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Using Classification Techniques to Accelerate Client Discovery: A Case Study for Wealth Management Services

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Proceedings of the Future Technologies Conference (FTC) 2023, Volume 3 (FTC 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 815))

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Abstract

The retail side of the finance industry is currently experiencing a deep transformation associated to the rise of automation technologies. Wealth management services, which are traditionally associated to the retail distribution of financial investment products, are of course impacted. The retail distribution of financial instruments is currently normalized for regulatory purposes. It yet remains costly. Documented examples of the use of automation technologies to improve its performance (outside of the classical example of robo-advisors) remain however sparse. 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. Once calibrated to a proprietary data-set owned by one of the leading French independent software vendors specialized in 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 (R.O.C either above 80% or 90%) when fed with standard wealth information available in most of the database of financial products distributors. This study thus shows that classifications tools could be easily embedded in the digital journey of distributors to improve the access to financial expertise and accelerate the sales of financial products.

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Notes

  1. 1.

    Collection can even be fully delegated to a client through a digital medium (e.g. an online “K.Y.C” portal (Know Your Client)).

  2. 2.

    This is consistent with the documented fact that, for now, wealth management services are mainly open to households with more than 500k$ of assets [15] in most mature countries.

  3. 3.

    Manymore produces applications for financial product distributors (e.g. wealth managers). The firm currently has the second largest market share in France (see https://www.manymore.fr/.

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Author information

Authors and Affiliations

  1. Researcher at CERNA, Mines Paristech, Paris, France

    Edouard Augustin Ribes

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  1. Edouard Augustin Ribes
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Correspondence to Edouard Augustin Ribes .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

Appendix

Appendix

Table 4. Households objectives - correlation matrix.
Full size table
Table 5. Machine learning algorithms calibration results with down sampling.
Full size table
Table 6. Machine learning algorithms calibration results with up-sampling.
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Table 7. Machine learning algorithms calibration results with ROSE sampling.
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Table 8. Financial objective - proposed standard framework.
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Ribes, E.A. (2023). Using Classification Techniques to Accelerate Client Discovery: A Case Study for Wealth Management Services. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2023, Volume 3. FTC 2023. Lecture Notes in Networks and Systems, vol 815. Springer, Cham. https://doi.org/10.1007/978-3-031-47457-6_15

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