A new algorithm for money laundering detection based on structural similarity

Soltani, Reza; Nguyen, Uyen Trang; Yang, Yang; Faghani, Mohammad Reza; Yagoub, Alaa; An, Aijun

doi:10.1109/uemcon.2016.7777919

Cited by 27 publications

(16 citation statements)

References 6 publications

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“…Luego del análisis de la literatura, los modelos escogidos fueron: Redes bayesianas dinámicas [18], Redes neuronales de base radial [19], Máquinas de soporte vectorial [20], Clúster de dos fases [21], Maximización de la esperanza [22] y Sequence Matching [23].…”

Section: Variables a Obtenerunclassified

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Máquinas de soporte vectorial y árboles de clasificación para la detección de operaciones sospechosas de lavado de activos

Granados¹

2019

Lámpsakos

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El lavado de activos es un delito que trae consigo un gran número de consecuencias negativas a la sociedad en general. Para mitigar este problema en las entidades financieras, que es donde principalmente se presenta, se han desarrollado sistemas anti lavado de dinero. Lo anterior origina un nuevo problema: los falsos positivos que se obtienen a partir de dichos sistemas, los cuales representan para las entidades financieras pérdidas de dinero, tiempo y foco, al no tratar las verdaderas operaciones inusuales. Se evalúan los principales métodos de detección de operaciones inusuales de lavado de activos que se encuentran en la literatura, para determinar cuáles técnicas ofrecen los mejores resultados y a partir de estas generar un nuevo modelo que mejore los indicadores registrados. A partir de un proceso de revisión y replicación de metodologías de detección de anomalías encontradas en la literatura, se pudo generar un nuevo modelo que presenta mejores métricas a la hora de clasificar operaciones como normales e inusuales, lo cual puede representar para las entidades financieras una manera de disminuir las tasas de falsos positivos en sus sistemas anti lavado.

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Section: Variables a Obtenerunclassified

“…Este modelo busca por medio del algoritmo de maximización de la esperanza, encontrar de manera iterativa el conjunto de datos que maximiza la probabilidad de ser inusuales [22].…”

Section: Maximización De La Esperanzaunclassified

Máquinas de soporte vectorial y árboles de clasificación para la detección de operaciones sospechosas de lavado de activos

Granados¹

2019

Lámpsakos

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“…A context for evolving an smart, discerning scheme of anti-money cleaning model to classify money laundering. Different layers play different roles during the analyzing procedure [1]. Data of Transaction layer and Account Layer are submitted from the root bank branches and have composed the primary sources.…”

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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“…Money laundering is defined as the process of hiding an illicit origin of dirty money and making it seem legitimate and valid (Le- Khac et al, 2016). ML can also be defined as the process of cleaning "dirty" money, which means funds collected from criminal or illegal activities including drug trafficking, illegal gambling and tax evasion (Soltani et al, 2016) and (Salehi et al, 2017). Another definition of ML is the "process of converting unaccountable money into accountable money" (Suresh et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…ML has a negative impact on the global economy, and it is considered a serious problem discussed by countries around the world (Syed Mustapha Nazri et al, 2019). Indeed, ML is the third largest business around the world, accounting for about 2.7% of global gross domestic product (GDP) after the currency exchange and auto industries (Le- Khac et al, 2016;Soltani et al, 2016). The International Monetary Fund estimated that ML proceeds are between 2% and 5% of global GDP (Syed Mustapha Nazri et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Anti-money laundering systems: a systematic literature review

Alsuwailem

Saudagar

2020

JMLC

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Purpose This paper aims to understand and document the state of the art in the anti-money laundering (AML) systems literature. Design/methodology/approach A systematic literature review (SLR) is performed using the Saudi Digital Library. The outputs published as conference proceedings, workshop proceedings, journal articles and books were all considered. The final sample size after omitting out-of-scope selections was 27 documents, which mainly span from 2015 to 2020. Findings The sample is discussed based on a categorization, which demarcates solutions, machine learning, data sources, evaluation methods, implementation tools, sampling techniques and regions of study. Originality/value This SLR could serve as a useful basis for researchers and salient decision-makers, who are seeking to understand the nature and extent of the currently available research into AML systems.

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A new algorithm for money laundering detection based on structural similarity

Cited by 27 publications

References 6 publications

Máquinas de soporte vectorial y árboles de clasificación para la detección de operaciones sospechosas de lavado de activos

Máquinas de soporte vectorial y árboles de clasificación para la detección de operaciones sospechosas de lavado de activos

Money Laundering Identification Using Risk and Structural Framework Estimation

Anti-money laundering systems: a systematic literature review

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