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Mariona Vivar Mompel Rédactrice en Chef Adjointe News Assurances Pro

Plan séquence - Intelligence artificielle, rêve ou cauchemar ?

lundi 24 février 2020

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des promesses aux actes

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L’intelligence artificielle progresse dans de nombreux secteurs comme le commerce en ligne ou la téléphonie mobile. Dans le secteur de l’assurance, les algorithmes s’intègrent au compte-gouttes, uniquement sur une partie de la chaîne de valeur. Dans ce dossier, nous avons voulu comprendre les raisons de cette inertie, les prouesses des algorithmes aujourd'hui et leurs promesses pour les prochaines années.

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Par Mariona Vivar

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POURQUOI L'IA dans l'assurance traîne des pieds ?

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La première raison est inhérente au rythme de déploiement des nouvelles technologies, tel que théorisé par Gartner dans son cycle du « hype » (voir infographie). « Après une période d’emballement médiatique, l’intelligence artificielle (ou la blockchain) est actuellement entrée dans la vallée de l’incertitude : les acteurs se rendent compte que ce n’est pas si simple, que le retour sur investissement n’est pas aussi rapide que celui qu’on avait imaginé, que les promesses des start-up ne sont pas toujours immédiatement au rendez-vous… Mais les fondamentaux sont là et au sein du secteur, nous pensons que dans 5 à 10 ans, l’intelligence artificielle sera présente dans toute la chaîne de valeur de l’assurance », selon Jérôme Balmes, directeur digital et innovation de la Fédération française de l'assurance.

Les assureurs eux-mêmes mettent du temps à absorber les nouvelles technologies. Par nature, un assureur est prudent et cherche à éviter les risques. Avant de déployer une innovation de manière massive, il est nécessaire de passer par un certain nombre d’étapes et d’obtenir l’aval de la direction des risques, conformité, marketing…

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La dette informatique ou legacy informatique représente un frein technique. Les assureurs ont des systèmes d’information anciens, très robustes, mais peu agiles. « Tous les assureurs sont engagés dans une course contre la montre pour migrer leurs systèmes d’information anciens vers des SI qui gardent la robustesse mais qui sont plus adaptés à l'interconnexion qui caractérise le monde d’aujourd’hui. Les données contenues dans ces systèmes anciens sont difficilement accessibles, insuffisamment propres ou structurées. Les assureurs qui progresseront le plus vite dans la transformation de leurs systèmes d’information seront plus en avance dans le déploiement du cloud et l’intégration de l’intelligence artificielle ou des objets connectés à leurs opérations », selon Jérôme Balmes.

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Les assureurs sont en retard par rapport aux banques car ces dernières sont confrontées au client final et ont été contraintes par la réglementation à ouvrir leurs données à des tiers, dans le cadre de l’open banking. « L’open insuring n’est pour l’instant pas obligatoire mais il est dans le sens de l’histoire. Tôt ou tard, la commission européenne imposera aux acteurs de l’assurance d’ouvrir leurs données afin de faciliter la transmission d’informations d’un assureur à un autre, aider le client à changer d’assureur ou bien lui permettre d’avoir une vision complète de tous ses contrats d’assurance vie via un agrégateur ».

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Pour que l’intelligence artificielle fonctionne, il faut lui transférer des exemples précis, lui transmettre les critères de culture de l’entreprise, sa politique d’indemnisation. « À partir du moment où vous transférez à l’IA vos processus cognitifs, il faut prendre en compte son éducation et sa formation en vue de son déploiement. Les personnes qui sont en charge du transfert de connaissances de l’entreprise vont devoir transmettre leurs raisonnements vers le système intelligent. Et cela prend du temps », conclut Jérôme Balmes.

