The Danish Business Authority (DBA) uses machine learning and a graph database to analyze data from various sources like the Danish Business Registry and tax authorities. The goal is to detect potential fraud in near real-time by profiling businesses and monitoring their lifecycles. A risk score is calculated based on 224 parameters from raw data, processed data, and model outputs. If suspicious activity is detected, like a sudden increase in the risk score, a VAT control may be triggered. The system aims to continuously improve by learning from the data and evaluating model performance.

1. Marius Hartmann, The Danish Business Authority
First Line of Defence
03. March 2020

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. Reduce the yearly VAT deficit by 1 billion DKK
Erhvervsstyrelsen 3
Mission
*approx. 133 million EUR

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. 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. ML data platform
Erhvervsstyrelsen 6

7. Data
ML data platform
ML
Automated
insights
Events
Near real
time
Graph
Connected
data
Metadata
governance,
traceability
7Erhvervsstyrelsen

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. Knowledge graph
Erhvervsstyrelsen 9

10. Erhvervsstyrelsen 10
Metadata
ML
Data

11. Erhvervsstyrelsen 11
Metadata
ML
Data
?
?
?
?
?
?

12. Erhvervsstyrelsen 12
ML indsigt

13. Erhvervsstyrelsen 13
Data from data
Metadata
ML
Data

14. Fraud detection
Erhvervsstyrelsen 14

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. Complexity
Erhvervsstyrelsen 16
The model utilize complex non-linear interactions between 224 parameters

17. Erhvervsstyrelsen 17
224 parameters
• 69 raw data parameters
• 128 processed parameters
• 27 model data parameters

18. A business starts
Months
0 10050
Risc
score
0
Erhvervsstyrelsen 1811%
:Person
:Business
:Business status
:Business type
:Business classification
:Address

19. 0 10050
0
Erhvervsstyrelsen 19
6
52%
:Person
:Business
:Business status
:Business type
:Business classification
:Address
A business starts
Months
Risc
score

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. 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. 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. 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. 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. ▪ All companies controlled in near real time
▪ Continuously
▪ Full transparency and traceability
Erhvervsstyrelsen 25
Effect of graph + ML

26. Spørgsmål?
Erhvervsstyrelsen 26
Questions?

27. Marius Hartmann
marhar@erst.dk
+4535291946