MATEC Web Conf.
Volume 210, 201822nd International Conference on Circuits, Systems, Communications and Computers (CSCC 2018)
|Number of page(s)||6|
|Published online||05 October 2018|
Graph-Network Models and Methods Used to Detect Financial Crimes with IAFEC Graphs IT Tool
Institute of Computer and Information Systems, Faculty of Cybernetics, Military University of Technology, Warsaw, Poland
* Corresponding author: firstname.lastname@example.org
The article outlines graph-network models used to detect financial crimes. The general graph-network model of transaction participants was defined. The article also provides some examples of the graph-network models used to detect financial crimes. The authors also proposed an original method for detecting financial crimes based on measurements of the network characteristics and graph similarity, used in the R&D project: “Advanced information technologies supporting the (mainly financial) data analysis processes in the area of financial crimes” sponsored by National Centre of Research and Development (NCBiR), Poland. The method is based on the use of measures of the selected graph characteristics and similarity of graph elements. Some examples of the use of the proposed measures and methods on the basis of the IAFEC Graphs IT tool, which was created as part of the project, were described. Tools options, such as filtering graphs, determining clusters, determining centrality measures, or links between network elements are presented. The IAFEC Graphs architecture was presented both from the software point of view (system-to-layer division) and hardware requirements. In addition, an example of the use of the described functions of the tool for examining links between entities contained in public registers in Poland is presented.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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