How to detect illegal employment using online sources?
State Labour Inspectorate is looking for an innovative solution that would help reduce the cases of illegal work in Lithuania.
- Illegal work is one of the most serious violations of labor law. During 2020, The State Labor Inspectorate performed 4,161 inspections of illegal work and other unregistered employment activities. The inspectorate identified 1,794 persons who had worked illegally.
- When gathering evidence of an entity’s irregularities, inspectors often do not have all the information or the information is inaccurate. They do not have the possibility to link individuals, and there is a lack of information about complaints filed against a particular entity.
- Currently, the Inspectorate uses a risk assessment system, which analyzes the data available in the Inspectorate information system, as well as information received from other institutions. Nevertheless, a lot of information can also be found online, such as complaints, negative feedback or other information related to labor law violations.
- The analysis of such information would help to identify risky entities, therefore the Inspectorate’s goal is to expand the analysis of risky entities using publicly available data.
The solution should:
- Identify publicly available data that is online according to the features of the entity (entity number, entity name, person name and surname, telephone number, etc.).
- Systematize the identified information according to the established criteria (address of the economic entity, telephone numbers, identified complaints, feedback, comments, etc.).
- Be able to identify risky entities using online sources according to the established criteria (e.g. sector of economic activity)
- Identify the links between the entities and display them by keywords (common telephone numbers, general managers of the entity, employees, etc.)
Solution evaluation criteria
- Innovativeness and relevance of the solution (40% of the total score)
- Commercial potential and competitiveness of the solution (40% of the total score)
- Competences and motivation of the authors of the solution (20% of the total score)
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