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Graphes et détection de fraude : exemple de l'assurance

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Comment utiliser Linkurious et la base de données de grpahes Neo4j pour identifier des fraudeurs à l'assurance?

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Graphes et détection de fraude : exemple de l'assurance

  1. 1. Détection de fraude à l'assurance avec Neo4j et Linkurious SAS founded in 2013 in Paris | http://linkurio.us | @linkurious
  2. 2. CEO Fondateur de Gephi Phd en Informatique et Systèmes Complexes CMO >5 ans dans le conseil Sciences-Po et Intelligence Economique HELLO WORLD Linkurious est une startup fondée en 2013 Jean Seb Romain CTO Ingénieur (Microsoft, Spotify) Machine Learning (Georgia Tech)
  3. 3. QU’EST CE QU’UN GRAPHE? Father Of Father Of Siblings Ceci est un graphe
  4. 4. QU’EST CE QU’UN GRAPHE : NOEUDS ET RELATIONS Un graphe est un ensemble de noeuds liés par des relations Father Of Father Of Siblings Ceci est un noeud Ceci est une relation
  5. 5. Antennes, serveurs, téléphones, clients Dépanner plus rapidement des réseaux Noms, adresses, téléphones, transactions Identifier des transactions et individus suspects Fournisseurs, routes, entrepôts, produits Diminuer le temps et coût de transport Quelques domaines dans lesquels nos clients utilisent les graphes CAS D’UTILISATION Logistique Sécurité Telco
  6. 6. La compagnie d’assurance analyse les déclaration et indemnise ses clients. Les fraudeurs n’ont qu’à toucher l’argent ! Sur la base du scénario, les fraudeurs remplissent leurs déclarations d’assurance (blessures et dégâts matériels). Quelques fraudeurs se réunissent. Ils définissent un scénario d’accident et le réalise. Organiser de faux accidents pour recevoir de vraies indemnités COMMENT FONCTIONNE LA FRAUDE A L’ASSURANCE AUTOMOBILE Organiser un faux accident Encaisser de l’ argent Faire appel à son assureur
  7. 7. $80 milliards par an de coûts liées à la fraude pour les compagnies d'assurances $144 de coût supplémentaire par conducteur au RU LE COUT DE LA FRAUDE Source : http://www.insurancefraud.org/80-billion.htm#.U4iTFxV9SuY
  8. 8. Mais pourquoi est-il si difficile de détécter les fraudeurs? POURQUOI EST IL DIFFICILE D’IDENTIFIER LES FRAUDEURS
  9. 9. Les criminels demandent des sommes modestes, coordonnent leurs déclarations et ont des “blessures” impossibles à vérifier PROBLEME 1 : LES CRIMINELS SAVENT ETRE DISCRETS
  10. 10. PROBLEME 2 : DIFFICILE DE VOIR UNE ANOMALIE DANS UN GRAND VOLUME DE DONNEES D’un accident à l’autre, le véhicule, les personnes, and leurs rôles changent : difficile de voir un schéma émerger
  11. 11. Comment les graphes peuvent aider? LES GRAPHES ET LA DETECTION DE FRAUDE
  12. 12. Un accident unique n’a pas l’air suspect UN MODELE DE DONNEES POUR UN ACCIDENT UNIQUE IS_LAWYER IS_DOCTOR Udo (Person) Monroe (Person) Robrectch (Person) Skyler (Person) Euanthe (Person) Jasmine (Person) Chelle (Person) Sousanna (Person) Focus (Car) Corolla (Car) Accident 1 (Accident) IS_INVOLVEDIS_INVOLVE D PASSENGER DRIVER DRIVER PASSENGER PASSENGER PASSENGER
  13. 13. 3 accidents séparés (en haut) implique 8 personnes différents (en bas) qui sont connectées entres elles : suspect? En regardant les déclarations sous forme de graphes, on voit les groupes de fraudeurs A QUOI RESSEMBLE UN GROUPE DE FRAUDEURS
  14. 14. En tant qu’analyste fraude, nous utiliserons Neo4j pour analyser les déclarations d’assurance et voir si nous pouvons identifier quelque chose de suspect COMMENT DEMASQUER UN GROUPE DE FRAUDEURS : POINT DE DEPART L’enquête commence par un accident...
