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Many applications from the financial industry successfully leverageclustering algorithms to reveal meaningful patterns among a vast amount ofunstructured financial data. However, these algorithms suffer from a lack ofinterpretability that is required both at a business and regulatory level. Inorder to overcome this issue, we propose a novel two-steps method to explainclusters. A classifier is first trained to predict the clusters labels, thenthe Single Feature Introduction Test (SFIT) method is run on the model toidentify the statistically significant features that characterise each cluster.We describe a real wealth management compliance use-case that highlights thenecessity of such an interpretable clustering method. We illustrate theperformance of our method using simulated data and through an experiment onfinancial ratios of U.S. companies.

Explainable Clustering and Application to Wealth Management Compliance