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Introduction
The combat against money laundering is assigned a top priority in the economies of many countries. Today, the Internet provides the opportunity to have digital money such as Bitcoins, Zen, and so on, while the rapidly developing technology becomes one of the agents in this struggle. For example, outlier detection approaches and principles along with the increased investment in technology development may be noted as important strategies.
Main body
The first issue I have learned is that the main problem lies in the presence of Big Data that includes trillions of transactions of various financial organizations and systems. The identification of suspicious transactions is impeded by the fact that many money launderers tend to use sets of small transactions to make them look more trustworthy (Unger et al. 87). Instead of using paper reporting checks, law enforcement agencies apply digital means of fighting money laundering. For example, CLEAR software is designed to investigate suspicious transactions and identify users. In the future, it is possible to use CLEAR and develop more advanced software to track and reveal outliers.
The second issue that was unknown to me previously is that money laundering can be performed through casinos. A casino is not a bank, which means that it is not a financial institution. However, the list of casino services includes not only entertainment but also a number of currency operations: beginning with money transfers from one account to another and ending with the safekeeping of securities (Williamson et al. 20).
The case of E-Gold shows that criminals always strive to improve and change their methods, but law enforcement also upgrades its methods. In addition, laundering cash through online games seems to be associated with risks since the leading countries tend to transition to digital currency. Sweden, for example, aims at the transition to a cashless society, which will make it more difficult for criminals to hide their transactions.
The finding that was mentioned above is useful for preparing strategies to eliminate money laundering. In particular, while creating a full-fledged anti-money laundering system, it becomes evident that a compromise should be reached. First, the increase in costs at both public and private levels to finance such initiatives and the restriction of freedom of economic entities’ actions should be targeted (Godinho 263). Second, the efficiency of the system and tightening control over the implementation of law enforcement requirements should also be taken into account. The introduction of new mechanisms, especially costly ones, is advisable to implement only when the benefits from their use exceed the untapped potential of the already existing systems.
Conclusion
The third issue I have learned refers to approaches to identifying an outlier among a variety of transactions. For example, I knew more about Mahalanobis distance that measures the distance between a distribution and a point. In the problem of finding the probability that a given point belongs to an unknown set, which is given by several known points, the goal is to determine the center of the mass of this set (Kannan and Somasundaram 32).
Mahalanobis distance is widely used in clustering and classification problems (Gong et al. 250). In order to determine which of the known classes the point belongs to, it is necessary to find the covariance matrices for all the classes and take the class with the smallest to the point. This knowledge may be used in the future to analyze transactions and integrate statistical methods into new programs.
Works Cited
Godinho, Jorge. “The Prevention of Money Laundering in Macau Casinos.” Gaming Law Review and Economics, vol. 17, no. 4, 2013, pp. 262-274.
Gong, Shaogang, et al. Person Re-Identification. Springer Science & Business Media, 2014.
Kannan, Ananda, and Kanagasabapathi Somasundaram. “Selection of Optimal Mining Algorithm for Outlier Detection-An Efficient Method to Predict/Detect Money Laundering Crime in Finance Industry.” Elysium Journal of Engineering Research and Management, vol. 1, no. 1, 2014, pp. 30-42.
Unger, Brigitte, et al. The Economic and Legal Effectiveness of the European Union’s Anti-Money Laundering Policy. Edward Elgar Publishing, 2014.
Williamson, Cindy, et al. “Technology in the Fight against Money Laundering in the New Digital Currency Age.” Thomson Reuters, 2013, pp. 1-30.
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