LES EXPLOITS DE L'IA AUJOURD'HUI

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ROBOTS

« Les robots virtuels sont en mesure de comparer des informations entre deux systèmes ou d'accompagner la mise en conformité légale d’une nouvelle norme. Par exemple, grâce à ces programmes automatisés, Axa est en mesure de mettre à jour les dossiers clients en consultant automatiquement certains registres nationaux ou encore d’envoyer automatiquement un courrier en cas de pièces manquantes. La robotisation a permis d'accélérer les processus administratifs de remboursement ces dernières années », selon Marcin Detynieck, chief data scientist du groupe Axa et en charge de la recherche et développement.

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CHATBOTS

Les agents conversationnels ont un niveau cognitif plus élevé et permettent d’améliorer la relation client. Dans le domaine de l’assistance, les chatbots permettent de gérer des pics de communication 7 jours sur 7 et 24 heures sur 24. Le bot est capable d’avoir une conversation simple avec l’assuré victime d’un sinistre, ce qui permet de filtrer les demandes par ordre de priorité et de gérer de façon automatisée les cas les plus simples. Le client peut par la suite interroger le chatbot pour connaitre l’état d’avancement de son dossier. Un voice bot peut être idéal pour informer du solde de votre contrat d’assurance vie. En revanche, après un sinistre habitation grave, l’assuré préférera parler à une personne.

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ROBO-ADVISOR

En France, les robo-advisors en assurance vie sont encore balbutiants. Aux Etats-Unis, des robo-advisors fournissent des conseils personnalisés et automatisés, pour gérer les petits encours à faible valeur ajoutée, mais dès que les portefeuilles excèdent 100.000 ou 150.000 dollars, le conseiller humain prend le relais pour apporter des conseils en matière de fiscalité ou encore de patrimoine…

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"Le devoir de conseil est pour l’instant difficile à transférer intégralement à une machine, car il faut intégrer des éléments inter-subjectifs : les projets de vie, la fiscalité, l’ensemble du patrimoine. A horizon 5-10 ans, je ne pense pas que l’on passera par une machine à 100% pour préparer sa retraite par exemple. En revanche, le conseiller de demain disposera de nombreux outils dopés à l’IA, pour l’aider à fournir le meilleur conseil à son client. Y a-t-il un phénomène générationnel ? Est-ce que le rapport sur ces enjeux ne va pas basculer vers la machine ? Peut-être que dans un horizon lointain – 10 ans et plus, l’idée d’aller voir un conseiller pour ouvrir une assurance-vie deviendra obsolète, mais aujourd’hui les Français semblent encore attachés au contact humain et à la présence d'agences sur le territoire."

Jérôme Balmes, directeur de l'innovation et du digital de la FFA

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RECONNAISSANCE D'IMAGE

Un autre type d’intelligence artificielle est capable d’analyser des images satellites dans le cadre de la prévention des catastrophes naturelles. L’IA est capable de surveiller de grandes surfaces géographiques à la recherche d’événements climatiques en temps réel. Mais l’IA n’est pas seule, elle est uniquement une aide à la décision, elle est là pour faire une proposition d’attention. Un être humain est dans la boucle et, après vérification, c'est lui qui déclenche manuellement l’alerte pour prévenir les secours.

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Après la catastrophe, l'analyse d'images satellites permet d’estimer plus précisément les zones et la surface impactée et le montant des dommages assurés en cas d’inondation, incendie, sécheresse ou bien en assurance agricole.

L’intelligence artificielle permet d’automatiser les tâches les plus répétitives à faible valeur ajoutée, de faire un pré-travail, dans une logique de complémentarité avec l'être humain. « Nous confions à la machine une partie du processus. L’humain intervient non seulement pour vérifier que la machine ne s’est pas trompée, mais aussi pour gérer les cas trop compliqués », selon Marcin Detyniecki.

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MACHINE LEARNING

Le machine learning permet d’effectuer des prédictions à partir de l’analyse d’exemples. Par exemple, Axa Partners utilise un algorithme d’intelligence artificielle pour planifier les besoins de personnel sur un plateau d’assistance, à partir de l’analyse de données historiques.

