System supporting money laundering detection

Dreżewski, Rafał; Sepielak, Jan; Filipkowski, Wojciech

doi:10.1016/j.diin.2012.04.003

Cited by 33 publications

(16 citation statements)

References 5 publications

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“…Proposed systems in [11] [12] used k-means approach to categorize data into specific group before transferring to neural network system. Similarly, clustering using graph technique was required in proposed system [13] to arrange data into proper subset based on transaction frequency. In general, all of above solutions strongly depends on transaction frequency and require lots of complex formula for pre-processing step.…”

Section: A Related Workmentioning

confidence: 99%

Exploration of the effectiveness of expectation maximization algorithm for suspicious transaction detection in anti-money laundering

Chen

Nazir

et al. 2014

2014 IEEE Conference on Open Systems (ICOS)

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Money laundering refers to activities that disguise money receive through illegal operations and make them become legitimate. It leaves serious consequence that may lead to economy corruption. Extensive research has been conducted to investigate proper solution for suspicious transactions detection. In the realm of clustering approaches, traditional research only concentrate on k-means as the best technique so far. On the other hand, although belongs to the same class, there is a lack of studies conducted in employing Expectation Maximization (EM) for Anti-Money Laundering (AML). The objective of this study is to exploit the advantages of EM for suspicious transaction detection. Data used in this study was obtained through a local bank in Malaysia. Subsets of crucial attributes were selected using genetic search and best first search algorithm. Results indicate that critical fields required for clustering phase include amount, number of credit & debit as well as its sum. The outcome of this study shows that EM overwhelmed traditional clustering method k-means for AML in terms of detecting correct suspicious and normal transactions. This lays the groundwork of employing EM in this field. However, further research is needed using different dataset of other banks in order to clarify the effectiveness of EM in AML.

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Section: A Related Workmentioning

confidence: 99%

Exploration of the effectiveness of expectation maximization algorithm for suspicious transaction detection in anti-money laundering

Chen

Nazir

et al. 2014

2014 IEEE Conference on Open Systems (ICOS)

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“…Since then researchers have investigated both machine-learning and traditional statistical approaches to detect money laundering (Irwin et al, 2012, Bidabad, 2017, Chang et al, 2008, Deng et al, 2009, Drezewski et al, 2012, Colladon and Remondi, 2017, Gilmour, 2017, Ju and Zheng, 2009, Ngai et al, 2011, Perols, 2011, Regan et al, 2017, Savage et al, 2016, Savage, 2017, Turner and Irwin, 2018, Unger et al, 2011, Wang et al, 2007, Zdanowicz, 2004a, Zdanowicz, 2009, Zhang et al, 2003, Gao, 2009). Zhang et al authors on using the system on real-world data found it was able to detect suspicious activity with a low rate of false positives.…”

Section: Detection Of Money Launderingmentioning

confidence: 99%

A review of money laundering literature: the state of research in key areas

Tiwari

Gepp

Kumar

2020

PAR

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Purpose:The purpose of this study is to review the literature on money laundering and its related areas. The main objective is to identify any gaps in the literature and direct attention towards addressing them.Design/Methodology/Approach: A systematic review of the money laundering literature was conducted with an emphasis on the Pro-Quest, Scopus and Science-Direct databases. Broad research themes were identified after investigating the literature. The theme about the detection of money laundering was then further investigated. The major approaches of such detection are identified as well as research gaps that could be addressed in future studies. Findings:The literature on money laundering can be classified into the following six broad areas: (i) anti-money laundering framework and its effectiveness, (ii) the effect of money laundering on other fields and the economy, (iii) the role of actors and their relative importance, (iv) the magnitude of money laundering, (v) new opportunities available for money laundering and (vi) detection of money laundering. Most studies about the detection of money laundering have focused on the use of innovative technologies, banking transactions, or real estate and trade-based money laundering. However, the literature on the detection of shell companies being explicitly used to launder funds is relatively scarce.Originality/Value: This paper provides insights into an area related to money laundering where research is relatively scant. Shell companies incorporated in the UK alone were identified to be associated with laundering 80 billion pounds of stolen money between 2010 and 2014. The use of these entities to launder billions of dollars as witnessed through the laundromat schemes and several data leaks clearly indicate the need to focus on illicit financial flows through such entities.

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“…Considering various studies we also include some determinant factors of economic and financial crimes which are used by the literature as control variables, such as economic development (Husted, 1999;Schneider & Klingmair, 2004;Tsakumis et al, 2007;Achim et al, 2018), governance (Medina & Schneider, 2018;Richardson, 2008;Torgler & Schneider, 2007), tax burden (Dreher & Schneider, 2010;McGee, 2012;Schneider & Klinglmair, 2004;Torgler & Schneider, 2007), audit quality (Drezewski et al, 2012;Vaithilingam & Nair, 2009;Nikoloska & Simonovski, 2012), unemployment rate (Dell' Anno & Solomon, 2008;Dobre et al, 2010;Williams & Schneider, 2016;Medina & Schneider, 2018), press freedom (Brunetti & Weder, 2003;Kalenborn & Lessmann, 2013;Lv, 2017;Florescu & Cuceu, 2019) or shadow economy (Heshmati, 2016, p. 131), religion (Heinemann & Schneider, 2011;Ko & Moon, 2014;McGee et al, 2015) and legal origin (La Porta et al 1997Lv, 2017).…”

Section: Control Variablesmentioning

confidence: 99%

Does Technology Matter for Combating Economic and Financial Crime? A Panel Data Study

Achim

Borlea

Văidean

2021

Technological and Economic Development of Economy

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In this paper we analyze the influence of technology on the level of the economic and financial crime, using data for 185 countries over the 2012–2015 time period and controlling for many important variables. First, we find that on average, the size of the economic and financial crime in low income countries is about double compared to that of high income countries, while their Research and development expenditure (as % of GDP) are about four times lower than in high income countries. We find clear evidence that increased technology reduces the size of the economic and financial crime. In addition, we find that Research and development expenditure (% of GDP) matter more in reducing the economic and financial crime in low income countries than they do in high income countries. Our main findings are generally stable after conducting some robustness checks. From a policy perspective, our study may prove to be of great use to decision makers of the states, to government and non-governmental enterprises and to potential investors within different markets.

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System supporting money laundering detection

Cited by 33 publications

References 5 publications

Exploration of the effectiveness of expectation maximization algorithm for suspicious transaction detection in anti-money laundering

Exploration of the effectiveness of expectation maximization algorithm for suspicious transaction detection in anti-money laundering

A review of money laundering literature: the state of research in key areas

Does Technology Matter for Combating Economic and Financial Crime? A Panel Data Study

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