Intelligent Anti-Money Laundering System

Gao, Shijia; Xu, Dongming; Wang, Huaiqing; Wang, Yingfeng

doi:10.1109/soli.2006.328967

Cited by 23 publications

(11 citation statements)

References 6 publications

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“…Today some detection techniques are used in money laundering prevention which are represented in reference [2,3] and decision tree classification and outliner analysis tools are introduced in detection of suspicious money transactions. In references [4,5,6] some techniques are mentioned such as clustering analysis tools, clustering analysis tools, back-up vector machine, link analysis, intelligent agents and neural networks.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Destructive e-Banking Users’ Loyalty by Applying RFM

Maryam¹,

Ali²

2019

AJARR

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World economy has encountered money laundering phenomenon and its destructive effects on economy of countries over the last decades. Money laundry detection is one of the fields in which data mining tools can be very helpful in detecting it. Nowadays, recognizing credible customers to present banking facilities to them is of high importance. On the other hand, by increasing cheating in banking, detecting the fraud customers is also important. In this study, the decision tree was being trained by providing a fuzzy decision tree and users’ behavioral vectors. The output of fuzzy tree can signify users’ risking behaviors. Some features of customers’ accounts need to be extracted in order to identify the people with high risks. For instance, the variance of money transaction and money transfer can be done. In another part of the study, RFM (Recency, Frequency, Monetary) features and MLP (Multi-Layer Perception) classifications were used to identify the loyal customers. The three-fold features of refreshment, frequency, and shopping amount was completely discussed for every customer, whereby the customers’ scores were established as well. This classification aimed to categorize the credible users of neural networks. The results of current research indicate that the represented techniques possess high precision values, in comparison with previous techniques.

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Section: Related Workmentioning

confidence: 99%

Evaluation of Destructive e-Banking Users’ Loyalty by Applying RFM

Maryam¹,

Ali²

2019

AJARR

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“…Em Gao et al (2006), uma arquitetura de sistema é definida utilizando um conjunto de agentes especializados, tais como: agentes de coleta de dados (sistemas internos e informações externas); agentes de monitoramento para acompanhamento do perfil cadastral do cliente e das transações realizadas; e um agente que emite relatórios e alertas sobre possíveis operações de BC. Apesar da boa proposta de arquitetura, o problema crucial do volume de análises submetidas ao analista humano não é enfrentando, aliás, pode até ser agravado com a automatização da fase de sinalização de transações suspeitas.…”

Section: Arquiteturas De Sistemasunclassified

“…Outra abordagem baseada em agentes foi apresentada em Xuan & Pengzhu (2007). A arquitetura proposta é semelhante à descrita em Gao et al (2006), porém, com evolução dos agentes incluindo as características de negociação, diagnóstico e autoaprendizagem. As características dos agentes é o ponto forte desta proposta, contudo, também encerra sua contribuição nas sinalizações para o analista humano.…”

Section: Arquiteturas De Sistemasunclassified

“…As evoluções das bases de conhecimento e o aprendizado previstos no sistema visam, primordialmente, mitigar o risco da ocorrência de falso positivo e/ou falso negativo, existente em sistema baseados unicamente em um conjunto de regras e padrões de comportamento (Gao et al, 2006) Em função do sigilo legal envolvido nas informações, a Figura 7 mostra apenas dois perfis com parte dos detalhes envolvidos, devidamente descaracterizados. No entanto, é possível observar o texto de algumas regras que ressaltam a relevância das suspeitas, como por exemplo a regra legal BCXX2016003, originada no normativo do Banco Central Brasileiro que sinaliza uma queda acentuada na movimentação de um perfil que, normalmente, tem alta movimentação.…”

Section: Modelo Dos Agentesunclassified

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Um Sistema Multiagente no Combate ao Branqueamento de Capitais

Alexandre¹,

Balsa²

2017

risti

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Resumo: Branqueamento de capitais é um crime que possibilita o financiamento de outros crimes, por isso ele é importante para as organizações criminosas e seu combate é motivo de mobilização das nações do mundo inteiro. O processo de anti-branqueamento de capitais não evoluiu como esperado pois tem priorizado a sinalização de transações suspeitas. O crescente aumento no volume de transações tem sobrecarregado o indispensável trabalho humano de avaliação final das sinalizações. Este artigo apresenta um sistema multiagente que objetiva ir além da captura de transações suspeitas, buscando auxiliar o especialista humano na análise das suspeições. Os agentes criados utilizam técnicas de data mining para criação de perfis de comportamento transacional; aplicam as regras obtidas no aprendizado em conjunto com regras especificas baseadas em aspectos legais e nos perfis criados para captura de transações suspeitas; e analisam estas transações sinalizadas indicando ao especialista humano aquelas que necessitam de análise mais detalhada.Palavras-chave: Sistemas multiagente; agentes inteligentes; data mining; antibranqueamento de capitais. A Multi-Agent System in the Combat Against Money LaunderingAbstract: Money laundering is a crime that makes it possible to finance other crimes, for this reason, it is important for criminal organizations and their combat is prioritized by nations around the world. The anti-money laundering process has not evolved as expected because it has prioritized only the signaling of suspicious transactions. The constant increasing in the volume of transactions has overloaded the indispensable human work of final evaluation of the suspicions. This article presents a multiagent system that aims to go beyond the capture of suspicious transactions, seeking to assist the human expert in the analysis of suspicions. The agents created use data mining techniques to create transactional behavioral profiles; apply rules generated in learning process in conjunction with specific rules based on legal aspects and profiles created to capture suspicious transactions; and analyze these suspicious transactions indicating to the human expert those that require more detailed analysis.

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“…Several detecting technologies nowadays have been applied in anti-money laundering field are proposed, in [2,3], decision tree classification and outlier analysis tools were introduced in extracting suspicious financial transaction. And in [4,5,6], such as classification analysis tools (Bayesian classification, Genetic algorithms etc), cluster analysis tools, support vector machine(SVM), case-based reasoning, rough set, link analysis, intelligent agents and neural network were mentioned. Outlier detection is used to discover data object which is not consistent with the features of general data, but it can't recognize the suspicious behavior of peer comparison.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Anti-Money Laundering Method for Detecting Risky Users in the Banking Systems

Heidarinia¹,

Harounabadi²,

Sadeghzadeh³

2014

IJCA

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During the last decades, universal economy has experienced money laundering and its destructive impact on the economy of the countries. Money laundering is the process of converting or transferring an asset in order to conceal its illegal source or assist someone that is involved in such crimes. Criminals generally attempt to clean the sources of the funds obtained by crime, using the banking system. Due to the large amount of information in the banks, detecting such behaviors is not feasible without anti-money laundering systems. Money laundering detection is one of the areas, where data mining tools can be useful and effective.In this research, some of the features of the users are extracted from their profiles by studying them. These features may include large financial transactions in risky areas regarding money laundering, reactivation of dormant accounts with considerable amounts, etc. Network training is performed by designing a fuzzy system, developing an adaptive neuro-fuzzy inference system and adding feature vectors of the users to it. The network output can determine the riskiness of the user behavior. The evaluation results reveal that the proposed method increases the accuracy of detecting risky users.

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Intelligent Anti-Money Laundering System

Cited by 23 publications

References 6 publications

Evaluation of Destructive e-Banking Users’ Loyalty by Applying RFM

Evaluation of Destructive e-Banking Users’ Loyalty by Applying RFM

Um Sistema Multiagente no Combate ao Branqueamento de Capitais

An Intelligent Anti-Money Laundering Method for Detecting Risky Users in the Banking Systems

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