Can I read Analysis of Classifier Algorithms to Detect Anti-Money Laundering on EtoBox?

Analysis of Classifier Algorithms to Detect Anti-Money Laundering by Ashwini Kumar; Sanjoy Das; Vishu Tyagi; Rabindra Nath Shaw; Ankush Ghosh is a book available to read on EtoBox.

What is Analysis of Classifier Algorithms to Detect Anti-Money Laundering about?

In the financial sectors like banking, anti-money laundering (AML) is a very challenging issue. To prevent the money laundering, various set of procedures, government policies, and ordinances are designed which are known as anti-money laundering, where income through illegal actions, e.g., market operations, deal of illegal commodities, and corruption of public funds, and tax evasion, can be stopped. The major objective of the chapter is classification of any transaction correctly as illegal or not. To achieve this, we have used the big data analytics technique for a dataset to identify money laundering activities. The dataset with 10,000 transactions is used in our analysis. The overall process includes data cleaning, statistical analysis, and data mining process. The linear support vector machine and decision tree classifier are used to find money laundering activities. The analysis has been done using Python and customized datasets. The result obtained through analysis is very significant and shows accuracy is higher in the decision tree classifier. Other parameters, namely recall and precision, is also better in the decision tree.

Author

Ashwini Kumar; Sanjoy Das; Vishu Tyagi; Rabindra Nath Shaw; Ankush Ghosh

Publisher

Springer Singapore : Imprint: Springer

Published

2021

Language

EN

ISBN

9789811604089

Subjects

Computer Science, Engineering, Science