The application of machine learning in the classification and classification of securities and futures customers

DOI

10.2991/978-94-6463-098-5_37How to use a DOI?

Keywords

Target customer locating; Customer classification; Customer ranking; K-means clustering; Gradient Boosting Algorithm

Abstract

How to identify high-value customers among the massive customer base and achieve precise marketing and service is the current challenge facing securities and futures companies. The traditional method of dividing customer groups according to the amount of assets is more based on experience and not accurate enough. The goal of the research is to explore if machine learning algorithms can solve the above problem. In this study, a K-means clustering model is built to categorize individual customers into different groups based on their behavior. The Elbow method and Gap Statistics are used to determine 7 as the best number of clusters, and the corresponding K-means model is able to group customers in a more accurate way with regard to client total contribution to the firm’s revenue. Later, a gradient boost algorithm on a decision tree is developed to quantitatively score customers based on a weighted average of various dimensions. The 2 most important dimensions are net retained transaction fees and assets according to the model. These 2 models can help improve the accuracy of locating key customers compared to traditional methods.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Daniel Lu
PY  - 2022
DA  - 2022/12/27
TI  - The application of machine learning in the classification and classification of securities and futures customers
BT  - Proceedings of the 2022 4th International Conference on Economic Management and Cultural Industry (ICEMCI 2022)
PB  - Atlantis Press
SP  - 310
EP  - 323
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-098-5_37
DO  - 10.2991/978-94-6463-098-5_37
ID  - Lu2022
ER  -