Abstract
Legal entity analysis nowadays is an important issue for enterprises, when they select their providers, clients, or other cooperation partners. Considering regular amendments to normative documents, changes in the performance of legal entities, and continuously developing new schemes for law violations, the paper suggests application of continuous engineering approach in development of IT solutions for legal entity analysis. Continuous engineering helps to ensure that the developed solutions can align with the continuous changes in regulatory requirements, legal entity performance, and available data sources used for analysis. The paper contributes the model for continuous IT solution engineering for legal entity analysis that supports various analysis factors. The model is theoretically evaluated to assess its ability to meet the challenges of legal entity analysis. Ability to implement the model is tested with the help of an IT solution prototype.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Twombly, J., Shuman, J.: The Partner Portfolio Manager : Shining the Spotlight on. Rhythm Bus, vol. 2 (2013)
EUROPA - Competition - List of NACE codes. https://ec.europa.eu/competition/mergers/cases/index/nace_all.html
Law on the Prevention of Money Laundering and Terrorism and Proliferation Financing. https://likumi.lv/ta/en/en/id/178987-law-on-the-prevention-of-money-laundering-and-terrorism-and-proliferation-financing
Directive (EU) 2018/843 of the European Parliament and of the Council of 30 May 2018 amending Directive (EU) 2015/849 on the prevention of the use of the financial system for the purposes of money laundering or terrorist financing, and amending Directives. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32018L0843
Welcome to LexisNexis Legal & Professional. https://www.lexisnexis.com/en-us/home.page
Accuity | Data & software to control financial risk, compliance & payments. https://accuity.com/
Legal acts in the AML/CFT area – FKTK. https://www.fktk.lv/en/law/general/legal-acts-in-the-aml-cft-area/
Sandkuhl, K.: Aligning software architecture and business strategy with continuous business engineering. In: Metzger, A., Persson, A. (eds.) CAiSE 2017. LNBIP, vol. 286, pp. 14–26. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60048-2_2
Rudzajs, P., Kirikova, M.: Conceptual correspondence monitoring: multimode information logistics approach. Complex Syst. Inf. Model. Q. 1, 57 (2014)
Wieringa, R.J.: Design Science Methodology for Information Systems and Software Engineering. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-43839-8
Beer, S.: Diagnosing the System for Organizations. Wiley, New York (1995)
Kirikova, M.: Work systems paradigm and frames for fractal architecture of information systems. In: Nurcan, S., Pimenidis, E. (eds.) CAiSE 2014. LNBIP, vol. 204, pp. 165–180. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19270-3_11
The ArchiMate® Enterprise Architecture Modeling Language. https://www.opengroup.org/archimate-forum/archimate-overview
Archi – Open Source ArchiMate Modelling. https://www.archimatetool.com/
ADOit platform. https://www.boc-group.com/en/adoit/
O’Connor, R.V., Elger, P., Clarke, P.M.: Continuous software engineering-a microservices architecture perspective. J. Softw. Evol. Process. 29, e1866 (2017)
Fitzgerald, B., Stol, K.-J.: Continuous software engineering: a roadmap and agenda. J. Syst. Softw. 123, 176–189 (2017)
Loayza, N., Villa, E., Misas, M.: Illicit activity and money laundering from an economic growth perspective: a model and an application to Colombia. J. Econ. Behav. Organ. 159, 442–487 (2019)
Badal-Valero, E., Alvarez-Jareño, J.A., PavÃa, J.M.: Combining Benford’s Law and machine learning to detect money laundering. An actual Spanish court case. Forensic Sci. Int. 282, 24–34 (2018)
Singh, K., Best, P.: Anti-money laundering: using data visualization to identify suspicious activity. Int. J. Account. Inf. Syst. 34, 100418 (2019)
Imanpour, M., Rosenkranz, S., Westbrock, B., Unger, B., Ferwerda, J.: A microeconomic foundation for optimal money laundering policies. Int. Rev. Law Econ. 60, 105856 (2019)
Gao, S., Xu, D.: Conceptual modeling and development of an intelligent agent-assisted decision support system for anti-money laundering. Expert Syst. Appl. 36, 1493–1504 (2009)
Alexandre, C., Balsa, J.: Integrating client profiling in an anti-money laundering multi-agent based system. New Advances in Information Systems and Technologies. AISC, vol. 444, pp. 931–941. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31232-3_88
Palshikar, G.K.: Detecting frauds and money laundering: a tutorial. In: Srinivasa, S., Mehta, S. (eds.) BDA 2014. LNCS, vol. 8883, pp. 145–160. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13820-6_12
McCarthy, K.J., van Santen, P., Fiedler, I.: Modeling the money launderer: microtheoretical arguments on anti-money laundering policy. Int. Rev. Law Econ. 43, 148–155 (2015)
Gilmour, N.: Understanding the practices behind money laundering - a rational choice interpretation. Int. J. Law Crime Justice 44, 1–13 (2016)
Ardizzi, G., De Franceschis, P., Giammatteo, M.: Cash payment anomalies and money laundering: an econometric analysis of Italian municipalities. Int. Rev. Law Econ. 56, 105–121 (2018)
Hong, X., Liang, H., Gao, Z.: Adaptive resource allocation for anti-money laundering based on SMDP. In: Xu, K., Zhu, H. (eds.) WASA 2015. LNCS, vol. 9204, pp. 190–200. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-21837-3_19
Jayasree, V., Siva Balan, R.V.: Money laundering regulatory risk evaluation using bitmap index-based decision tree. J. Assoc. Arab. Univ. Basic Appl. Sci. 23, 96–102 (2017)
Isa, Y.M., Sanusi, Z.M., Haniff, M.N., Barnes, P.A.: Money laundering risk: from the bankers’ and regulators perspectives. Procedia Econ. Financ. 28, 7–13 (2015)
Shanaev, S., Sharma, S., Ghimire, B., Shuraeva, A.: Taming the blockchain beast? Regulatory implications for the cryptocurrency market. Res. Int. Bus. Financ. 51, 101080 (2020)
Eifrem, E.: How graph technology can map patterns to mitigate money-laundering risk. Comput. Fraud Secur. 2019, 6–8 (2019)
Fronzetti Colladon, A., Remondi, E.: Using social network analysis to prevent money laundering. Expert Syst. Appl. 67, 49–58 (2017)
Acknowledgments
The work on this paper is supported by ERAF research No. 1.2.1.1/18/A/003 project No. 1.9.
We acknowledge BOC Company for supporting the research group by ADOit tool for scientific experiments, and developers of ArchiMate Language and developers of Archi tool for providing excellent freely available modelling language and tool.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Kirikova, M., Miltina, Z., Stasko, A., Pincuka, M., Jegermane, M., Kiopa, D. (2020). The Model for Continuous IT Solution Engineering for Supporting Legal Entity Analysis. In: Buchmann, R.A., Polini, A., Johansson, B., Karagiannis, D. (eds) Perspectives in Business Informatics Research. BIR 2020. Lecture Notes in Business Information Processing, vol 398. Springer, Cham. https://doi.org/10.1007/978-3-030-61140-8_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-61140-8_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-61139-2
Online ISBN: 978-3-030-61140-8
eBook Packages: Computer ScienceComputer Science (R0)