Behavioral Finance

The Battle of the Entrepreneur Sexes

Battle of the Entrepreneurial Sexes

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Are there differences between men and women entrepreneurs? According to Harvard Business Review, there are!

While men and women rated themselves similarly on many dimensions, women were more confident in their ability to efficiently manage operations and in their vision and influence, while men expressed greater confidence in their comfort with uncertainty and finance and financial management.

According to the Global Entrepreneurship Monitor 2014 Women’s Report, women around the world have narrowed the gender gap in entrepreneurship by 6% from 2012 to 2014. They are finding paths to launching more businesses in industrialized and developing nations, according to a new report. On average, more women globally are taking advantage of educational gains and perceived economic opportunities to start businesses that can pave the way for financial independence.

In their United States Study titled, Force Multipliers How Three Fundamental Adaptations Can Help Women Entrepreneurs Scale Big, Ernst & Young found that:

  1. In the US, women start businesses at 1.5 times the rate of men and are at least half-owners of 46% of privately held firms.
  2. In 2015, the number of enterprises with full or partial female ownership was expected to increase by nearly 7% with sales growing to reach $2.967 trillion, representing nearly 18% of projected GDP in 100 countries, as measured by the World Bank.
  3. Yet, only 2% of women-owned businesses in the US break $1 million in revenue
  4. Businesses owned by men are 3.5 times more likely to reach the $1 million threshold.
  5. Women-owned businesses currently employ 7.8 million workers in the US and generate $1.3 trillion in revenue.

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However, in a Global survey conducted by the French bank BNP Paribas and consultancy firm Scorpio Partnership, their findings found that women-run businesses reported average annual sales of $9.1 million, while their male rivals manage about $8.4 million. Additionally, they discovered that female entrepreneurs launch more businesses (4.9) than male entrepreneurs (4.3).

Ernst and Young conclude by suggesting entrepreneurs develop a flexible, adaptive leadership style, highlighting the need to be self-aware and to know when to change focus, and how.

This last observation from Ernst and Young, i.e., entrepreneurs develop a flexible, adaptive leadership style, highlighting the need to be self-aware and to know when to change focus, and how holds the key to understanding the mind and genetics of entrepreneurs, regardless of gender.

DNA Behavior International, through extensive research using their highly-validated Business DNA Natural Behavior Discovery Process, has identified several key personality traits that define entrepreneurs, and detailed in their latest book, “Mastering Your Entrepreneurial Style“. These findings go to the core of individuals knowing themselves and understanding their hard-wired genetics. The Discovery process specifically reveals, in addition to the hard-wired personality traits, the characteristics inherently ingrained into successful entrepreneurs, regardless of gender. This is the “go to” behavior which repeatedly reveals itself under pressure, during all the stages of the entrepreneurial journey.

The natural hard-wired behavior reflects a person’s genetics and the experiences from the first three (3) years of their life. So, we recommend the use of the Business DNA Natural Behavior Discovery Process to measure a person’s entrepreneurial genes.

Regardless of the success of the individual sexes – each demonstrates certain degrees of measurable traits that determine whether they have the entrepreneurial gene.

The five (5) key traits of an entrepreneur are listed below Figure 1. Figure 2 identifies how these traits break down between men and women entrepreneurs.

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Figure 1

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Figure 2

Understanding how to manage and use these entrepreneurial traits determines success or failure in terms of the enterprise, regardless of what that is.

Finally, the National Council of Women in Technology (NCWIT), whose analysts assessed data to discover the differences between men and women entrepreneurs determined, there was almost no difference between men and women company founders

  • Both had an equally strong passion for building wealth.
  • Both started their companies to capitalize on business ideas.
  • Both enjoyed the culture of startups.
  • Both were tired of working for a boss.
  • Both had a long-standing desire to own their own businesses.
  • Their average ages at startup were the same.
  • Men and women were equally likely to have children at home when they started their businesses. (However, men were more likely to be married.)

Do you have what it takes to be an Entrepreneur?

It is more than a desire to control your own destiny, though that’s a major key.
Not every person possesses all the qualities required to be successful in business. Whats important is to understand if you have the entrepreneurial genetics. Completing the DNA Behavior Discovery process is a first step to revealing your personality traits.

To learn more, please speak with one of our DNA Behavior Specialists (LiveChat), email, or visit DNA Behavior

merger aquisition

Mergers and Acquisitions – Putting People before Numbers for Success

According to various research and a Harvard Business Review report, the failure rate for mergers and acquisitions (M&A) sits between 70 percent and 90 percent.

