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GraphTour 2020 - Danish Business Authority: First line of Defence

Marius Hartmann, Danish Business Authority
Neo4j GraphTour 2020 Stockholm

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GraphTour 2020 - Danish Business Authority: First line of Defence

  1. 1. Marius Hartmann, The Danish Business Authority First Line of Defence 03. March 2020
  2. 2. What is the DBA? Erhvervsstyrelsen 2 Ministry of Industry, Business and Financial Affairs https://www.linkedin.com/in/mariushartmann/ The Danish Business Authority The Danish Business Registry(CVR) part of runs ML Lab part of works at Marius
  3. 3. Reduce the yearly VAT deficit by 1 billion DKK Erhvervsstyrelsen 3 Mission *approx. 133 million EUR
  4. 4. Data sources Erhvervsstyrelsen 4 DBA ⚫ Danish Business Registry ⚫ Cases ⚫ Repeals ⚫ Annual reports Tax authority ⚫ VAT ⚫ Cases ⚫ Regulation ⚫ Repeals 151 mio. nodes 199 mio. relations
  5. 5. Tactical choices Erhvervsstyrelsen 5 Batch Profiling Lease domain experts Tech first Supercomputer Black box Precision first Redundant systems Manual processes Automated decision making Unsupervised Running models without governance Limited traceability Complexity Rigid Concentration Proprietary POC Monolith -Real time Prevent fraud Retain and build capability Business driven Fast response times Explainability Data ethics Reusable components Automation Decision support Supervised Continous improvement Single source of truth Simplicity Adaptive Distribution Open source Productionization Micro services +
  6. 6. ML data platform Erhvervsstyrelsen 6
  7. 7. Data ML data platform ML Automated insights Events Near real time Graph Connected data Metadata governance, traceability 7Erhvervsstyrelsen
  8. 8. 8Erhvervsstyrelsen ML dataplatform Erhvervsstyrelsen 8 ▪ Tech stack Python Azure cloud Kubernetes Docker Neo4J Apache Kafka ▪ ML data governance Evaluation plan Meta data ▪ Control tower Business control
  9. 9. Knowledge graph Erhvervsstyrelsen 9
  10. 10. Erhvervsstyrelsen 10 Metadata ML Data
  11. 11. Erhvervsstyrelsen 11 Metadata ML Data ? ? ? ? ? ?
  12. 12. Erhvervsstyrelsen 12 ML indsigt
  13. 13. Erhvervsstyrelsen 13 Data from data Metadata ML Data
  14. 14. Fraud detection Erhvervsstyrelsen 14
  15. 15. Machine learning controls all identity papers for foreign business actors ML controls that fictional assets are not inserted ‘Weaponize’ unstructured data concerning complaince Control new businesses for concerns of fraud Identity Assets Audits 1.st line Model to model Erhvervsstyrelsen 15
  16. 16. Complexity Erhvervsstyrelsen 16 The model utilize complex non-linear interactions between 224 parameters
  17. 17. Erhvervsstyrelsen 17 224 parameters • 69 raw data parameters • 128 processed parameters • 27 model data parameters
  18. 18. A business starts Months 0 10050 Risc score 0 Erhvervsstyrelsen 1811% :Person :Business :Business status :Business type :Business classification :Address
  19. 19. 0 10050 0 Erhvervsstyrelsen 19 6 52% :Person :Business :Business status :Business type :Business classification :Address A business starts Months Risc score
  20. 20. 0 10050 0 Erhvervsstyrelsen 20 6 7 64% :Person :Business :Business status :Business type :Business classification :Address A business starts Months Risc score
  21. 21. 0 10050 0 Erhvervsstyrelsen 21 6 7 11 64% :Person :Business :Business status :Business type :Business classification :Address A business starts Months Risc score
  22. 22. 0 10050 0 Erhvervsstyrelsen 22 6 7 1211 72% :Person :Business :Business status :Business type :Business classification :Address A business starts Months Risc score
  23. 23. 0 6 7 12 17 VAT control! Possible fraud Erhvervsstyrelsen 23 :Person :Business :Business status :Business type :Business classification :Address :VAT control 11 A business starts Months
  24. 24. The principles ▪ Uses data from other authorities ▪ Event driven so we can react in (near) real time ▪ 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 24
  25. 25. ▪ All companies controlled in near real time ▪ Continuously ▪ Full transparency and traceability Erhvervsstyrelsen 25 Effect of graph + ML
  26. 26. Spørgsmål? Erhvervsstyrelsen 26 Questions?
  27. 27. Marius Hartmann marhar@erst.dk +4535291946

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