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...

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....