A Survey on Clustering Techniques based on Money Laundering Fraud Detection

Nowadays, banking frauds like money laundering becomes more and more sophisticated. Money laundering is a process by which legally obtained funds are given the appearance of having been legally obtained. There are various techniques for detecting the different types of frauds. These techniques include different data mining techniques. Data mining is a process of discovering the interesting and useful patterns and relationships in large volumes of data. Data mining techniques such as cluster analysis, classification, neural network, and prediction have been used to detect the banking fraud. Clustering analysis groups the similar objects into a cluster in such way that intracluster similarity of objects is high and intercluster similarity is low. The clustering result is calculated based on cluster quality and cluster performance. This paper presents survey on various clustering methods. These clustering techniques measure the similarity based on some criterion functions.