Application of Typing and Analysis of Reconnaissance Information for the Purpose of Its Transformation into the Financial Information Administered by FIUs

Matthias Alexander Kedzierski

doi:doi:10.11648/j.ijls.20220501.13

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Application of Typing and Analysis of Reconnaissance Information for the Purpose of Its Transformation into the Financial Information Administered by FIUs

Matthias Alexander Kedzierski

Published in International Journal of Law and Society (Volume 5, Issue 1)

Received: 14 December 2021 Accepted: 4 January 2022 Published: 14 January 2022

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Abstract

The system of counteracting money laundering and financing of terrorism (ML/FT) is built mainly on analysis of data of obliged entities, risk assessment and application of financial security measures. The growing amounts of data connected with their processing for the purpose of execution of subject instruments requirements improvement of methods in the scope of their acquisition, analysis and management. Thus, this system is also a basis for support of the human factor by means of state-of-the-art technical solutions. Therefore, meta data analyses, machine learning, predictive modelling or semantic modelling of natural language are incorporated in the assessment of ML/FT threats. The assumption is that each of these support methods must simplify and accelerate as well as reduce the costs of the processes of identification of ML/FT threats. The data analysis techniques used are aimed at - in the initial phase, before establishing a relationship with the obliged entity - the search for primary data, their verification and determination of the purpose of the client's activity, which may generate a threat. In the advanced phase - ongoing relations with the obliged entity - with the control of its behavior in the profit / risk relationship for safety and the introduction of "drivers" [controller] or "security bells" to the offered products - which is associated with the need to counteract the threat.

Published in	International Journal of Law and Society (Volume 5, Issue 1)
DOI	10.11648/j.ijls.20220501.13
Page(s)	19-27
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Financial Information, Risk Assessment, Reconnaissance Information, Money Laundering AML, Semantics, Machine Learning, Natural Language Processing NLP

References

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APA Style

Matthias Alexander Kedzierski. (2022). Application of Typing and Analysis of Reconnaissance Information for the Purpose of Its Transformation into the Financial Information Administered by FIUs. International Journal of Law and Society, 5(1), 19-27. https://doi.org/10.11648/j.ijls.20220501.13

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ACS Style

Matthias Alexander Kedzierski. Application of Typing and Analysis of Reconnaissance Information for the Purpose of Its Transformation into the Financial Information Administered by FIUs. Int. J. Law Soc. 2022, 5(1), 19-27. doi: 10.11648/j.ijls.20220501.13

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AMA Style

Matthias Alexander Kedzierski. Application of Typing and Analysis of Reconnaissance Information for the Purpose of Its Transformation into the Financial Information Administered by FIUs. Int J Law Soc. 2022;5(1):19-27. doi: 10.11648/j.ijls.20220501.13

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@article{10.11648/j.ijls.20220501.13,
  author = {Matthias Alexander Kedzierski},
  title = {Application of Typing and Analysis of Reconnaissance Information for the Purpose of Its Transformation into the Financial Information Administered by FIUs},
  journal = {International Journal of Law and Society},
  volume = {5},
  number = {1},
  pages = {19-27},
  doi = {10.11648/j.ijls.20220501.13},
  url = {https://doi.org/10.11648/j.ijls.20220501.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijls.20220501.13},
  abstract = {The system of counteracting money laundering and financing of terrorism (ML/FT) is built mainly on analysis of data of obliged entities, risk assessment and application of financial security measures. The growing amounts of data connected with their processing for the purpose of execution of subject instruments requirements improvement of methods in the scope of their acquisition, analysis and management. Thus, this system is also a basis for support of the human factor by means of state-of-the-art technical solutions. Therefore, meta data analyses, machine learning, predictive modelling or semantic modelling of natural language are incorporated in the assessment of ML/FT threats. The assumption is that each of these support methods must simplify and accelerate as well as reduce the costs of the processes of identification of ML/FT threats. The data analysis techniques used are aimed at - in the initial phase, before establishing a relationship with the obliged entity - the search for primary data, their verification and determination of the purpose of the client's activity, which may generate a threat. In the advanced phase - ongoing relations with the obliged entity - with the control of its behavior in the profit / risk relationship for safety and the introduction of "drivers" [controller] or "security bells" to the offered products - which is associated with the need to counteract the threat.},
 year = {2022}
}

