{"id":9303,"date":"2016-11-16T09:22:50","date_gmt":"2016-11-16T08:22:50","guid":{"rendered":"https:\/\/www.riskinsight-wavestone.com\/?p=9303"},"modified":"2019-12-31T10:15:44","modified_gmt":"2019-12-31T09:15:44","slug":"machine-learning-opportunites-enjeux-banque-ligne-moderne","status":"publish","type":"post","link":"https:\/\/www.riskinsight-wavestone.com\/en\/2016\/11\/machine-learning-opportunites-enjeux-banque-ligne-moderne\/","title":{"rendered":"Le Machine Learning, quelles opportunit\u00e9s et quels enjeux dans une Banque en Ligne moderne ?"},"content":{"rendered":"<p>La <strong>Banque en Ligne<\/strong> conna\u00eet de <strong>profondes mutations<\/strong>, tant sur le plan des <strong>enjeux m\u00e9tiers<\/strong> \u2013 avec des p\u00e9rim\u00e8tres de plus en plus larges et de moins en moins ensilot\u00e9s \u2013 que sur celui des <strong>enjeux r\u00e8glementaires<\/strong> (<em>Instant Payment<\/em>, <a href=\"https:\/\/www.riskinsight-wavestone.com\/en\/2016\/01\/la-dsp2-une-directive-sur-les-services-de-paiements-qui-prone-la-concurrence\/\">DSP2<\/a>\u2026). Les cas de fraude se multiplient et les sch\u00e9mas d\u2019attaque men\u00e9s par des fraudeurs de plus en plus aguerris se diversifient. Pour accompagner ces nombreux changements, les m\u00e9thodes et les <strong>processus m\u00e9tiers<\/strong> se doivent d\u2019\u00eatre <strong>plus efficaces, mieux adapt\u00e9s, et plus flexibles<\/strong>. Les m\u00e9thodes de <em>Machine Learning<\/em>, malgr\u00e9 leur r\u00e9cente d\u00e9mocratisation, permettent d\u2019\u00e9pouser la r\u00e9volution digitale de la Banque en Ligne.<\/p>\n<h2>Machine Learning, d\u00e9mystification et opportunit\u00e9s<\/h2>\n<p>Le <em>Machine Learning<\/em> est <a href=\"http:\/\/www.wired.co.uk\/article\/machine-learning-ai-explained\">une forme d\u2019intelligence artificielle<\/a> qui consiste \u00e0 apprendre et mod\u00e9liser un ph\u00e9nom\u00e8ne pour mieux le comprendre et le ma\u00eetriser. Pour cela, un ou plusieurs algorithmes permettent d\u2019\u00e9tablir des corr\u00e9lations entre les \u00e9v\u00e8nements qui composent ce ph\u00e9nom\u00e8ne. On distingue deux grands types de m\u00e9thodes\u00a0:<\/p>\n<ul>\n<li>Les m\u00e9thodes supervis\u00e9es, qui cr\u00e9ent des mod\u00e8les \u00e0 partir d\u2019une base de donn\u00e9es d\u2019exemples (g\u00e9n\u00e9ralement des cas d\u00e9j\u00e0 trait\u00e9s et valid\u00e9s).<\/li>\n<li>Les m\u00e9thodes non-supervis\u00e9es, qui n\u2019ont pas besoin d\u2019une base de donn\u00e9es d\u2019exemples<\/li>\n<\/ul>\n<p>Pour illustrer la diff\u00e9rence entre les deux m\u00e9thodes, on peut consid\u00e9rer le cas de la d\u00e9tection de fraude. Pour s\u2019entra\u00eener et cr\u00e9er des mod\u00e8les pr\u00e9cis, les m\u00e9thodes supervis\u00e9es utiliseraient en entr\u00e9e des donn\u00e9es d\u00e9j\u00e0 trait\u00e9es et marqu\u00e9es comme \u00e9tant li\u00e9es ou non \u00e0 des cas de fraude (sch\u00e9mas de fraude connus), alors que les m\u00e9thodes non-supervis\u00e9es utiliseraient des donn\u00e9es brutes issues des applications du SI afin de mod\u00e9liser les comportements normaux. Conceptuellement, cela revient \u00e0 mod\u00e9liser respectivement ce qui est anormal (la fraude \u2013 en ayant assez de donn\u00e9es pour que cette repr\u00e9sentation soit fid\u00e8le) ou ce qui est normal (en d\u00e9tectant <em>de facto <\/em>les fraudes lorsque l\u2019on s\u2019\u00e9loigne de cette normalit\u00e9).