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GraphTalk Copenhagen - Fraud Detection with Graphs

Neo4j GraphTalk Copenhagen
Marius Hartmann, Danish Business Authority

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GraphTalk Copenhagen - Fraud Detection with Graphs

  1. 1. Marius Hartmann Fraud Detection with Graphs 24. september 2019
  2. 2. Main task of The Danish Business Authority 2Danish Business Authority Business service and registration of companies Business development and digital growth Business regulation and supervision planning and rural business EU and international affairs
  3. 3. Virk: The joint public one-stop shop to the Danish business world 3Danish Business Authority 27.000.000 visits at Virk on an annual basis 96% of companies in Denmark know Virk 4.000.000 filings on Virk annually 92% Instant case handling
  4. 4. ML Lab  9 person strong  Physics, astro-physics, economics, computer science, fine art, social science, 7 Phd’s (+1 on the way)  2/7 gender balance, kids and no-kids Erhvervsstyrelsen 4
  5. 5. What can we do with ML and Graph?  Help and guide users to make fewer mistakes  Improve and scale our control and supervision  Provide recommendations and personalize our solutions  Improve our policy development with ML created insight 5Danish Business Authority
  6. 6. Management Owner ? Example of control: Strengthened company control regarding VAT 6 Owner Management Revision Adverse Opinion Not complying to bookkeeping act VAT not filed on time Adverse Opinion Not complying to bookkeeping act VAT not filed on time Salery tax not paid Adverse Opinion Holding company OwnerOwner Real owner Danish Business Authority
  7. 7. Et nyt dataparadigme Erhvervsstyrelsen 7 (Legal) network of a lawyer with roles in relation to 12.400 companies
  8. 8. Transformation 8Danish Business Authority
  9. 9. What’s the deal with Graph and ML?  ML is based on data properties, but isn’t suited to handle relations between objects in data  Graph provides context to ML and even supports algorithms based on data structure 9Danish Business Authority Currently 126 mio. nodes 160 mio. relations
  10. 10. ML insights persisted to graph 10Danish Business Authority Blue: Company Yellow: Person Purple: Annual report Red: ML insights
  11. 11. Machine learning controls all identity papers for foreign business actors ML controls that fictional assets are not inserted ‘Weaponize’ unstructured data concerning negligence Control new businesses for concerns of fraud Identity Assets Audits 1.st line Handling complexity - 4 intelligent controls in 2019 Erhvervsstyrelsen 11
  12. 12. Connected data from data Erhvervsstyrelsen 12
  13. 13. Erhvervsstyrelsen 13 Registry data Business registry VAT Annual reports data
  14. 14. Erhvervsstyrelsen 14 Registry data Network data
  15. 15. Erhvervsstyrelsen 15 Registry data + metadata Data from data Delta values Discrepancies Client profile, IP, timestamp data metadata
  16. 16. Erhvervsstyrelsen 16 Registry data + metadata Enriched network data metadata
  17. 17. Erhvervsstyrelsen 17 Registry data + metadata + observations Shares client Group Fictionous Anormalities data metadata Machine learning
  18. 18. Erhvervsstyrelsen 18 Registry data + metadata + observations Shares client Group Fictionous Anormalities data metadata Machine learning
  19. 19. Erhvervsstyrelsen 19 data metadata Machine learning Registry data + metadata + observations
  20. 20. Erhvervsstyrelsen 20 Automatic control of new data Exploits what we already know Uses machine insights Machine learning Registry data + metadata + observations
  21. 21. Erhvervsstyrelsen 21 Data from data growth
  22. 22. Data Metadata ML Automate 01 02 03 04 Information about persons, companies, annual reports, VAT etc. Data from data. Observations, machine driven insights. Data driven business. Registries Metadata ML Business Intelligent control Erhvervsstyrelsen 22
  23. 23. ERST ML data platform Erhvervsstyrelsen 23 Machine learning models use and enrich our Knowledge graph triggered by events in near real time Knowledge graph maintains 360° network analysis of customers and business life cycles ML data platform Cloud infrastructure Event driven architecture ML data governance Data event store Automated intelligent controls applied to business systems in support of decision making.
  24. 24. What is complicated?  ML data governance  Machine learning in production  Reacting in near real-time  Business transformation  Explainability  Automation 24Danish Business Authority
  25. 25. Transparency and fairness in AI  Data ethics Erhvervsstyrelsen 25
  26. 26. Traceability in data 26 Business Who did what? Technology Data lineage, metadata management Evaluation Can we do better? Danish Business Authority
  27. 27. The knowledge graph and semantic AI Erhvervsstyrelsen 27
  28. 28. Graph as a knowledge catalyst 28Danish Business Authority Data sources Meta model Agent ML enrichment Knowledge graph Automation Semantic AI EVENT DATA
  29. 29. The semantic journey 29Danish Business Authority Data sources Meta model Agent ML enrichment Knowledge graph Automation Semantic AI
  30. 30. Knowledge AI 30Danish Business Authority AI abstraction Semantic layer
  31. 31. The principles  Graph adoption to contextualize business lifecycles  Meta data strategy to produce data from data  ML enriched automation so we may adopt machine generated insight  Monitor and trace usage so we can explain  Evaluate and improve continuously Erhvervsstyrelsen 31
  32. 32. Questions? 32Danish Business Authority
  33. 33. Marius Hartmann marhar@erst.dk +45 35 29 19 46

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