Clustering based anomalous transaction reporting

Abstract

Anti-money laundering (AML) refers to a set of financial and technological controls that aim to combat the entrance of dirty money into financial systems. A robust AML system must be able to automatically detect any unusual/anomalous financial transactions committed by a customer. The paper presents a hybrid anomaly detection approach that employs clustering to establish customers' normal behaviors and uses statistical techniques to determine deviation of a particular transaction from the corresponding group behavior. The approach implements a variant of Euclidean Adaptive Resonance Theory, termed as TEART, to group customers in different clusters. The paper also suggests an anomaly index, named AICAF, for ranking transactions as anomalous. The approach has been tested on a real data set comprising of 8.2 million transactions and the results suggest that TEART scales well in terms of the partitions obtained when compared to the traditional K-means algorithm. The presented approach marks transactions having high AICAF values as suspicious.

A Multi-Agent System Based Approach to Fight Financial Fraud: An Application to Money Laundering

“…Chang and Chang [13] proposed the use of decision trees based on C4.5 to induce rules and use them to validate the identified cluster. Larik & Haider [14] focus their work on the debit and credit information made by clients of a financial institution to identify suspicious transactions.…”

Ontology Based Expert-System for Suspicious Transactions Detection

“…From a historical point of view, the earlier anti-money laundering systems focused only on legislative considerations and compliance requirement which could easily be learned and evaded by money launderers (Gao & Ye, 2007). Recently, several suspicious transaction detection techniques have been developed that are based on machine learning techniques such as dynamic Bayesian Networks (Raza & Haider, 2011) and clustering (Larik & Haider, 2011). Despite the fact that the machine learning techniques help in acquiring hidden knowledge, the biggest challenge in their successful application is the training data requirement.…”

DBSCAN Clustering Algorithm Applied to Identify Suspicious Financial Transactions

“…(2) Using statistical methods to identify the suspicious data beyond predetermined threshold value [7]. (3) Using data mining techniques to detect suspicious financial transactions.…”

“…The data extracted from spreadsheet (excel) usually is not really suspicious data, because anomalous situations don't happen so frequently that most work we do turns out to be a waste of time and labor. Statistical method is suitable for low-dimensional data, but its application in financial data, which is high dimension, is limited [7]. Classification is a supervised learning method [4], which requires the knowledge of classes before.…”