Marc Lore President & CEO, | Founder & CEO, in his LinkedIn article Empowerment after Acquisition makes this observation: Often after one company buys another, you hear leaders talk about how they’re going to empower the executives of the acquired firm, we’re going to leave them alone, we’re not going to mess with their culture, I just want them to keep doing what they’re doing. If you simply tell them to keep doing what you’ve been doing, you take away the exciting part of their careers – the part where they face new challenges and experience the sense of accomplishment that comes with finding inventive solutions.

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Mergers and acquisitions are tough – that’s why so many fail; not because on paper they were not a sound business move, but because little or no attention was paid to the people involved in the M&A.

Case Study 1: Some years ago, a major financial organization relocated its back-end functions to a superb new site 100s of kilometers away. Families were relocated, tempted by significant financial incentives. The rumor mill was in overdrive suggesting this was part one of an inevitable M&A. The relocated staff couldn’t settle, families were splitting, staffers were not performing as well as in the past and the business was suffering.
As an independent consultancy, we were brought in to work with the relocated teams. My first question to senior management was is the business to be sold the response was no. I suggested the CEO speak directly to the staff via video link to assure them.

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A few days after this assuring video link took place, the headlines announced that the business had been sold. Maybe there were sound business reasons for this – but no one was thinking about the impact this breakdown in trust would have with the employees.

For years after this the company floundered. Lessons were learned but at the cost of family breakups, loss of trust and the failure of senior management to put people before numbers.

Case Study 2: When approached by a major airline to facilitate a merger, they explained that they wanted to use an independent third party to be the buffer between the various parties. They determined that this would remove any emotion from the merger and subsequently declared how valuable this approach had been. Further, they requested every team leader, manager, and senior decision maker complete the DNA Behavior Natural Discovery process. They needed to understand the impact such a merger would have on their people. Armed with this knowledge they could make plans that ensured their people migrated successfully and that the business of the airline continued without a hiccup. Not only did this build significant trust it also encouraged the other parties to the merger to complete the same process. All parties now understood their people and could manage the merger more effectively.

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There is nothing more empowering to any business than to demonstrate trust and transparency. People are smart, just set out a vision and point them in the right direction and they will be successful. Sometimes the path they take might be different from what you expected, but that’s the nature of individuals. There is nothing more rewarding than watching a group of talented people all with different personalities come together to resolve a situation.

This kind of releasing and empowerment is made more effective when individual personalities, communication styles and behaviors are known.


  1. Communication: messages need to be relevant to the audience. During periods of uncertainty, people want to know whats going to happen to them. Further, knowing in advance how people wish to be communicated with, ensures messaging is heard and understood.
  2. People react differently under pressure. Knowing this up front makes managing any potential hot spots easier.
  3. Some people thrive on change, this awareness could help leadership identify useful ambassadors to support the messaging.
  4. Leadership that is weak (or who absent themselves from the process) lose credibility very quickly. A leader who projects energy, is honest and clear in keeping the people informed, is far more likely to hold the teams together.
  5. People before numbers as a mantra is more likely to bring success to any complex change such as M & A.

To learn more, please speak with one of our DNA Behavior Specialists (LiveChat), email, or visit DNA Behavior.

We Cant Agree on Anything.

We Can’t Agree On Anything

Nothing is more exasperating than watching a group of smart, qualified, intelligent executives deliberate about a key strategy, and fail to reach an agreement. In frustration, the team turns to the CEO to make the decision. Yet this is counterproductive, as whatever the CEO decides, some of the team will resent – and that resentment leads to a lack of a commitment to delivering an outcome.

It’s even more frustrating when attempting to reach a forward-thinking strategic plan for the business.

How you might ask, can this be so? These people are our leaders. They set the direction of the organization. We rely on them to make sensible decisions that can impact our careers. So, how come they are in disarray?

The CEO, after a few attempts to reach an agreement, called in a DNA Behavior facilitator to oversee the discussions.

These are just a few questions that went through my head as I watched, incredulous, as a significant group of executives began the process of planning for the next stage of the company’s direction.

As I sat to one side and observed their interaction, it was clear the room was heavy with bias, one-upmanship, egotism, and overconfidence pitched against compliance, indifference, and timidity. The assertive ones held their ground. The more vocal got louder. And the reflective and thoughtful seemed to be brooding.

Nothing was being resolved. Every stake put in the ground took the team further away from making decisions.

The DNA Behavior Solution

Each member of the team completed the Communication DNA Discovery Process, an assessment predominantly focused on revealing individual communication styles. Patterns quickly emerged showing the relationship gaps and areas where communication was breaking down, and why.

Independent research shows that Communication DNA leads to solving 87% of business issues, which are hidden as they are communication-related.

Once the team understood how their communication style was getting in the way of bringing their talent and behavioral smarts to the table, outcomes began to change.