<\/p>\n<p>Tous les algorithmes ne se valent pas. Chacun poss\u00e8de des qualit\u00e9s et des d\u00e9fauts qu\u2019il faut savoir peser et qui d\u00e9pendent en grande partie des donn\u00e9es d\u2019entr\u00e9e, propres \u00e0 chaque cas m\u00e9tiers. Il est important de <strong>choisir des donn\u00e9es \u00e0 la fois pertinentes et disponibles en quantit\u00e9 suffisante<\/strong> pour obtenir des r\u00e9sultats probants. Dans le contexte de la Banque en Ligne, <strong>de nombreuses donn\u00e9es peuvent faire l\u2019objet de <em>Machine Learning<\/em><\/strong><em>\u00a0<\/em>:<\/p>\n<ul>\n<li>Habitudes de transaction\u00a0: montants des virements, pays destinataires\u2026<\/li>\n<li>Habitudes de connexion\u00a0: heure de connexion, user-agent, <em>device<\/em> utilis\u00e9\u2026<\/li>\n<li>Habitudes de navigation\u00a0: parcours client, v\u00e9locit\u00e9 de navigation\u2026<\/li>\n<li>Donn\u00e9es comportementales\u00a0: vitesse de frappe, d\u00e9placement de la souris\u2026<\/li>\n<li>Donn\u00e9es marketing\u00a0: produits consomm\u00e9s, libell\u00e9s des virements\u2026<\/li>\n<\/ul>\n<p>Correctement exploit\u00e9e par des algorithmes de <em>Machine Learning<\/em>, la conjugaison de ces diff\u00e9rentes donn\u00e9es, pr\u00e9c\u00e9d\u00e9e par un traitement tirant le maximum de leur valeur, peut permettre des <strong>r\u00e9sultats bien plus significatifs<\/strong> que ne le permettent les m\u00e9thodes classiques. <strong>La connaissance client (<em>KYC<\/em>), <\/strong>en <a href=\"https:\/\/www.internetretailer.com\/commentary\/2016\/10\/20\/combat-fraud-getting-know-your-customer-better\">exploitant par exemple le parcours client<\/a> type<strong>, la d\u00e9tection de fraude<\/strong>, en utilisant les habitudes de virement pour identifier des cas suspects (pays de connexion, distribution des montants\u2026),<strong> ou encore le marketing <\/strong>\u00e0 travers <a href=\"http:\/\/www.huffingtonpost.com\/advertising-week\/machine-learning-is-about_b_12649810.html\">la connaissance des habitudes<\/a> de consommations (analyse des libell\u00e9s, regroupements des achats par cat\u00e9gories\u2026) peuvent notamment largement tirer parti de ces donn\u00e9es.<\/p>\n<h2>Concr\u00e8tement, quels sont les gains du Machine Learning\u00a0?<\/h2>\n<p><strong>Tout d\u2019abord, conna\u00eetre le client et mieux adresser ses besoins<\/strong><\/p>\n<p>Le <em>Machine Learning <\/em>permet de tirer le maximum de valeur des donn\u00e9es <strong>en singularisant les mod\u00e8les<\/strong> l\u00e0 o\u00f9 les m\u00e9thodes \u00ab\u00a0classiques\u00a0\u00bb reposent sur un mod\u00e8le commun \u00e0 l\u2019ensemble des donn\u00e9es d\u2019entr\u00e9e. Par exemple dans le cas de la d\u00e9tection de fraude, les mod\u00e8les de r\u00e8gles \u00ab\u00a0classiques\u00a0\u00bb reviennent \u00e0 \u00e9laborer un mod\u00e8le qui sera commun \u00e0 tous les clients, sans tenir compte de leur unicit\u00e9, l\u00e0 o\u00f9 le <em>Machine Learning <\/em>permettra une d\u00e9tection plus efficace en associant un profil \u00e0 chaque client et en effectuant une surveillance et une d\u00e9tection propres \u00e0 ce profil. Ce raisonnement vaut pour tous les autres domaines d\u2019applications, et permet, <em>in fine<\/em>, <strong>une meilleure repr\u00e9sentation et une meilleure connaissance<\/strong> non plus \u00ab\u00a0du client\u00a0\u00bb au sens large, mais <strong>de chacun des clients<\/strong>.<\/p>\n<p><strong>Le Machine Learning permet \u00e9galement d\u2019offrir de nouveaux services<\/strong><\/p>\n<p>Au-del\u00e0 de l\u2019am\u00e9lioration notable des r\u00e9sultats bas\u00e9s sur les <em>KPI <\/em>classiques (taux de faux positifs, taux de d\u00e9tection\u2026), le <em>Machine Learning <\/em>permet une <strong>cr\u00e9ation de valeur en termes de gains financiers<\/strong> en personnalisant les outils dont profite le client. Cela peut parfaitement <strong>servir de socle \u00e0 une offre commerciale<\/strong> qui reposerait par exemple sur la personnalisation de ses seuils par le client ou sur la possibilit\u00e9 d\u2019\u00eatre alert\u00e9 en temps r\u00e9el lorsqu\u2019une information marketing, commerciale ou concernant sa s\u00e9curit\u00e9 a particuli\u00e8rement du sens. <strong>Certaines banques ont d\u2019ailleurs d\u00e9j\u00e0 franchi le pas<\/strong>, en offrant la possibilit\u00e9 \u00e0 leurs clients Entreprises d\u2019\u00eatre alert\u00e9s en cas de virements qui d\u00e9passent des seuils personnalis\u00e9s pr\u00e9alablement \u00e9tablis.