Threshold fine-tuning of money laundering scenarios through multi-dimensional optimization techniques
| DOI | https://doi.org/10.1108/JMLC-12-2020-0138 |
| Published date | 04 March 2021 |
| Date | 04 March 2021 |
| Pages | 72-78 |
| Author | Abhishek Gupta,Dwijendra Nath Dwivedi,Ashish Jain |
Threshold fine-tuning of money
laundering scenarios through
multi-dimensional optimization
techniques
Abhishek Gupta
Department of Management, Bharathidasan Institute of Management,
Tiruchirappalli, India
Dwijendra Nath Dwivedi
Department of Economics and Finance, UEK, Krakow, Poland and Development,
IGIDR, Mumbai, India, and
Ashish Jain
Department of Finance and Strategy, Indian Institute of Management Lucknow,
Lucknow, India
Abstract
Purpose –Transaction monitoringsystem set up by financial institutions is one of the most used ways to
track money laundering and terroristfinancing activities. While being effective to a large extent, the system
generates very high false positives. With evolvingpatterns of financial transactions, it also needs effective
mechanism for scenario fine-tuning. The purpose of this paper is to highlight quantitative method for
optimizing scenarios in money laundering context.While anomaly detection and unsupervised learning can
identify huge patterns of false negatives, that can reveal new patterns, for existing scenarios, business
generally rely on judgment/data analysis-based threshold finetuning of existing scenario. The objective of
such exercisesis productivity rate enhancement.
Design/methodology/approach –In this paper, the authors propose an approach called linear/non-
linear optimization on threshold finetuning. This traditional operations research technique has been
often used for many optimization problems. Current problem of threshold finetuning for scenario has
two key features that warrant linear optimization. First, scenario-based suspicious transaction
reporting (STR) cases and overall customer level catch rate has a very high overlap, i.e. more than one
scenario captures same customer with different degree of abnormal behavior. This implies that
scenarios can be better coordinated to catch more non-overlapping customers. Second, different
customer segments have differing degree of transaction behavior; hence, segmenting and then reducing
slack (redundant catch of suspect) can result in better productivity rate (defined as productive alerts
divided by total alerts) in a money laundering context.
Findings –Theresults show thatby implementing the optimization technique,the productivity rate can be
improved. This is done through two drivers. First, the team gets to know the bestpossible combination of
threshold across scenarios for maximizing the STR observations better coverage of STR –fine-tuned
thresholds are able to bettercover the suspected transactions as compared to traditionalapproaches. Second,
there is reduction of redundancy/slack margins on thresholds, thereby improving the overall productivity
rate. The experimentsfocused on six scenario combinations, resulted in reductionof 5.4% of alerts and 1.6%
of unique customersfor same number of STR capture.
Originality/value –The authors propose an approach calledlinear/non-linear optimization on threshold
finetuning, as verylittle work is done on optimizing scenarios itself, which is the most widely usedpractice to
monitor enterprise-wideanti-money laundering solutions. This proves that by adding a layer of mathematical
optimization, financialinstitutions can additionally save few million dollars, without compromising on their
JMLC
25,1
72
Journalof Money Laundering
Control
Vol.25 No. 1, 2022
pp. 72-78
© Emerald Publishing Limited
1368-5201
DOI 10.1108/JMLC-12-2020-0138
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1368-5201.htm
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