The detection of criminal groups in real-world fused data: using the graph-mining algorithm “GraphExtract”

Robinson, David; Scogings, Chris

doi:10.1186/s13388-018-0031-9

Cited by 21 publications

(10 citation statements)

References 42 publications

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“…They affirm that bank transactions have their own characteristics and properties that can be considered in the graphs. For Robinson and Scogings (2018), graph mining can be defined simply by detecting patterns in graphs, which include identification of subgraphs of interest within wider graphs. The authors argue graphic representation is an expressive model of data that best captures characteristics of relationships that point to criminal activities.…”

Section: Literature Reviewmentioning

confidence: 99%

Identifying financial patterns of money laundering with social network analysis: a Brazilian case study

Lima

Serrano

Imoniana

et al. 2021

JMLC

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Purpose This study aims to understand how forensic accountants can analyse bank transactions suspected of being involved with money laundering crimes in Brazil through social network analysis (SNA). Design/methodology/approach The methodological approach taken in this study was exploratory. This study cleaned and debugged bank statements from criminal investigations in Brazil using computational algorithms. Then graphs were designed and matched with money laundering regulations. Findings The findings indicated that graph techniques contribute to a range of beneficial information to help identify typical banking transactions (pooling accounts, strawmen, smurfing) used to conceal or disguise the movement of illicit resources, enhancing visual aspects of financial analysis. Research limitations/implications Research found limitations in the data sets with reduced identification of originators and beneficiaries, considered low compared to other investigations in Brazil. Furthermore, to preserve restrict information and keep data confidential, data sets used in research were not made available. Practical implications Law enforcement agencies and financial intelligence units can apply graph-based technique cited in this research to strengthen anti-money laundering activities. The results, grounded in analytical approaches, may offer a source of data to regulators and academia for future research. Originality/value This study created data sets using real-life bank statements from two investigations of competence by the Brazilian Federal Justice, including real-data perspectives in academic research. This study uses SNA, which is a popular approach in several areas of knowledge.

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Section: Literature Reviewmentioning

confidence: 99%

Identifying financial patterns of money laundering with social network analysis: a Brazilian case study

Lima

Serrano

Imoniana

et al. 2021

JMLC

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“…With respect to the type of network being analysed, the method developed is most suited for network when the links represents flow or patterns of movement among vertices. Chen & Saad's work [54], as they referred to it, is the extraction of dense subgraph with respect to community detection in a network. Having established the problem as very challenging but really essential in the analyses of graph structures and complex network, the work revealed community detection has being similar to the problem of re-ordering matrices in sparse matrix techniques and thus exploited the concept and resulted in the method of identifying matrix column similarities.…”

Section: Random Walk Algorithmmentioning

confidence: 99%

“…And at third level, expressive data representation are created for evidence-based decision-making. [58] in the year 2014, and in 2018, Magalingam et al [52], Bahulkar et al [53], Robinson et al [54] ,Junjing at al [55], where they have used network graph analysis for community detection to identify clusters or communities from a covert dataset, these work proven its value in refining criminological concepts and theories to aid the understanding of social processes behind crime problems and to assist law enforcement agencies in enforcing crime. However, the Figure 3 above shows the gap identifies the less number of research done for the past decade.…”

Section: Community Detection In Criminal Networkmentioning

confidence: 99%

Criminal Network Community Detection Using Graphical Analytic Methods: A Survey

Sangkaran¹,

Abdullah²,

Jhanjhi³

2018

EAI Endorsed Transactions on Energy Web

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Criminal networks analysis has attracted several numbers of researchers as network analysis gained its popularity among professionals and researchers. In this study, we have presented a comprehensive review of community detection methods based on graph analysis. The concept of community was vividly discussed as well as the algorithms for detecting communities within a network. Broad categorization of community detection algorithms was also discussed as well as a thorough review of detection algorithms which has been developed, implemented and evaluated by several authors in social network analysis. Most importantly, a strict review of researches based on the detection of community in a criminal network was carried out revealing the strength and limitations of criminal network community detection methods. Thus, it becomes obvious through this study that more research activities is necessary and expected in order to further grow this research area.

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“…SNA relies on real datasets used as sources which allow to build networks that are then examined [10,[12][13][14][15][16][17][18]. However, the collection of complete network data describing the structure and activities of a criminal organization is difficult to obtain.…”

Section: Introductionmentioning

confidence: 99%

Criminal networks analysis in missing data scenarios through graph distances

Ficara

Cavallaro²,

Curreri³

et al. 2021

PLoS ONE

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Data collected in criminal investigations may suffer from issues like: (i) incompleteness, due to the covert nature of criminal organizations; (ii) incorrectness, caused by either unintentional data collection errors or intentional deception by criminals; (iii) inconsistency, when the same information is collected into law enforcement databases multiple times, or in different formats. In this paper we analyze nine real criminal networks of different nature (i.e., Mafia networks, criminal street gangs and terrorist organizations) in order to quantify the impact of incomplete data, and to determine which network type is most affected by it. The networks are firstly pruned using two specific methods: (i) random edge removal, simulating the scenario in which the Law Enforcement Agencies fail to intercept some calls, or to spot sporadic meetings among suspects; (ii) node removal, modeling the situation in which some suspects cannot be intercepted or investigated. Finally we compute spectral distances (i.e., Adjacency, Laplacian and normalized Laplacian Spectral Distances) and matrix distances (i.e., Root Euclidean Distance) between the complete and pruned networks, which we compare using statistical analysis. Our investigation identifies two main features: first, the overall understanding of the criminal networks remains high even with incomplete data on criminal interactions (i.e., when 10% of edges are removed); second, removing even a small fraction of suspects not investigated (i.e., 2% of nodes are removed) may lead to significant misinterpretation of the overall network.

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The detection of criminal groups in real-world fused data: using the graph-mining algorithm “GraphExtract”

Cited by 21 publications

References 42 publications

Identifying financial patterns of money laundering with social network analysis: a Brazilian case study

Identifying financial patterns of money laundering with social network analysis: a Brazilian case study

Criminal Network Community Detection Using Graphical Analytic Methods: A Survey

Criminal networks analysis in missing data scenarios through graph distances

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