Using classification techniques to accelerate client discovery: a case study for wealth management services

Background context. The retail side of the finance industry is currently undergoing a deep transformation associated to the rise of automation technologies. Wealth management services, which are traditionally associated to the retail distribution of financial investments products, are no stranger to this phenomena. Specific knowledge gap the work aims to fill. The retail distribution of financial instruments is currently normalized for regulatory purposes but yet remains costly. Documented examples of the use of automation technologies to improve its performance (outside of the classical example of robo-advisors) remain sparse. Methods used in the study. This work shows how machine learning techniques under the form of classification algorithms can be of use to automate some activities (i.e. client expectations analysis) associated to one of the core steps behind the distribution of financial products, namely client discovery. Key findings. Once calibrated to a proprietary data-set owned by one of the leading french productivity tools providers specialized on the wealth management segment, standard classification algorithms (such as random forests or support vector machines) are able to accurately predict the majority of households financial expectations (ROC either above 80% or 90%) when fed with standard wealth information available in most of the database of financial products distributors. Implications. This study thus shows that classifications tools could be easily embedded in digital journey of distributors to improve the access to financial expertise and accelerate the sales of financial products.