Detecting money laundering using a pattern matching approach based on Rete Algorithm

Abstract

Money laundering is the criminal practice of filtering funds gotten from illegal activities through a series of transactions so that the funds appear as proceeds from legal activities. Money laundering is a diverse, complex process and basically involves three independent steps namely placement - placing, layering, and integration. It mainly occurs within financial institutions and it is difficult to detect [1]. All financial institutions in Uganda are mandated by the Central Bank to have mechanisms in place for detecting money laundering activities. The Parliament of Uganda passed an Anti-Money Laundering Act Law in 2013 which criminalizes these activities. In addition to the above laws, many institutions have developed policies internally that help them in detecting money laundering activities. Money laundering can have negative effects on the economy of the countries such as loss of government tax revenue, driving up the cost of government due to the need for increased law enforcement to fight the vice, undermining the legitimate private sector and integrity of the financial markets since Money launderers often use front companies which co-mingle the proceeds of illicit with genuine ones. These front companies have access to substantial illicit funds, allowing them to subsidize front company products and services at levels well below market rates. The objective of this project was to develop and validate money laundering detection rules using rete pattern matching algorithm. Through case studies with core banking systems of two local banks and related systems, requirements were established to inform the design of the patterns linked to potential money laundering activities. These patterns informed the design of rules such as the Daily transactions limit and extended the existing rules with a dynamic scoring system where a threshold score combines a net laundering score at run time. A banking system prototype was developed using python and SQLite for the database. The designed rules were implemented using Jess rule engine and integrated into the prototype banking core system. The system integrates dynamic rules making it hard for criminals to deduce the transactional limits. A scoring system has been introduced which evaluates each transaction against all the rules and categorizes it based on the overall score. This reduces on the number of false positives. Jess wrapped in Java was used to develop the improved rule sets and a scoring system that detected suspicious transaction patterns. Several transactions were carried out using the banking applications and suspicious transactions were highlighted by the system.