Autoregressive-based outlier algorithm to detect money laundering activities
| Published date | 02 May 2017 |
| Date | 02 May 2017 |
| Pages | 190-202 |
| DOI | https://doi.org/10.1108/JMLC-07-2016-0031 |
| Author | Kannan S.,Somasundaram K. |
| Subject Matter | Accounting & Finance,Financial risk/company failure,Financial compliance/regulation,Financial crime |
Autoregressive-based outlier
algorithm to detect money
laundering activities
Kannan S.
Department of Computer Science and Engineering, Karpagam University,
Coimbatore, Tamil Nadu, India, and
Somasundaram K.
Department of Computer Science and Engineering,
Aarupadai Veedu Institute of Technology, Chennai, India
Abstract
Purpose –Due to the large-size, non-uniform transactions per day, the money laundering detection (MLD) is
a time-consuming and difcult process. The major purpose of the proposed auto-regressive (AR) outlier-based
MLD (AROMLD) is to reduce the time consumption for handling large-sized non-uniform transactions.
Design/methodology/approach –The AR-based outlier design produces consistent asymptotic
distributed results that enhance the demand-forecasting abilities. Besides, the inter-quartile range (IQR)
formulations proposed in this paper support the detailed analysis of time-series data pairs.
Findings –The prediction of high-dimensionality and the difculties in the relationship/difference between
the data pairs makes the time-series mining as a complex task. The presence of domain invariance in
time-series mining initiates the regressive formulation for outlier detection. The deep analysis of time-varying
process and the demand of forecasting combine the AR and the IQR formulations for an effective outlier
detection.
Research limitations/implications –The present research focuses on the detection of an outlier in the
previous nancial transaction, by using the AR model. Prediction of the possibility of an outlier in future
transactions remains a major issue.
Originality/value –The lack of prior segmentation of ML detection suffers from dimensionality. Besides,
the absence of boundary to isolate the normal and suspicious transactions induces the limitations. The lack of
deep analysis and the time consumption are overwhelmed by using the regression formulation.
Keywords Autoregression, Bank transactions, Inter-quartile range, Money laundering detection,
Outlier detection, Regression deviation
Paper type Research paper
1. Introduction
Money laundering (ML) is the nancial transaction scheme that converts illegal money into
legitimate money. Detecting ML activities and tracing the origin of funds in real-time
nancial systems is a difcult process. Hence, nancial institutions utilize the anti-money
laundering (AML) program to detect any suspicious transaction activities. AML system
lters and classies based on suspicion levels and inspects the data for transaction
anomalies. Generally, ML is categorized into three stages: placement, layering and
integration (Bidabad, 2013). In the placement stage, the launderer places his/her illegal
properties in the nancial system. The layering stage includes the following transactions:
nancing in real estate and legitimate businesses, moving deposited cash from one account
to another and reselling monetary instruments. Finally, the integration stage converts the
laundered money into the economy to generate a perception of legitimacy. Huge money
The current issue and full text archive of this journal is available on Emerald Insight at:
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JMLC
20,2
190
Journalof Money Laundering
Control
Vol.20 No. 2, 2017
pp.190-202
©Emerald Publishing Limited
1368-5201
DOI 10.1108/JMLC-07-2016-0031
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