Open AccessImplementation of XGBoost Ensemble Learning Model for Detecting Money Laundering
Author(s) -
Abarna Ramprakash
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.36323
Subject(s) - false positive paradox , money laundering , computer science , true positive rate , process (computing) , sequence (biology) , order (exchange) , computer security , artificial intelligence , finance , business , operating system , biology , genetics
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.