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Know Your Clients' behaviours: a cluster analysis of financial transactions

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  • John R. J. Thompson
  • Longlong Feng
  • R. Mark Reesor
  • Chuck Grace

Abstract

In Canada, financial advisors and dealers are required by provincial securities commissions and self-regulatory organizations--charged with direct regulation over investment dealers and mutual fund dealers--to respectively collect and maintain Know Your Client (KYC) information, such as their age or risk tolerance, for investor accounts. With this information, investors, under their advisor's guidance, make decisions on their investments which are presumed to be beneficial to their investment goals. Our unique dataset is provided by a financial investment dealer with over 50,000 accounts for over 23,000 clients. We use a modified behavioural finance recency, frequency, monetary model for engineering features that quantify investor behaviours, and machine learning clustering algorithms to find groups of investors that behave similarly. We show that the KYC information collected does not explain client behaviours, whereas trade and transaction frequency and volume are most informative. We believe the results shown herein encourage financial regulators and advisors to use more advanced metrics to better understand and predict investor behaviours.

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  • John R. J. Thompson & Longlong Feng & R. Mark Reesor & Chuck Grace, 2020. "Know Your Clients' behaviours: a cluster analysis of financial transactions," Papers 2005.03625, arXiv.org, revised May 2020.
    • Handle: RePEc:arx:papers:2005.03625
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    Cited by:

    1. Emilia Herman & Kinga-Emese Zsido, 2023. "The Financial Sustainability of Retail Food SMEs Based on Financial Equilibrium and Financial Performance," Mathematics, MDPI, vol. 11(15), pages 1-26, August.
    2. John R. J. Thompson & Longlong Feng & R. Mark Reesor & Chuck Grace & Adam Metzler, 2021. "Measuring Financial Advice: aligning client elicited and revealed risk," Papers 2105.11892, arXiv.org.
    3. Cynthia Pagliaro & Dhagash Mehta & Han-Tai Shiao & Shaofei Wang & Luwei Xiong, 2021. "Investor Behavior Modeling by Analyzing Financial Advisor Notes: A Machine Learning Perspective," Papers 2107.05592, arXiv.org.
    4. Jerinsh Jeyapaulraj & Dhruv Desai & Peter Chu & Dhagash Mehta & Stefano Pasquali & Philip Sommer, 2022. "Supervised similarity learning for corporate bonds using Random Forest proximities," Papers 2207.04368, arXiv.org, revised Oct 2022.

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