AI Potential to Uncover Criminal Modus Operandi Features

  • Ana Isabel Barros Netherlands Police Academy
  • Koen van der Zwet
  • Joris Westerveld
  • Wendy Schreurs
Keywords: artificial intelligence, modus operandi, intelligence analysis


Technological innovations such as digitalisation have an increasingly important role in our society. This development is also reflected in police work. In particular, the access to information on a global scale has increased the international character, adaptivity, and fluidity of criminal organisations. As such, there is a pressing need to better understand the evolving nature of these organisations and their associated modus operandi. While digitalisation enables access to lots of information and yields information overload challenges, developments in Artificial Intelligence (AI) offer new opportunities to tackle these challenges. In particular, they provide support in the automatic extraction and analysis of unstructured sources of information to efficiently make sense of large amounts of textual information sources. In this paper, we will explore the potential and challenges of various AI methods to extract criminal modus operandi from unstructured open text sources, like law court sentences. Such open text sources are reliable information sources that includes detailed validated information on the criminal activities and the modus operandi evolution in a given country. The application of this approach offers an alternative to the examination of classified police information and it also facilitates cross-country comparisons. The inherent complexity of modus operandi and the unstructured character of law court sentences yield the need to align and structure the modus operandi question with particular text mining methods. Specifically, we propose a step-wise approach to analyse automatic extraction of modus operandi-related problems via exploration, detection, and categorisation analysis. This decomposition enables to align these problems to specific functions of text-mining or machine learning methods, such as similarity detection, clustering, or named entity recognition. Using practical examples we demonstrate how this approach enables to automatically extract relevant information from court cases sentences for analysing modus operandi evolution in time.

How to Cite
Barros, A. I., van der Zwet, K., Westerveld, J., & Schreurs, W. (2022). AI Potential to Uncover Criminal Modus Operandi Features. European Law Enforcement Research Bulletin, (6), 255-263. Retrieved from
Conference Contribution