  15. 15. 1. Est-ce que les personnes impliquées dans l’ accident sont aussi dans d’autres accidents? 2. S’ils le sont, avec qui sont-ils impliqués? Ces personnes sont-elles connectées à d’autres accidents? 3. Est-il possible de détecter la fraude automatiquement? COMMENT DEMASQUER UN GROUPE DE FRAUDEURS : QUESTIONS
  16. 16. QUESTION 1 : EST CE QUE LES PERSONNES IMPLIQUES DANS L’ACCIDENT SONT DANS D’AUTRES ACCIDENTS MATCH (accident)<-[]-(cars)<-[]-people-[]->(othercars)-[]->(otheraccidents:Accident) WHERE accident.location = 'New Jersey' RETURN DISTINCT otheraccidents.location as location, otheraccidents.date as date Une simple requête Cypher pour Neo4j
  17. 17. location date Florida 23/05/2014 Florida 27/05/2014 QUESTION 1 : EST CE QUE LES PERSONNES IMPLIQUES DANS L’ACCIDENT SONT DANS D’AUTRES ACCIDENTS Nos suspects sont impliqués dans 2 autres accidents
  18. 18. Avec un simple “*” on élargit la recherche à tout le graphe QUESTION 2 : AVEC QUI SONT-ILS IMPLIQUES MATCH (accident)<-[*]-(potentialfraudtser:Person) WHERE accident.location = 'New Jersey' RETURN DISTINCT potentialfraudtser.first_name as first_name, potentialfraudtser. last_name as last_name
  19. 19. first_name last_name Udo Halstein Robrecht Miloslav Monroe Maksymilian Skyler Gavril Euanthe Rossana Jasmine Rhea Sousanna Pinar Chelle Jessie QUESTION 2 : AVEC QUI SONT-ILS IMPLIQUES On a 8 personnes impliquées dans 3 accidents
  20. 20. Peut-on détecter automatiquement ces comportements suspects? QUESTION 3 : EST IL POSSIBLE DE DETECTER LA FRAUDE
  21. 21. Rechercher en temps réel des “victimes” suspectes QUESTION 3 : EST IL POSSIBLE DE DETECTER LA FRAUDE MATCH (person1:Person)-[*..2]->(accident1:Accident)<-[*..2]-(person2:Person)-[*..2]-> (accident2:Accident)<-[*..2]-(person3:Person)-[*..2]->(accident3:Accident) RETURN DISTINCT person1, person2, person3
  22. 22. Une requête Neo4j est utilisée pour vérifier un pattern de fraude Identifier des comportements suspects à grande échelle QUESTION 3 : EST IL POSSIBLE DE DETECTER LA FRAUDE Nouveau client Nouvelle voiture enregistrée Nouvel accident On identifie les fraudeurs éventuels Un événement déclenche une vérification
  23. 23. Les équipes de fraude sont plus rapides et les pertes peuvent être limitées. Linkurious aide les équipes de fraude à étudier les données et enquêter sur les cas suspects. Linkurious aide à verifier les alertes pour s’assurer que les vrais clients ne sont pas traités comme des criminels. Enquêter sur les cas sérieux Traiter les faux positifs En cas d’alerte, un analyste peut utiliser Linkurious pour rapidement évaluer la situation CE QU’APPORTE LINKURIOUS Economiser de l’ argent
  24. 24. DEMO Aller sur linkurio.us et essayez !
  25. 25. Prêt pour le cloud et basé sur l’open-source TECHNOLOGIE
  26. 26. Les graphes sont partout AUTRES CAS D’UTILISATION
  27. 27. CONCLUSION Contactez-nous pour discuter de vos projets sur contact@linkurio.us
  28. 28. Presentation on fraud and whiplash for cash by Philip Rathle and Gorka Sadowski (the inspiration for this presentation) : https://vimeo.com/91743128 Article on whiplash for cash : - the article : http://linkurio.us/whiplash-for-cash-using-graphs-for-fraud-detection/ - the dataset : https://www.dropbox.com/s/6ipfn4paaggughv/Whiplash%20for%20cash.zip GraphGist on whiplash for cash : - the article : http://gist.neo4j.org/?6bae1e799484267e3c60 Whitepaper on fraud detection by Philip Rathle and Gorka Sadowski : - the whitepaper : http://www.neotechnology.com/fraud-detection/ SOME ADDITIONAL RESOURCES TO CONSIDER

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