Laundromats: More Than Just Missing Socks: Improving the estimation of the False Negative Rate of money laundering detection at Dutch banks

The Dutch banking sector is mandated to identify and report transactions that may signify money laundering (ML) activities. Banks have been reliant on rule-based transaction monitoring (TM) systems that flag transactions exceeding predefined thresholds. While such systems are instrumental in filtering potential ML transactions, the inherently...

Fitting Behavioural Artificial Intelligence Technology in the banking sector: A market research study

The banking sector is currently dealing with Anti-Money Laundering (AML) issues, with stricter legislation in Know Your Customer (AML) being the instigator. De Nederlandsche Bank is pursuing fully explainable and transparent models with a risk-based approach. Councyl provides such a model: Behavioural Artificial Intelligence Technology (BAIT)....

Threshold tuning of transaction monitoring models: A risk-based approach to combat money laundering

Money laundering is an increasing problem for the global economy. To combat money laundering, banks use transaction monitoring models with particular thresholds to detect unusual transaction behaviour. However, it is a challenge to determine and evaluate the suitability of a threshold level to ensure that the risk of misclassification of...

Dirty Digital Washing Machines: Identifying money laundering in mixing services

Money laundering has been in existence for a long time, but with recent developments in cryptocurrency technologies, using them as a new method of laundering criminal proceeds has become available to a wider audience of criminals due to mixer services. Bitcoin can be considered pseudonymous, and Mixer services attempt to obfuscate the money...

Detecting BestMixer: An exploratory study on centralized mixing services

Mixing services try to distort cash flow tracking of cryptocurrencies and obfuscate the origin of customers’ earnings by substituting customers’ cryptocurrency funds with the funds of other customers or the mixers’ private assets. This quality makes mixing services interesting for money laundering, and they are therefore often used by criminals....

Locally Explainable Isolation Forest with Mixed-Attribute Data and Ternary Isolation Trees: Combatting Money Laundering with Anomaly Detection

In the fight against money laundering, demand for data-driven Anti-Money Laundering (AML) solutions is growing. Particularly anomaly detection algorithms have proven effective in the detection of suspicious customer behaviour, as well as observing patterns otherwise hidden in customer transaction data. In this thesis, the Isolation Forest...

Countering money laundering: Implications of the 5th Anti-Money Laundering Directive on virtual currency exchanges in the Netherlands

In order to combat financially-economically related crime the government implemented a new directive ensuring that virtual currency exchanges now have to adhere to requirements from legislation countering money laundering. This thesis researches what the extent is of effects that this legislation posed to the daily operations of virtual currency...

Cleaning Up Our Financial System: Combating Money Laundering Using Multiparty Computation

Globally, it is estimated by the UN that 2% - 5% of the annual GDP is lost to money laundering. Cur-rent anti-money laundering efforts are hindered by both the lack of trust between financial institutions internationally and the presence of local privacy regulations like GDPR. This makes it unfeasible to share plain transaction data between...

A systemic design approach to tackle money laundering

The goal of almost all forms of crime is monetary gain. In serious forms of crime, like drug crime or human trafficking, enormous amounts of money are made. For criminals, this money is not directly usable. Spending it right away in the economy would be too obvious and attract attention from the authorities. Therefore, the dirty money gained by...

Planning for Money Laundering Investigations

According to the United Nations, the amount of money laundered worldwide each year is an estimated 2 - 5% of global GDP (equivalent to $800 billion to $2 trillion in US dollars). This is money that criminal enterprises rely on to oper- ate. For that reason, the European Union demands that gatekeepers (banks and other obliged entities) apply...

Local Explanation Methods for Isolation Forest: Explainable Outlier Detection in Anti-Money Laundering

Machine learning methods like outlier detection are becoming increasingly more popular as tools in the fight against money laundering. In this thesis, we analyse the Isolation Forest outlier detection algorithm in detail and introduce a new local explanation method for Isolation Forest, the MI-Local-DIFFI (Multiple Indicator Local-DIFFI) method....