As the Goal Setting individuals encouraged input from the Information and Stability individuals and the Lifestyle individuals used their approach to encourage everyone of the importance to reach a solution – suddenly everyone felt they had a voice. And rather than chaos, a solid structure began to take shape.

The team was then able to focus on their task. Egos, bias, and intolerance were replaced with listening, acknowledging input, and intelligent suggestions – a lively, but meaningful debate.


As the task proceeded, the Lifestyle individuals suggested a flow chart to capture ideas. The Information individuals populated the flow chart, carefully catching ideas and suggestions. And the Goal Setters captured the key milestones for taking the organization into the next season and all agreed that it was a job well done.

From my perspective, the lesson learned for them as a strategic planning team of executives was the importance of understanding how to communicate with each other. Without the Communication DNA Discovery Process, this team would have failed to meet its obligations to set out the strategic plan for the next season. Important skills and talents would not have been brought to the table. Individuals would have left frustrated, and the business would have suffered without a cohesive sense of direction.


To learn more, please speak with one of our DNA Behavior Specialists (LiveChat), email, or visit DNA Behavior.

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Financial Advisors under Scrutiny from the RoboPolice

Financial Advisors are already under the most intense scrutiny. Never have their every move, decision, interaction with clients been analyzed to this degree. And it’s going to get worse.

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Photo Cred: The Protection Booth Podcast


Regulatory authorities are now considering the use of artificial intelligence (AI) and machine learning tools to enforce regulatory compliance.


Writing for CNBC Ryan Browne discusses UK regulator use of A.I., machine-learning to enforce financial compliance. Further commenting… Experts believe it could drastically reduce the cost of regulatory compliance, currently estimated to be around $80 billion globally.

And referencing. A report published last October by the University of Hong Kong identified the RegTech industry as a field capable of addressing risk in “real time” and increasing the efficiency of compliance.


Whilst the financial industry worldwide has taken a big hit in terms of its questionable practices, I wonder if AI is a step too far? Financial Advisors, like many businesses, are already bogged down in regulatory red tape. This next level scrutiny could put significant pressure on them to perform.


DNA Behavior has been across these regulatory issues for some time now. They know the importance of having a person complete a process of self-discovery. They understand the significance of revealing natural and instinctive behavior risks, and how they provide the firm assurance, they Know the Client at the deepest level to mitigate compliance risks.


AI is limited, it provides prompts and signposting after the event. The DNA Behavior Discovery process uncovers behavioral biases, risk patterns, decision-making approach and reactionary market movement pressure points in advance. This insight gives financial advisors access to the personality of their clients. It helps advisors use independently validated behavioral data to transform their role to the Wealth Mentor by:

  1. Putting their clients at the center of the financial planning process.
  2. Matching their advisory teams, clients, goals, and solutions.
  3. Using customized communication at all stages of the client lifecycle.
  4. Building tailored portfolios.
  5. Behaviorally managing client emotions.
  6. Enhancing compliance and reducing complaints.


To learn more, please speak with one of our DNA Behavior Specialists (LiveChat), email, or visit DNA Behavior


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Can Big Data Make Risk Tolerance Questionnaires Obsolete?

This week, we remember a seminal event in the history of artificial intelligence. It was the twentieth anniversary of world chess champion Garry Kasparov’s loss to IBMs Deep Blue supercomputer.

I was fortunate to be in attendance when Kasparov gave a keynote speech at the 2017 Envestnet Advisor Summit, where he spoke about his latest book, Deep Thinking. The book is the first detailed account from Kasparov of his battle with the IBM machine as well as his recommendation that we should be prepared to adjust our lives to future advances in technology.

With every new encroachment of machines, the voices of panic and doubt are heard, and they are only getting louder today, Kasparov writes.

While the ability to play chess does not, in itself, make a computer as intelligent as a human, this milestone was just one in a long string of examples where a machine outperformed a human in a specific task. As more and more complex tasks are taken over by software, we should be looking ahead to the next breakthrough and considering the benefits as well as the potential drawbacks.

This started me thinking about how artificial intelligence combined with Big Data could improve wealth management. We have witnessed the automation of almost the entire end-to-end wealth management process, from proposal generation to new account opening, portfolio rebalancing, and performance reporting all the way through to billing.

But one key aspect has stubbornly remained a (mostly) manual process and highly dependent on human input: Risk tolerance questionnaires (RTQs).

The purpose of the RTQ is to create an accurate risk profile of the client, which becomes the primary factor in selecting their investments. Yet, the entire industry still forces clients to take what amounts to a multiple-choice pop quiz, one for which they have not studied but relies on their answers to build a portfolio that they may be invested in for decades.

