Explainable Clustering and Application to Wealth Management Compliance

TL;DR

This paper introduces a two-step explainable clustering method for financial data, enhancing interpretability crucial for compliance and regulatory needs in wealth management.

Contribution

The paper proposes a novel approach combining classification and statistical testing to interpret clusters, addressing the interpretability gap in traditional clustering algorithms.

Findings

Effective in simulated financial data

Identifies significant features for each cluster

Applicable to real-world wealth management data

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 (SFIT) method is run on the model to identify the statistically significant features that characterise 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 our method using simulated data and through an experiment on financial ratios of U.S. companies.