Des algorithmes sont aussi en mesure d’identifier des cas potentiels de fraude, des transactions suspectes, à partir des exemples de fraudes avérées. Axa travaille avec la start-up Shift Technology pour une partie de ses activités et avec la solution interne Sherlock pour d’autres.

Sur le secteur bancaire, Crédit Mutuel utilise l’intelligence artificielle Watson pour trier des mails entrants, aller piocher un projet de réponse dans une base, que le conseiller va tout de même valider avant de le faire partir. Il va falloir entraîner l’IA pour s’assurer qu’elle répond correctement aux demandes des clients.

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LES PROMESSES POUR DEMAIN

[/vc_column_text][vc_empty_space height="22px"][vc_customcitation custom_citation="« Grâce à l’analyse des photos de véhicules sinistrés, l’IA permet de déterminer la gravité du sinistre. En dessous d’un certain seuil, l’assureur pourra décider s’il y a une perte totale du véhicule ou pas, s’il convient mobiliser un expert ou pas, déterminer un montant précis du coût de la réparation en fonction des pièces endommagées, et déclencher l’indemnisation forfaitaire en une heure »" citation_profil_img="1186672" citation_author="Jérôme Balmes, directeur innovation de la FFA." custom_citation_color="#46abc4"][vc_empty_space][/vc_column][/vc_row][vc_row extend="false" rtl_reverse="" css_animation="" bg_type="image" bg_grad="" bg_color_value="" parallax_style="vcpb-default" bg_image_new="id^1001527|url^https://d1p2aimeyx6kdr.cloudfront.net/wp-content/uploads/2019/11/06131513/texture_brique_noir.jpg|caption^null|alt^null|title^texture_brique_noir|description^null" layer_image="" bg_image_repeat="" bg_image_size="initial" bg_cstm_size="" bg_img_attach="" parallax_sense="30" bg_image_posiiton="" animation_direction="" animation_repeat="" video_url="" video_url_2="" u_video_url="" video_opts="loop" video_poster="" u_start_time="" u_stop_time="" viewport_vdo="" enable_controls="" controls_color="" bg_override="" disable_on_mobile_img_parallax="" parallax_content="" parallax_content_sense="" fadeout_row="" fadeout_start_effect="" enable_overlay="" overlay_color="" overlay_pattern="" overlay_pattern_opacity="" overlay_pattern_size="" overlay_pattern_attachment="" multi_color_overlay="uvc-multi-color-bg" multi_color_overlay_opacity="" seperator_enable="" seperator_type="" seperator_position="" seperator_shape_size="" seperator_svg_height="" seperator_shape_background="" seperator_shape_border="" seperator_shape_border_color="" seperator_shape_border_width="" icon_type="" icon="" icon_size="" icon_color="" icon_style="" icon_color_bg="" icon_border_style="" icon_color_border="" icon_border_size="" icon_border_radius="" icon_border_spacing="" icon_img="" img_width="" ult_hide_row="" ult_hide_row_large_screen="" ult_hide_row_desktop="" ult_hide_row_tablet="" ult_hide_row_tablet_small="" ult_hide_row_mobile="" ult_hide_row_mobile_large="" notification="" full_width="stretch_row" equal_height="" content_placement="" design__empty_1="" full_height="" columns_placement="top" background="" background_image_type="static" background_image__effect="" bg_size="initial" bg_position="center-center" row_bg_image="" bg_repeat="no-repeat" background_image_type__bg_color="rgba(255, 255, 255, 1)" row_bg_parallax_images="" row_bg_slideshow_images="" background_video_type="youtube" background_video__youtube__url="" background_video__youtube__parallax="" background_video__youtube__options="loop" background_video__youtube__fallback_image="" background_video__hosted__mp4="" background_video__hosted__webm="" background_video__hosted__ogv="" background_video__hosted__parallax="" background_video__hosted__options="loop" background_video__hosted__fallback_image="" background_color_type="color" bg__empty_1="" background_color_type__gradient_color1="rgba(44, 74, 228, 1)" background_color_type__gradient_color2="rgba(44, 74, 228, 1)" background_color_type__color_color1="rgba(44, 74, 228, 1)" bg__empty_2="" background_color__effect="" background_color__direction="left-to-right" parallax="" parallax_speed_bg="1.5" parallax_image="" video_bg="" video_bg_url="https://www.youtube.com/watch?v=lMJXxhRFO1k" video_bg_parallax="" el_id="" gap="0" disable_element="" settings__tag_1="" viewport_lg="" viewport_md="" viewport_sm="" viewport_xs="" settings__empty_1="" el_class="" version="1" css=""][vc_column extend="false"][vc_column_text]

