An Agent-Based Model for Detection in Economic Networks

  • João Brito
  • Pedro Campos
  • Rui Leite
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 887)


The economic impact of fraud is wide and fraud can be a critical problem when the prevention procedures are not robust. In this paper we create a model to detect fraudulent transactions, and then use a classification algorithm to assess if the agent is fraud prone or not. The model (BOND) is based on the analytics of an economic network of agents of three types: individuals, businesses and financial intermediaries. From the dataset of transactions, a sliding window of rows previously aggregated per agent has been used and machine learning (classification) algorithms have been applied. Results show that it is possible to predict the behavior of agents, based on previous transactions.


Agent-based models Fraud Networks Machine learning algorithms 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of EconomicsUniversity of PortoPortoPortugal
  2. 2.LIAAD (Laboratory of Artificial Intelligence and Decision Support), INESC TECPortoPortugal

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