<\/p>\n<p><strong>Finalement, le Machine Learning est aussi une occasion de moderniser les outils et rester \u00e0 l\u2019\u00e9tat de l&#8217;art<\/strong><\/p>\n<p>Lancer un projet de <em>Machine Learning<\/em> permet de communiquer sur le sujet et de profiter du <em>buzzword<\/em> pour g\u00e9n\u00e9rer de la <strong>satisfaction <\/strong>chez un certain nombre de <strong>client de plus en plus sensible \u00e0 des probl\u00e9matiques de s\u00e9curit\u00e9 ou de confidentialit\u00e9<\/strong>, tout en s\u2019assurant d\u2019\u00eatre<strong> \u00e0 l\u2019\u00e9tat de l\u2019art du march\u00e9<\/strong>. Cela peut \u00e9galement permettre de <strong>moderniser des outils existants<\/strong> en vue des changements qui vont continuer d\u2019op\u00e9rer dans la Banque en Ligne <strong>au gr\u00e9 des nouvelles r\u00e8glementations<\/strong> et des exigences techniques (temps r\u00e9el notamment avec <a href=\"http:\/\/www.europeanpaymentscouncil.eu\/index.cfm\/sepa-instant-payments\/what-are-instant-payments\/\"><em>Instant Payment<\/em><\/a>) et m\u00e9tiers qui en d\u00e9coulent. Dans ce cadre, on voit par exemple \u00e9clore des m\u00e9thodes de <em>Machine Learning <\/em>pour la surveillance des march\u00e9s et lutter contre les d\u00e9lits d\u2019initi\u00e9s.<\/p>\n<p>En conclusion, la pleine ma\u00eetrise technique du <em>Machine Learning <\/em>co\u00efncide avec de <strong>nouveaux besoins et de nouvelles exigences<\/strong> exprim\u00e9s dans la Banque en Ligne moderne. Embrasser cette \u00e9volution pr\u00e9sente de nombreux avantages, <strong>de l\u2019am\u00e9lioration des performances et des r\u00e9sultats \u00e0 la satisfaction des clients, en passant par une meilleure flexibilit\u00e9 technique<\/strong>. La ma\u00eetrise des diff\u00e9rentes m\u00e9thodes doit permettre un <strong>renouvellement des traitements et des processus m\u00e9tiers<\/strong>, en les rapprochant du client (aujourd\u2019hui ces m\u00e9thodes sont plut\u00f4t transparentes pour lui). Dans le cas de la lutte contre la fraude, on peut par exemple imaginer de nombreux cas autour de <em>l\u2019alerting <\/em>et des contre-mesures, comme une v\u00e9rification par authentification forte en cas de suspicion, ou des informations re\u00e7ues en temps r\u00e9el pour mieux impliquer les clients.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>La Banque en Ligne conna\u00eet de profondes mutations, tant sur le plan des enjeux m\u00e9tiers \u2013 avec des p\u00e9rim\u00e8tres de plus en plus larges et de moins en moins ensilot\u00e9s \u2013 que sur celui des enjeux r\u00e8glementaires (Instant Payment, DSP2\u2026)&#8230;.<\/p>\n","protected":false},"author":1263,"featured_media":9308,"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":[533,3307,550,2733,180,319],"coauthors":[2734],"class_list":["post-9303","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-cyber-for-financial-services","category-cybersecurity-digital-trust","tag-banque","tag-financial-services-cyber","tag-fraude","tag-machine-learning","tag-satisfaction-client","tag-technologies"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.0 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Le Machine Learning, quelles opportunit\u00e9s et quels enjeux dans une Banque en Ligne moderne ? - RiskInsight<\/title>\n<meta name=\"description\" content=\"La Banque en Ligne conna\u00eet de profondes 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