Les assureurs souhaitent mettre à contribution la reconnaissance d’image pour accélérer la gestion des sinistres automobiles. Ces solutions existent au Japon, États-Unis et Angleterre. En France, plusieurs acteurs leaders sur le marché automobile sont actuellement en train d’entraîner l’IA. Cette solution sera opérationnelle en France d’ici un an ou deux.

En assurance automobile, il y a une volumétrie de sinistres importante et des milliers de cas similaires sur des modèles de véhicules connus. En assurance habitation, en revanche, il y a plus de cas de figure. « La reconnaissance d’images de sinistres n’a pas beaucoup progressé pour l’instant en MRH, car l’analyse à partir de photos s'avère beaucoup plus complexe (diversité des cas de figure plus importante, moins de normes et de standards qu’en réparation automobile). En revanche, les assureurs dommages s’intéresseront très certainement à cet enjeu pour le secteur de l'assurance habitation, après avoir déployé l'IA en assurance auto », explique Jérôme Balmes.

L’analyse des émotions

Un des terrains d’étude les plus avancés concerne la reconnaissance des émotions. En relation client, un chatbot pourrait fournir un premier niveau de réponse sur les appels entrants. Si la machine détecte une émotion forte ou un énervement de la part du client, elle basculera l’appel vers un conseiller humain. Des sociétés comme Ipsoft aux Etats-unis explorent ce domaine.

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L’assurance paramétrique

Dans le cadre de l’assurance paramétrique, on pourrait imaginer que le paramètre qui permet de déclencher l’indemnisation est calculé par la machine. Cela soulève cependant des problèmes de confiance. Comment faire confiance à la machine ?

Parmi les prochaines pistes de développement, Axa travaille sur l’historique de données de catastrophes naturelles. L’objectif est de pouvoir conseiller aux entreprises de s’installer dans tel ou tel endroit afin de réduire leur exposition au risque climatique.

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background_image_type="static" background_image__effect="" bg_size="initial" bg_position="center-center" row_bg_image="" bg_repeat="no-repeat" background_image_type__bg_color="rgba(255, 255, 255, 1)" row_bg_parallax_images="" row_bg_slideshow_images="" background_video_type="youtube" background_video__youtube__url="" background_video__youtube__parallax="" background_video__youtube__options="loop" background_video__youtube__fallback_image="" background_video__hosted__mp4="" background_video__hosted__webm="" background_video__hosted__ogv="" background_video__hosted__parallax="" background_video__hosted__options="loop" background_video__hosted__fallback_image="" background_color_type="color" bg__empty_1="" background_color_type__gradient_color1="rgba(44, 74, 228, 1)" background_color_type__gradient_color2="rgba(44, 74, 228, 1)" background_color_type__color_color1="rgba(44, 74, 228, 1)" bg__empty_2="" background_color__effect="" background_color__direction="left-to-right" parallax="" parallax_speed_bg="1.5" parallax_image="" video_bg="" video_bg_url="https://www.youtube.com/watch?v=lMJXxhRFO1k" video_bg_parallax="" el_id="chapitre2" gap="0" disable_element="" settings__tag_1="" viewport_lg="" viewport_md="" viewport_sm="" viewport_xs="" settings__empty_1="" el_class="" version="1" css=""][vc_column extend="false" el_class="chapitre_x_title" version="1"][vc_row_inner][vc_column_inner extend="false"][vc_customtitle customtitle="CHAPITRE 2" customsubtitle="" customtxtcolor="#0a0000"][/vc_column_inner][/vc_row_inner][vc_row_inner][vc_column_inner extend="false"][vc_customtyping]

