Exploration of the effectiveness of expectation maximization algorithm for suspicious transaction detection in anti-money laundering

Abstract

Money laundering refers to activities that disguise money receive through illegal operations and make them become legitimate. It leaves serious consequence that may lead to economy corruption. Extensive research has been conducted to investigate proper solution for suspicious transactions detection. In the realm of clustering approaches, traditional research only concentrate on k-means as the best technique so far. On the other hand, although belongs to the same class, there is a lack of studies conducted in employing Expectation Maximization (EM) for Anti-Money Laundering (AML). The objective of this study is to exploit the advantages of EM for suspicious transaction detection. Data used in this study was obtained through a local bank in Malaysia. Subsets of crucial attributes were selected using genetic search and best first search algorithm. Results indicate that critical fields required for clustering phase include amount, number of credit & debit as well as its sum. The outcome of this study shows that EM overwhelmed traditional clustering method k-means for AML in terms of detecting correct suspicious and normal transactions. This lays the groundwork of employing EM in this field. However, further research is needed using different dataset of other banks in order to clarify the effectiveness of EM in AML.