Know Your Clients' behaviours: a cluster analysis of financial transactions
This work addresses the problem for financial regulators and advisors by suggesting improved metrics for understanding investor behaviors, though it is incremental as it applies existing methods to new data.
The study analyzed financial transaction data from over 50,000 accounts to group investors by behavior using clustering algorithms, finding that traditional Know Your Client information does not explain behaviors, while trade frequency and volume are more informative.
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.