LES RISQUES autour de l'avenir des métiers

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La machine va-t-elle un jour remplacer l’homme ? Si le grand remplacement n'est pas prévu, des métiers comme celui de gestionnaire de sinistres seront profondément transformés.

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Ce n’est pas parce que l’IA permet d’automatiser 20% des tâches d’un poste de travail que le poste va disparaître pour autant. Le collaborateur va pouvoir libérer du temps pour des tâches à valeur ajoutée, selon les spécialistes les plus optimistes.

« L’humain restera une composante essentielle du métier d’assureur car, même si des sinistres simples seront indemnisés automatiquement, d’autres plus complexes, suite à des événements climatiques extrêmes, par exemple, demanderont l’intervention et l’empathie d’un conseiller sur le terrain ou au téléphone. L’accompagnement humain et émotionnel est très important en assurance dommages, tout comme la dimension de conseil est essentielle en assurance vie. Les gains de productivité seront réinvestis dans la relation client », selon Jérôme Balmes.

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Parallèlement, la révolution numérique autour de la donnée crée de nouveaux métiers comme celui de data scientist.

« L’actuaire demain va travailler plus sur des aspects de production et devoir associer ses compétences mathématiques à des compétences informatiques. De nombreux autres métiers seront amenés, aussi, à apprendre à programmer. Heureusement, les langages de programmation vont parallèlement se simplifier progressivement », selon Marcin Detyniecki.

Des changements organisationnels vont également s’imposer. Par exemple, dès lors que l’intelligence artificielle permet de traiter automatiquement les sinistres les plus simples, les équipes vont s’orienter davantage vers les sinistres complexes.

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background_color__direction="left-to-right" parallax="" parallax_speed_bg="1.5" parallax_image="" video_bg="" video_bg_url="https://www.youtube.com/watch?v=lMJXxhRFO1k" video_bg_parallax="" el_id="" gap="0" disable_element="" settings__tag_1="" viewport_lg="" viewport_md="" viewport_sm="" viewport_xs="" settings__empty_1="" el_class="" version="1" css=""][vc_column extend="false"][vc_empty_space][vc_column_text]

Le machine learning implique d’analyser beaucoup d’information, de croiser plein de variables, et de tirer parfois des conclusions hâtives. Les biais de discrimination peuvent porter sur la non-représentativité de la population ou sur des biais structurels de la société. Un des risques est l’anti-sélection.

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L’ASSURANCE EST-ELLE À L’ABRI DES BIAIS ?

Apple a lancé une carte bancaire avec Goldman Sachs aux Etats-Unis. La capacité de crédit de chaque client était calculée par une intelligence, en fonction du profil du client. Un étudiant avait 500 dollars de limite de crédit alors qu’un professionnel qui travaille depuis longtemps avait une limite de crédit de 20.000-30.000 dollars. L'algorithme s'est avéré biaisé puisque sur des couples mariés qui faisaient une déclaration d’impôts commune, l'époux recevait une limite de crédit de 50.000 dollars et l’épouse, une limite de crédit de 5.000 dollars.

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L’algorithme d’Amazon pour sélectionner des CV dans un processus de recrutement s’est avéré discriminatoire. L’algorithme pénalisait les CV contenant le terme « femme ». Des biais de race et de genre et sexistes ont amené Amazon a arrêter d’utiliser cette IA en 2017.

