Bestrijding van ondermijnende criminaliteit vereist een uitgebreid inzicht in organisaties en hun activiteiten
To repress and prevent labor exploitation, fraud, organized crime, drug production and trafficking, or any other forms of subversive crime, identifying signs of undermining activity is crucial. This requires combining large amounts of information and detecting anomalies in the accumulated data. To avoid generating false positives or negatives, and to substantiate potential risk, information has be brought together with knowledge from past events.
The amount of information that has to be brought together is too large for humans to process in a timely manner.
Pandora Intelligence provides the analytical power to produce comprehensive insights in organizations, their involvement and relation with specific activities, stakeholders or goods. To this avail, Pandora Intelligence’s ENGINE consults dynamic data from a broad range of sources. This empowers your organization to proactively detect undermining activity and subversive crime.
Municipalities, safety regions and national police use of Pandora Intelligence to prevent subversive crime and its degrading effect on society.
While going through the list of companies registered within their municipality, analysts investigated the establishment and liquidation of a medical service company. They noticed that this company has only existed for several months before officially ceasing activity. After days of investigation, it turned out that the company was registered on a residential address on which several other similar companies were registered before. Each of those companies had a lifespan of only several months before it dissapeared, and this pattern had been going on for the past eight years. Based on the analysis, a team went to investigate the location and eventually dismantled a criminal organisation that had been laundering money for eight consecutive years.
On a recurring basis, registration of companies or company changes are automatically analyzed. To this effect, relevent information is consulted from a wide range of dynamic datasources. Pattern analisis and anomaly detection reveal abnormal changes, reoccurring registrations, differences with market averages, and domain specific anomalies. The results of the analysis are presented with respective risk levels, allowing analysts to prioritize their focus on suspicious cases.