A survey of anomaly detection techniques in financial domain

Mohiuddin Ahmed, Abdun Naser Mahmooda, MD Rafiqul Islam

Research output: Contribution to journalArticlepeer-review

157 Citations (Scopus)

Abstract

Anomaly detection is an important data analysis task. It is used to identify interesting and emerging patterns, trends and anomalies from data. Anomaly detection is an important tool to detect abnormalities in many different domains including financial fraud detection, computer network intrusion, human behavioural analysis, gene expression analysis and many more. Recently, in the financial sector, there has been renewed interest in research on detection of fraudulent activities. There has been a lot of work in the area of clustering based unsupervised anomaly detection in the financial domain. This paper presents an in-depth survey of various clustering based anomaly detection technique and compares them from different perspectives. In addition, we discuss the lack of real world data and how synthetic data has been used to validate current detection techniques.
Original languageEnglish
Pages (from-to)278-288
Number of pages11
JournalFuture Generation Computer Systems
Volume55
Early online date2015
DOIs
Publication statusPublished - Feb 2016

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