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En assurance, la règlementation empêche quelques dérives. Par exemple, il est interdit aujourd’hui pour un assureur de sélectionner le risque en santé grâce à l’intelligence artificielle ou d’appliquer trop de différences tarifaires en fonction de l’exposition au risque.

Au-delà de la réglementation, certains assureurs comme Axa ont adopté des engagements pour une IA responsable.

Axa a mis en place en 2015 un panel d’experts indépendants spécialisés en confidentialité de données, déontologie et droit. Selon Axa, un assureur responsable se doit d’être le plus transparent possible sur l’utilisation des algorithmes, de garantir la sécurité des données et la robustesse de ses algorithmes pour éviter des erreurs.

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« Nous n’utilisons pas l’IA en tarification ou sélection du risque et pensons que cette technologie n’est, à ce stade, pas assez robuste pour une telle utilisation. Cet usage relève aujourd’hui de la R&D et nous déployons des efforts de recherche importants pour contrôler l’IA et en particulier les risques de biais mettant en compétition deux IA, selon Marcin Detyniecki. « En assurance, nous faisons aujourd’hui du machine learning avec peu de variables, qui sont toutes indépendantes. Les algorithmes sont assez simples et contrôlables il n’y a pas de risque de biais », rassure-t-il.

La FFA se montre aussi confiante. « Je ne connais pas un seul assureur qui prendrait le risque de mettre en production une IA avec des biais de discrimination, sans avoir préalablement testé ou validé ses algorithmes. Les IA doivent être en accord avec la législation et la culture de l'entreprise. Les assureurs font preuve d’une grande prudence sur cette problématique », souligne Jérôme Balmes.

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Certification des IA

Il n’existe pas aujourd’hui d’organisme de certification des IA. « Les grands cabinets d'audit s’intéressent de près aux méthodes de certification des IA, car demain, dans leurs missions de certification des comptes, ils auront très probablement des dimensions d’audit et de certifications techniques. D’ici 3 ou 4 ans, nous verrons émerger un champ autour de la certification des IA, selon J. Balmes. Nous pouvons imaginer que demain des IA feront elles-mêmes la certification des algorithmes. Aujourd’hui, nous sommes obligés d’utiliser des méthodes assez rudimentaires comme le test, c’est à dire, tester l’IA sur 10 000 ou 20.000 cas afin de déterminer, de façon statistique, qu’il n’y a pas de biais de discrimination. Cela prend énormément de temps ».

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les start-up qui sortent du bois

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Parmi toutes les applications concrètes de l'intelligence artificielle dans le secteur de l'assurance, la lutte contre la fraude et l'aide à la vente sont les plus avancées. Les gains en efficacité sont déjà mesurables.

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Quand la lutte contre la fraude se démultiplie

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Shift Technology, start-up française basée à Paris, Boston, Tokyo et Singapour, propose des solutions de détection de fraude à l’assurance basées sur l’intelligence artificielle. Membre du Next 40, la jeune entreprise emploie 300 personnes, dont une centaine de data scientists.

« Le plus difficile ce n’est pas de trouver des algorithmes mais de les mettre en œuvre et de les adapter aux problématiques spécifiques de nos 90 clients dans le monde. C’est pourquoi, en France, un data scientist accompagne le déploiement de l’algorithme chez le client », explique Arnaud Grapinet, chief data scientist de Shift Technology.

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Shift analyse les sinistres et signale à l’assureur les cas potentiellement frauduleux et les motifs de ses soupçons. Il peut s’agir des cas de « fraude opportuniste » dans lesquels l’assuré a menti sur les circonstances du sinistre ou bien des cas de « fraude organisée » qui impliquent des dizaines de personnes, des réseaux de réparation et des experts. Si l’envoi d’alertes est automatisé, en revanche il y a toujours un humain dans la boucle chez l’assureur pour mener l’enquête et vérifier si la fraude est avérée.

