| dc.description.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. | en_US |
