Explainable clustering and application to wealth management compliance

Abstract

Many applications from the financial industry successfully leverage clustering algorithms to reveal meaningful patterns among a vast amount of unstructured financial data. However, these algorithms suffer from a lack of interpretability that is required both at a business and regulatory level. In order to overcome this issue, we propose a novel two-steps method to explain clusters. A classifier is first trained to predict the clusters labels, then the Single Feature Introduction Test (SFTT) method is run on the model to identify the statistically significant features that characterize each cluster. We describe a real wealth management compliance use-case that highlights the necessity of such an interpretable clustering method. We illustrate the performance of the method using simulated data and through an experiment on financial ratios of U.S. companies.