A Data Mining-Based Solution for Detecting Suspicious Money Laundering Cases in an Investment Bank

Le-Khac, Nhien-An; Markos, Sammer; Kechadi, Tahar

doi:10.1109/dbkda.2010.27

Cited by 26 publications

(13 citation statements)

References 9 publications

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“…Therefore, most of recent AML systems design a separated task to cluster data into suspicious and normal group before transferring it for classification. 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.…”

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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“…avoid mistakes when faced with persons with the same name. Unfortunately, these and other methods have proved to be insufficient [15].…”

Section: Related Workmentioning

confidence: 99%

Money Laundering Identification Using Risk and Structural Framework Estimation

Krishnapriya¹

2017

BIJDM

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Money laundering refers to activities that disguise money receive through illegal operations and make them legitimate. It leaves serious consequence that may lead to economy corruption. One such problem consisting large amounts of money transferring through various accounts by the same person or entity is Money Laundering. Money laundering scheme is quite a complex procedure. It receipts some empathetic of the deposit transporting actions at many phases. Detecting money laundering activities is a challenging task due we propose a risk model framework in Structural Money Laundering based on Risk Evolution Detection Framework (SMLRDF). The connection deceitful deal trails a sequence of connected money laundering arrangements, structural money laundering uses sequence matching, social network investigation, and multifaceted happening processing, case-based examination. The context that put on case discount approaches to increasingly lessen the input data set to a knowingly minor size. The context images the summary data to discovery couples of communications through common qualities and performances that are possibly complicated in ML actions. It then applies a clustering method to detect potential Money Laundering (ML groups), then the risk model is used to create a valid and accurate transaction scoring system to be utilized in an ML prevention system. SMLRDFdependent risk modeling, which captures the hidden, and dynamic, relations among none-transacted entities. SMLRDF has components to collect data, run them against business rules and evolution models, run detection algorithms and use social network analysis to connect potential participants.

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“…Extraction and cleaning of data is done in data preprocessing [5]. Data quality is one of the challenging tasks.…”

Section: Detecting Suspicious Money Laundering Cases In An Investmentmentioning

confidence: 99%

“…New application gives new idea for Arabic documents [4] Bank transactions Detecting money laundering system can't run standalone absolutely without analyst [5] running time performance simple and efficient data mining-based solution [6] Automatic insertion of data into database…”

Section: Data Mining For Security Applicationsmentioning

confidence: 99%