Predicting Basel Anti-Money Laundering Sanctions using Machine Learning

Mark Lokanan was awarded funding through an Internal Grant for Research to use multiple machine learning algorithms to aid anti-money laundering operations against a country.

The objective of this paper is to use machine learning (ML) algorithms to predict the probability of Basel money laundering sanctions. The goals are to: (1) predict the likelihood of anti-money laundering (AML) sanctions using risk indicators from Basel and the World Bank and (2) find the best predictors for predicting the likelihood of sanctions. Multiple machine learning algorithms will be used to identify the most robust risk predictors, resulting in Basel sanctions against a country. Data for this project will come from the Basel Institute of Governance and the World Bank portal.