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Recently the report De Achterkant van Amsterdam (The Other Side of Amsterdam) was presented. A report on the investigation carried out by Pieter Tops and Jan Tromp into drug-related financial transactions and how they take place in the main city. The report describes in no uncertain terms the destructive effect of subversive crime on our society.

More or less at the same time as the presentation of this report, Den Bosch wrapped up the project Weerbaar (the Resilience project). A project, financed by the Dutch Ministry of Justice and Security, which revealed that an effective way to combat subversive crime starts with data. Test data from the Oost-Brabant Police has been combined with that from the municipality of Den Bosch along with information from open sources. All of this data was brought together in a scenario-based model that recognizes patterns, identifies indicators of subversive crime and generates future scenarios. The outcomes, possibilities and risks of this model were assessed by scientists from the Jheronimus Academy of Data Science. I had the privilege of being involved in this research myself.

Pieces of the puzzle

When the knowledge and experiences from De Achterkant van Amsterdam, are combined with the lessons from Weerbaar, an effective approach to combating subversive crime comes to the fore. De Achterkant van Amsterdam, for example, shows that research has been done into associations (as in non-profit organizations) that own expensive cars or a lot of real estate (Amsterdam CID, 2017). It is also known how many applications for licenses for the catering industry are privately financed (Municipal study, 2019). The number of expensive properties sold in Amsterdam without a mortgage was investigated (DNB, 2017). And it is known how many people act as financiers for personal loans, who, according to the tax authorities, do not have the capital to do so (Report on Personal Loans, 2019).

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    The project Weerbaar shows that it is entirely possible to combine the above sources together with police data and to translate these into practical scenarios. Moreover, there is no doubt that the results of all this is much more than the sum of its parts and that valuable, previously unknown insights have emerged. What has become clear from both studies is that many parties hold pieces of the puzzle when it comes to subversive organized crime, but there’s no one who is overseeing the whole puzzle.

    It is important in this context to stress that it is of course not illegal for an association to own expensive cars or expensive buildings. Nor is it illegal to issue personal loans, nor illegal to purchase real estate without a mortgage. But when this information is combined with other types of data, for example from the Chamber of Commerce, the Land Registry, the Inland Revenue Service, the Salvation Army, the shopkeeper’s association, the municipality and the police – a picture can be formed of a situation that points to less legal practices.

    This investigative method is as old as the Methuselah. Every detective works like this. The big difference is that a detective brings together information on an incidental basis and is only able to investigate a very limited amount of data, while technology can bring together data in a structural way (and in real time), and moreover, do this with very large amounts of data.

    Underlying problems

    One of the conclusions of the Tops and Tromp report is that the competency of ‘cooperation’ is not particularly highly developed within the Amsterdam governmental agencies. This brings us to the actual and underlying problem. Now that it is clear that subversive organized crime can be tackled more efficiently than is currently the case, the following painful question must be answered: Do we really want that? Is the disruptive nature of subversive crime on our society large enough for us to genuinely want to work together and share data? And do we sincerely want to look for the scope that laws and regulations offer us for that?

    Because if the answer to this is ‘yes’, then from now on the motto is: data, not words.

     

    About this column:

    In a weekly column, written alternately by Maarten Steinbuch, Mary Fiers, Peter de Kock, Eveline van Zeeland, Lucien Engelen, Tessie Hartjes, Jan Wouters, Katleen Gabriels and Auke Hoekstra, Innovation Origins tries to figure out what the future will look like. These columnists, occasionally joined by guest bloggers, are all working in their own way on solutions to the problems of our time. So that tomorrow is good. Here are all the previous articles.

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    Personal Info

    About the author

    Author profile picture Dr. Peter de Kock is adept at combining data science with scenario planning to prevent crime and enhance safety. De Kock graduated as filmmaker from the Film Academy of the Amsterdam School of the Arts where he mastered the art of creating scenarios for feature films and documentaries. After receiving a master’s degree in Criminal Investigation at the Police Academy, he was offered a position within the Dutch National Police force where he served as acting head of several covert departments. Within this domain he was able to introduce (creative) scenarios to anticipate and investigate crime. In 2014 De Kock combined art, criminal investigation, and data science in his dissertation "Anticipating Criminal Behaviour" with which he earned his Doctorate at Tilburg University. De Kock is founder and director of Pandora Intelligence, an independent security company specialized in security risks. The company uses a scenario-based approach to discover narratives in unstructured data, which helps (non) governmental organisations to mitigate risks and enhance opportunities.