An Intelligent and Secure Framework for Anti-Money Laundering

Predicting Money Laundering Using Machine Learning and Artificial Neural Networks Algorithms in Banks

“…The Literature On Aml And Computational Intelligence Money laundering is the process of laundering proceeds earned through criminal activities into clean money that appears to come from a legitimate source. In other words, it places illegally sourced funds into the standard nancial cycle or money circulation process by disguising them as clean money (Ardizzi et al, 2014;Sobh, 2020). Money laundering involves processing funds from underground activities like terrorism, cybercrime, drug tra cking, corruption, tax evasion, and quasi-legal activities such as concealment of income from public authorities (Habib et al, 2018;Karim et al, 2020;Tiwari et al, 2020).…”

“…Likewise, Loayza et al (2019) highlighted in their paper that Colombia saw a phase in 2001 and 2002 when the total value of illicit income was equal to 12% of its GDP and the volume of laundered assets increased from 8-14% of Colombia's total GDP. As a result, illegal activities like as tax evasion, corruption, extortion, and drug tra cking result in income loss for the government, internal market instability, erosion of private-sector efforts, volatile currency and interest rates, and political upheaval (Ofoeda et al, 2020;Sobh, 2020).…”

“…Money laundering is primarily a three-step process that involves placement, layering, and integration stages (Sobh, 2020;Tiwari et al, 2020). The rst step, known as placement, involves the introduction of illegal funds into the nancial system; the second stage, known as layering, involves a series of ctitious transactions that mask the true source of the cash (Ardizzi et al, 2012;Ofoeda et al, 2020).…”

Predicting Money Laundering using Machine Learning and Artificial Neural Networks Algorithms in Banks

“…Money laundering is primarily a three-step process that involves placement, layering, and integration stages (Sobh, 2020;Tiwari et al, 2020). The rst step, known as placement, involves the introduction of illegal funds into the nancial system; the second stage, known as layering, involves a series of ctitious transactions that mask the true source of the cash (Ardizzi et al, 2012;Ofoeda et al, 2020).…”

Detecting Roles of Money Laundering in Bitcoin Mixing Transactions: A Goal Modeling and Mining Framework

“…The ontology consists of domain knowledge and a set of Semantic Web rules, and the native reasoning support in the ontology was used to deduce new knowledge from the predefined rules about suspicious transactions. A Secure Intelligent Framework for Anti-Money Laundering was presented to make use of an intelligent formalism by using ontologies and rule-based planning [12]. Bayesian approaches were adopted to assign a risk score to money laundering-related behavior [13].…”