A NEW DESIGN OF A DETECTING ANTI-MONEY LAUNDERING USING TIME FREQUENCY AND TRANSACTION

Abstract

Money laundering is the crucial mechanism utilized by criminals to inject proceeds of crime into
the financial system. The primary responsibility of the detection of suspicious activity related to
money laundering is with the financial institutions. Most of the current systems in these
institutions are rule-based and ineffective (over 90 % false positives). The available data sciencebased
anti-money laundering (AML) models to replace the existing rule-based systems work on
customer relationship management (CRM) features and time characteristics of transaction
behaviour. Due to thousands of possible account features, customer features, and their
combinations, it is challenging to perform feature engineering to achieve reasonable accuracy.
Aiming to improve the detection performance of suspicious transaction monitoring systems for
AML systems, in this article, we introduce a novel feature set based on time-frequency analysis,
that uses 2-D representations of financial transactions. Random forest is utilized as a machine
learning method, and simulated annealing is adopted for hyperparameter tuning. The designed
algorithm is tested on real banking data, proving the results' efficacy in practically relevant
environments. It is shown that the time-frequency characteristics are discriminatory features for
suspicious and non-suspicious entities. Therefore, these features substantially improve the area
under curve results (over 1%) of the existing data science-based transaction monitoring systems.
Using time-frequency features alone, a false positive rate of 14.9% has been achieved, with an Fscore
of 59.05%. When combined with transaction and CRM features, the false positive rate is
11.85%, and the F-Score is improved to 74.06%.