Le collaborateur du département anti-fraude de l’assureur qualifie ensuite la fraude, ce qui permet à l’algorithme d’apprendre de ses erreurs.

Chaque assureur définit en amont ses critères de fraude, son seuil de tolérance et le pourcentage de cas suspicieux qu’il souhaite recevoir de la part de Shift… En moyenne, l’assureur décide de mener une enquête dans 75% des cas suspicieux relevés par Shift, mais cela dépend des politiques anti-fraude de l’assureur. Si aux Etats-Unis, Shift revendique 30 euros économisés par sinistre analysé en IARD, en France les assureurs peuvent espérer atteindre 10 euros économisés par sinistre analysé.

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quels sont les apports des algorithmes dans la lutte contre la fraude ?

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« L’intelligence artificielle permet d’identifier des typologies de fraude qui passaient sous les radars des assureurs auparavant, de traiter plus de cas, d’identifier plus de typologies de fraude et d’obtenir moins de faux positifs », selon Arnaud Grapinet, chief data scientist de Shift Technology.

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Plus de maturité en IARD

En IARD, les services de lutte contre la fraude ont plus de maturité qu’en assurances de personnes. Si l’assuré est souvent à l’origine de la fraude en assurance dommages, en revanche, en santé c’est souvent le professionnel de santé. Il existe beaucoup de potentiel de détection de fraude en assurance santé mais les assureurs sont confrontés aux difficultés de recouvrement. Les sanctions pour les fraudeurs restent très faibles en France.

Après la détection de fraude, Shift a lancé un nouveau service de déclaration de sinistre digitalisé. L’assuré victime d’un dégât des eaux explique brièvement son sinistre à un chatbot en deux phrases et répond ensuite à une vingtaine de questions. Le chatbot est en mesure d’orienter ses questions en fonction du type de sinistre, du profil de l’assuré (locataire ou propriétaire) et du type de contrat signé, afin de voir si le sinistre déclaré est couvert par une des garanties du contrat.

Utilisé par deux assureurs en France, ce service répond à une forte demande des assurés. « La déclaration de sinistre en ligne est la porte ouverte à la fraude. Plus vous automatisez le processus, plus il y a de fraude. Comme le robot est déterministe, si une méthode de fraude a marché une fois, elle remarchera », résume Arnaud Grapinet. Shift espère donc vendre ses deux services couplés, car la détection de fraude est indispensable dans tout projet d’automatisation de la chaîne de déclaration de sinistre.

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L’aide à la vente selon Zelros

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Zelros propose aux assureurs des solutions d’aide à la vente et d’aide à la relation client. L’algorithme se nourrit des données structurées présentes dans le CRM des assurés comme sa tranche d’âge ou la catégorie socio-professionnelle, ainsi que des données non structurées comme les échanges d’e-mail ou le contenu des pièces jointes. Depuis fin 2019, Zelros fait également de l’analyse conversationnelle. L’algorithme repère tous ces signaux faibles et propose par la suite le produit d’assurance et les arguments de vente adaptés au profil du client. Le conseiller a la possibilité de donner un retour à la machine sur la pertinence ou pas de l’argument de vente proposé par l’algorithme.

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L’analyse conversationnelle

« Nous allons passer d’un mode de vente traditionnel dans lequel l’assureur propose des produits de façon banale vers un mode de vente où le conseiller pose des questions sur le mode de vie du client pour ensuite, dans un deuxième temps, lui proposer un produit adapté à son profil. Les bancassureurs peuvent en plus analyser les informations des comptes bancaires », explique Christophe Bourguignat, CEO de Zelros.

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quel retour sur investissement ?

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La start-up affirme que le taux de conversion des affaires grimpe de 30% à 40%-45% dans les réseaux de vente qui utilisent Zelros. Un autre des avantages est la réduction du temps nécessaire pour conclure une vente. Maif, Groupama et un bancassureur utilisent la solution d’aide à la vente de Zelros.

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