Follow the connections.
Find suspicious patterns.
Graph analysis helps social security inspectors uncover unusual relationships and focus their investigations.
The inspection service of a national social security authority
The challenge
Social security systems depend on solidarity, but that principle comes under pressure when individuals or organisations don't meet their legal obligations. Key questions arise:
- Are social contributions being paid correctly?
- Is there fair competition among employers?
- Are workers treated as they are entitled to be?
These are the core concerns of the inspection service of the national social security authority. It has to detect fraud, unfair practices and systemic abuse, in a complex and interconnected ecosystem.
The solution
Common Sense AI used graph database technology to model and analyse the web of relationships across the social security landscape.
We built a graph database in which:
- Nodes represent entities such as individuals, companies, locations and employment relationships
- Edges capture the connections between them, including transactions, contracts and co-employment links
Using graph algorithms and network analysis, we could identify unusual structures and patterns in the data, often invisible in traditional relational models.
This gives proactive, data-driven insight into how entities are connected and where irregularities may exist.
The result
Social security inspectors can now detect clusters of suspicious relationships that suggest fraud or abuse.
- Field investigations are prioritised more effectively
- Inspectors act earlier, with better supporting evidence
The social security authority now has a more responsive, intelligent and transparent way to safeguard fairness and compliance, reinforcing the core values of the system.