The Challenge

Social security systems depend on solidarity, but this principle is under pressure when individuals or organizations fail to comply with legal obligations. Key questions arise:

• Are social contributions being paid correctly?

• Is there fair competition among employers?

• Are workers receiving their rightful treatment?

These are the core concerns addressed by the inspection service of the national social security authority, which must detect fraud, unfair practices, and systemic abuse, all within a complex and interconnected ecosystem.

The Solution

Common Sense AI leveraged graph database technology to model and analyze the intricate web of relationships across the social security landscape.

We developed a robust graph database, where:

• Nodes represented distinct entities such as individuals, companies, locations, and employment relationships

• Edges captured the connections and interactions, including transactions, contracts, or co-employment links

Using advanced graph algorithms and network analysis techniques, we were able to identify unusual structures and patterns in the data, often invisible in traditional relational models.

This setup enabled proactive, data-driven insights into how entities are connected and where irregularities may exist.

The Result

The new system empowered social security inspectors to:

• Detect clusters of suspicious relationships suggestive of fraud or abuse

• Prioritize field investigations more effectively

• Act earlier and with better supporting evidence

As a result, the social security authority now benefits from a more responsive, intelligent, and transparent framework to safeguard fairness and compliance, reinforcing the core values of the system.