Implementation of XGBoost Ensemble Learning Model for Detecting Money Laundering
2021 ◽
Vol 9
(VII)
◽
pp. 312-316
Keyword(s):
Money laundering is the illegal process of concealing the origins of money obtained illegally by passing it through a complex sequence of banking transfers. Currently banks use rule based systems to identify the suspicious transactions which could be used for money laundering. However these systems generate a large number of false positives which leads the banks to spend a huge amount of money and time in investigating the false positives. Hence, in this paper, the monitoring of transactions is to be done using XGBoost machine learning algorithm in order to reduce the number of false positives and to increase the probability of identifying true positives.
2014 ◽
Vol 687-691
◽
pp. 2693-2697
2007 ◽
Vol 29
◽
pp. 79-103
◽
2015 ◽
Vol 03
(03)
◽
pp. 1567-1570
◽
Keyword(s):
2019 ◽
Vol 7
(3)
◽
pp. 83-88
2019 ◽
Vol XVI
(4)
◽
pp. 95-113