Predicting and explaining Illicit Financial Flows in developing countries: A machine learning approach

Abstract

Cross-border corruption and the illicit movement of financial assets, referred to as illicit financial flows (IFFs), have a strongly deleterious effect on the economies of developing nations. Over the past 20 years, there has been a concerted international effort to mitigate cross-border corruption, however, the most important economic and political factors leading to IFFs are unclear. In this work, I use multiple machine learning (ML) approaches - including linear regression, logistic regression, support vector machines, random forests, and neural networks - to predict the levels of corruption using various economic and political measures from the years 2009 to 2018. Furthermore, to make clear the relative importance of these factors, I use several ML model interpretation tools. Out of the various regression ML Models, the Artificial Neural Network (ANN) had the most success in predicting the IFFs, with a Pearson correlation coefficient of 0.803. The most important features, as quantified using Shapley values, were Aid Percent of Gross National Income (GNI), control of corruption, and population. Taken together, these models and their interpretation provide a method for predicting the IFFs as well as the features that drive them, enabling policy makers to focus on these factors to decrease corruption.