Money laundering and fraud transactions analysis of mobile banking in Bangladesh using machine learning

Abstract

Mobile banking is a system that allows customers of a financial institution to conduct a number of financial transactions through a mobile device such as a mobile phone. It is quick and free, and it usually allows you to perform a variety of activities, such as paying bills, mobile topup and exchanging currency, without having to visit or call your branch. As a developing nation, Bangladesh is seeing an increase in online banking. People are still reliant on online banking because it makes a man's life much easier. Mobile banking services such as Rocket, bKash, and Nagad are now available in the region. While mobile banking makes life easier, money laundering incidents do occur from time to time. This thesis researches the detection of money laundering and fraud transactions using machine learning techniques. These techniques have potential benefits over time consuming human investigations to detect money laundering transactions. Seven traditional machine learning classification algorithms Logistic Regression, Random Forest, Naïve Bayes, support vector machine, Neural network, Decision tree, K nearest neighbor algorithms to complete this research work and find the concluded delimiter.