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TY  - JOUR
T1  - Application of Typing and Analysis of Reconnaissance Information for the Purpose of Its Transformation into the Financial Information Administered by FIUs
AU  - Matthias Alexander Kedzierski
Y1  - 2022/01/14
PY  - 2022
N1  - https://doi.org/10.11648/j.ijls.20220501.13
DO  - 10.11648/j.ijls.20220501.13
T2  - International Journal of Law and Society
JF  - International Journal of Law and Society
JO  - International Journal of Law and Society
SP  - 19
EP  - 27
PB  - Science Publishing Group
SN  - 2640-1908
UR  - https://doi.org/10.11648/j.ijls.20220501.13
AB  - The system of counteracting money laundering and financing of terrorism (ML/FT) is built mainly on analysis of data of obliged entities, risk assessment and application of financial security measures. The growing amounts of data connected with their processing for the purpose of execution of subject instruments requirements improvement of methods in the scope of their acquisition, analysis and management. Thus, this system is also a basis for support of the human factor by means of state-of-the-art technical solutions. Therefore, meta data analyses, machine learning, predictive modelling or semantic modelling of natural language are incorporated in the assessment of ML/FT threats. The assumption is that each of these support methods must simplify and accelerate as well as reduce the costs of the processes of identification of ML/FT threats. The data analysis techniques used are aimed at - in the initial phase, before establishing a relationship with the obliged entity - the search for primary data, their verification and determination of the purpose of the client's activity, which may generate a threat. In the advanced phase - ongoing relations with the obliged entity - with the control of its behavior in the profit / risk relationship for safety and the introduction of "drivers" [controller] or "security bells" to the offered products - which is associated with the need to counteract the threat.
VL  - 5
IS  - 1
ER  -

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Author Information

Matthias Alexander Kedzierski

Postgraduate Studies, Kozminski University, Warsaw, Poland

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Plain Text BibTeX RIS

APA Style

Matthias Alexander Kedzierski. (2022). Application of Typing and Analysis of Reconnaissance Information for the Purpose of Its Transformation into the Financial Information Administered by FIUs. International Journal of Law and Society, 5(1), 19-27. https://doi.org/10.11648/j.ijls.20220501.13

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ACS Style

Matthias Alexander Kedzierski. Application of Typing and Analysis of Reconnaissance Information for the Purpose of Its Transformation into the Financial Information Administered by FIUs. Int. J. Law Soc. 2022, 5(1), 19-27. doi: 10.11648/j.ijls.20220501.13

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AMA Style

Matthias Alexander Kedzierski. Application of Typing and Analysis of Reconnaissance Information for the Purpose of Its Transformation into the Financial Information Administered by FIUs. Int J Law Soc. 2022;5(1):19-27. doi: 10.11648/j.ijls.20220501.13

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@article{10.11648/j.ijls.20220501.13,
  author = {Matthias Alexander Kedzierski},
  title = {Application of Typing and Analysis of Reconnaissance Information for the Purpose of Its Transformation into the Financial Information Administered by FIUs},
  journal = {International Journal of Law and Society},
  volume = {5},
  number = {1},
  pages = {19-27},
  doi = {10.11648/j.ijls.20220501.13},
  url = {https://doi.org/10.11648/j.ijls.20220501.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijls.20220501.13},
  abstract = {The system of counteracting money laundering and financing of terrorism (ML/FT) is built mainly on analysis of data of obliged entities, risk assessment and application of financial security measures. The growing amounts of data connected with their processing for the purpose of execution of subject instruments requirements improvement of methods in the scope of their acquisition, analysis and management. Thus, this system is also a basis for support of the human factor by means of state-of-the-art technical solutions. Therefore, meta data analyses, machine learning, predictive modelling or semantic modelling of natural language are incorporated in the assessment of ML/FT threats. The assumption is that each of these support methods must simplify and accelerate as well as reduce the costs of the processes of identification of ML/FT threats. The data analysis techniques used are aimed at - in the initial phase, before establishing a relationship with the obliged entity - the search for primary data, their verification and determination of the purpose of the client's activity, which may generate a threat. In the advanced phase - ongoing relations with the obliged entity - with the control of its behavior in the profit / risk relationship for safety and the introduction of "drivers" [controller] or "security bells" to the offered products - which is associated with the need to counteract the threat.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - Application of Typing and Analysis of Reconnaissance Information for the Purpose of Its Transformation into the Financial Information Administered by FIUs
AU  - Matthias Alexander Kedzierski
Y1  - 2022/01/14
PY  - 2022
N1  - https://doi.org/10.11648/j.ijls.20220501.13
DO  - 10.11648/j.ijls.20220501.13
T2  - International Journal of Law and Society
JF  - International Journal of Law and Society
JO  - International Journal of Law and Society
SP  - 19
EP  - 27
PB  - Science Publishing Group
SN  - 2640-1908
UR  - https://doi.org/10.11648/j.ijls.20220501.13
AB  - The system of counteracting money laundering and financing of terrorism (ML/FT) is built mainly on analysis of data of obliged entities, risk assessment and application of financial security measures. The growing amounts of data connected with their processing for the purpose of execution of subject instruments requirements improvement of methods in the scope of their acquisition, analysis and management. Thus, this system is also a basis for support of the human factor by means of state-of-the-art technical solutions. Therefore, meta data analyses, machine learning, predictive modelling or semantic modelling of natural language are incorporated in the assessment of ML/FT threats. The assumption is that each of these support methods must simplify and accelerate as well as reduce the costs of the processes of identification of ML/FT threats. The data analysis techniques used are aimed at - in the initial phase, before establishing a relationship with the obliged entity - the search for primary data, their verification and determination of the purpose of the client's activity, which may generate a threat. In the advanced phase - ongoing relations with the obliged entity - with the control of its behavior in the profit / risk relationship for safety and the introduction of "drivers" [controller] or "security bells" to the offered products - which is associated with the need to counteract the threat.
VL  - 5
IS  - 1
ER  -

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