Detecting Mobile Money Laundering Using KPCA as Feature Selection Method

Abstract

In recent years, mobile phone payment systems have been extensively used in developed countries. Frauds are affecting the economy of the whole world. Different kinds of mobile money frauds are credit card, bank fraud, insurance fraud and financial fraud. In this paper, we discussed financial fraud and proposed an effectiveness method for money laundering. Payment system in fraud divided into four parts, point of sale, mobile payment platform, mobile payment independent and bill payment through mobile. Mobile phones are great source of service for financial transactions. Our objective is to identify the misuse of mobile money transaction and to prevent fraud from financial transaction to save the money. Financial Action Task Force (FATF) is an organization that views internationally money laundering. Financial Action Task Force continuously strengthens its standards for dealing with new risks. The Financial Action Task Force monitors countries to ensure the implementation the Financial Action Task Force Standards and holds countries to account that do not comply. This paper proposes hybrid Kernel Principal Component Analysis method used on as feature selection method and investigates the performance of Decision Tree and Boost classification Machine learning method. We applied Area under the ROC curve (AUC) and confusion matrix after using the feature selection method. We found the results of Decision Tree Training, testing and Boost with different Sampling of both datasets and Boost has better performance than Decision Tree.