The holy grail of behavioral insight is aligning and blending demographic and financial related big data (the external view of the client) and psychometrically measured personality traits (the internal view of the client), and then deploying tools to utilize the blended information to guide the planning process. Our view is that by using Big Data a firm can get a quick leg up in knowing the client to start the prospecting phase. However, from a behavioral perspective, the whole planning process should not be exclusively built on Big Data.
The advisor/firm will never know the complete picture until they have the client complete a psychometrically designed assessment, which if structured correctly, will provide strong insights into client emotions and decision-making under pressure. Two clients can have similar Big Data attributes but very different personalities. On the surface, their activities may mimic one another, but in reality, they’ll need to be communicated with differently and offered solutions from a different perspective to address differing risk profiles and suitability requirements.
A key point in approaching the application of Big Data to Risk is to recognize there are ranges of distinct and separately measured elements which make up a person’s risk profile. There are 3 primary elements, with sub-elements.
- The Risk Need – the risk required to achieve goals; This is really the domain of financial planning software, calculators etc not Big Data or RTQs/DNA
- The Financial Capacity – the financial ability to endure the risks of portfolio losses; If enough of the right Big Data attributes are accurately gathered then the Financial Capacity can be calculated that way to a reasonable level. However, there can be flaws because people use entities, separate accounts and many other factors that may hide their financial position. But, nevertheless, there is good external data to build the financial capacity story.
- The Risk Tolerance (and Risk Propensity, Loss Aversion) – the emotional ability to take risks and live with losses. This is personality driven. The Big Data can predict this to around a 30% level if the right Big Data attributes are used, although this can be increased to up to 60% if there is some database optimization using behavioral insights. However, adding a correctly structured RTQ to the mix will lift the accuracy to 80%+, and Financial DNA will lift accuracy to 91%+
Overall, we do not think that RTQs should be eliminated as they reflect the internal view of the investor’s risk tolerance. Although, as we know, there is a wide gap in the quality of RTQs. And the less robust ones may not move the needle much in improving the quality of behavioral insights. Whereas, the Big Data (if accurate) will reflect the external view and particularly their financial capacity. So, the RTQ and Big Data are designed to measure different risk profile elements. If both are used in tandem it would be a lot more helpful than if only one or the other were done.
Also, from our perspective, there is more at stake than just the risk profile in using Big Data with personality insights, there is advisor-client communication, financial management behaviors, identifying rogue advisors due to financial pressure, and many more elements. These are all areas in which we’ve developed algorithms for and more.
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