation partielle de la lutte contre la fraude, impliquant les conseiller bancaires, permettra une plus grande capacit\u00e9 d\u2019investigation. Ayant la connaissance la plus fine de leurs clients, ces derniers repr\u00e9sentent un atout dans le processus d\u2019investigation.<\/p>\n

    De plus, la biom\u00e9trie comportementale et le machine learning permettent de fournir une meilleure visibilit\u00e9 sur le niveau de confiance qu\u2019on peut accorder \u00e0 l\u2019utilisateur. Une fois le niveau de confiance d\u00e9fini, il est donc possible d\u2019adapter les niveaux d\u2019authentification demand\u00e9s en cons\u00e9quence. Une contribution adapt\u00e9e et gradu\u00e9e de l\u2019utilisateur permettra ainsi de r\u00e9duire le nombre d\u2019alertes \u00e9mises.<\/p>\n

    La mise en place d\u2019une nouvelle cible antifraude n\u2019est pas seulement pour assurer une r\u00e9ponse adapt\u00e9e \u00e0 un changement de contexte mais aussi pour anticiper une vague de fond qui s\u2019amorce aujourd\u2019hui. La d\u00e9tection de fraudes deviendra \u00e0 l\u2019avenir de plus en plus complexe compte tenu d\u2019une digitalisation qui va continuer \u00e0 s\u2019acc\u00e9l\u00e9rer, en particulier sur les moyens de paiement. L\u2019\u00e9mergence de nouveaux acteurs, comme les Fintechs<\/em>, et la d\u00e9sinterm\u00e9diation grandissante des banques vont notamment entra\u00eener un appauvrissement de la donn\u00e9e disponible. Les dispositifs antifraude sont donc vou\u00e9s \u00e0 \u00e9voluer en profondeur afin de garder et d\u00e9velopper leur efficacit\u00e9.<\/p>\n","protected":false},"excerpt":{"rendered":"

    Apr\u00e8s un premier article pr\u00e9sentant les nouvelles technologies que l’on trouve dans la lutte contre la fraude, ce deuxi\u00e8me article pr\u00e9sente comment int\u00e9grer ces technologies au mieux. Le dilemme de l’\u00e9volution des dispositifs antifraude : quels leviers pour int\u00e9grer ces…<\/p>\n","protected":false},"author":1132,"featured_media":11409,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"page-templates\/tmpl-one.php","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[3229,36],"tags":[533,165,2885,3307,550,2733],"coauthors":[2570,3064],"class_list":["post-11400","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-cyber-for-financial-services","category-cybersecurity-digital-trust","tag-banque","tag-confiance","tag-detection","tag-financial-services-cyber","tag-fraude","tag-machine-learning"],"acf":[],"yoast_head":"\nQuelle perspective pour la lutte contre la fraude ? 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