Using tech to apply behavioral finance to real
For a high-level example of how this can be achieved, consider a system where each client of a given firm has completed a financial personality assessment, coupled with an assessment of each investor’s accompanying financial situation.
These assessments can be further supplemented with other
behavioral information, short-term changes to financial
circumstances, and demographics. For example: details of account
activity, such as log-in records, trades, or contacting their
advisor; what happened to a given portfolio last week; and
working and/or family circumstances. This gives a comprehensive
profile of each client.
Because we already know a lot about which actions, messages, or products are most suitable for clients of different sorts, we can build up a similar profile for lists of products that are available to each client, categorizing them according to risk level, liquidity, “smoothness” over the investment journey, complexity, or any one of a dozen other attributes.
We can undertake a similar exercise for client communications: tagging all blogs, videos, articles, and newsletters according to their content, length, complexity, suitability for specific situations, etc.
We can also build an inventory of potential client engagements, or actions, or decisions, scoring each on attributes that can identify which specific aspects of investor personality that intervention will be most appropriate for.
With these simple lists in place our proprietary algorithm is able to rank each product, communication, or intervention according to which is most suitable for each specific client right now, and to continually do this in real time as circumstances change, or as the product or content lists are updated.
Even more powerfully, as the system observes which options are actually chosen or acted on by each client, Artificial Intelligence ensures that the machine learns from each interaction, gradually sharpening and improving the accuracy of its recommendations over time.
Investment is a journey; investment tech needs to come
along for the whole ride
Our research has conclusively demonstrated that we can measure investors’ financial personality with simple but well-constructed questionnaires that are: quick and easy to use; stable and empirically validated; and which add substantial depth to client profiles.
These questions don’t need to be answered at a single point in time, but can be spread over the course of the client relationship. This turns an onerous upfront-focused profiling process into a valuable element of client engagement, enabling an ongoing dialogue which gives the investor valuable feedback on their financial personality, within a system that continually refines and personalizes recommendations and feedback.
Technology can be harnessed not only to make the administration of cursory “risk-profiling” more streamlined. It can enable a truly comprehensive approach to suitability, that recognizes the complexity of each client, and subsequently prescribes advice designed for humans, not robots: hyper-personalized recommendations, prompts, nudges and communication that help investors toward better decisions, based on a rich understanding of who they are.
Over the next few phases of development, using data collected in each phase, this suite of client engagement and scientific suitability tools can be mapped to ever more sophisticated decision-support tools to ensure that all investors get the best investment outcomes for their unique needs and personality, in a manner that’s as always-on as they are.
This is a chapter from the 2021 edition of Technology Traps Wealth Managers Must Avoid. Click here to download your free copy.