Replacing RTQs has been on my mind for a while, and I have written about a number of reviews of different risk profiling software. Now seemed to be an excellent time to explore the idea of whether the concept the RTQ could, at some point, go the way of the Dodo.

The Sorry State of Risk Tolerance Questionnaires

My friend, Michael Kitces, wrote an aptly titled blog post, The Sorry State Of Risk Tolerance Questionnaires For Financial Advisors:risk tolerance questionnaire

the reality is that it’s difficult to measure the subjective aspects of risk tolerance itself, simply because it’s the representation of an abstract psychological trait in the first place. In other words, we can’t just objectively look into someone’s brain and figure out what their risk tolerance is. Instead, we have to ask questions, evaluate the responses, and try to figure out how clients feel about their willingness to take risky trade-offs, and how they perceive the risks around them.

Unfortunately, though, many risk tolerance questionnaires (RTQs) don’t actually do a very good job of helping to predict a client’s actual investment behavior during volatile markets, particularly when they ask about how the investor believes he/she would behave in the event of a significant financial loss. In part, this appears to be due to differences from one investor to the next as to what constitutes a risky and undesirable loss in the first place, which can be based on sometimes-arbitrary reference points.

Before we continue, let’s break out the three key components of the risk profile. I’m paraphrasing below from the Kitces article:

  • risk tolerance – the client’s willingness to take on risk
  • risk capacity – the client’s ability to endure a potential financial loss and still be able to achieve his/her goals
  • risk perception – the client’s understanding of how risky they think their investments are

There is a wide variety of risk profiling tools that all take varied approaches to the above components. Some focus primarily on risk tolerance, while others try to capture a combination of all three using different techniques. I’m going to skip all the details of the different techniques for assessing risk. Kitces does an excellent job of this in his article.

What we do know is that most risk tolerance questionnaires suffer from three problems:

  1. They are overly technical in nature;
  2. Response bias, which results in an investor responding differently when stressed;
  3. The difference between risk tolerance and risk capacity is not well-understood by clients or by many advisors, for that matter.

A 2012 study of risk tolerance questionnaires showed that many software products failed to accurately predict client behavior when the clients were under pressure.

What I would like to discuss is the possibility of automating the entire process and cutting the client out of the loop. Is this possible? If so, would it be recommended or would clients accept it? While some advisors and most of the software vendors are against it now, time and technology may soften their views.

To try and gather a range of opinions on this topic, I spoke with representatives from some of the leading providers of risk profiling software:

I was also able to speak with Thomas Oberlechner, Founder at FinPsy LLC, a consultancy specializing in behavioral research.

Can AI + Big Data Replace RTQs?

To restate my original question: Could artificial intelligence combined with big data provide a better understanding of an investor’s risk profile than a questionnaire?risk tolerance questionnaires

I’m thinking that companies like Yodlee, Quovo, etc. could provide access to an enormous quantity of very useful information about a prospective client. It could include the client’s entire investing history plus their personal finance history, including every transaction they ever made, and throw in social media, too. Could an algorithm be written to develop a personality profile for the client that would be a better representation of how they would deal with different market scenarios?

Let me begin the discussion with my favorite vendor response. This is from John Ndege of Pocket Risk:

We will have a manned mission to Mars before an algorithm replaces all risk profiling tools. Even with access to all the client data, it will take years (decades) to gain widespread trust and usage.

In response, a quote from Kasparov:

It’s remarkable how quickly we go from being skeptics to taking a new technology for granted. Given honest data, machines can make us into better decision makers.

How long did it take for us to switch from physical maps to GPS or to dump the Yellow Pages for Google? Not very long at all. Once we realize that a particular technological advance can perform a task faster, more accurately and/or with less effort, most people eventually accept it. Soon it becomes hard to remember what life was like without the technology. Anyone remember what it was like before smartphones were ubiquitous? (See How Risk Tolerance Software Is Disrupting Wealth Management)

Massie from Financial DNA agreed that technology cannot replace the questionnaire, but believes it can provide added value:

The right Big Data optimized with personality insights through an algorithm will provide a big leg up in the power and the ability to apply the behavioral insights across a whole data base of people. However, on its own it will not achieve the level of accuracy if a correctly structured pyschomterically designed questionnaire is used.

Can Humans Be Objective?

My original thought that an algorithm could do a better job at estimating someone’s risk profile was because of the extensive research that shows that humans are unable to answer financial questions objectively. Our innate biases always cloud our judgment.risk tolerance questionnaires

One of the challenges with RTQs is that they are heavily based on the individual’s personality, or perceived willingness to accept investment risk, explained Tolerisk’s Mark Friedenthal. [C]cognitive biases have been fairly well documented to suggest that a person’s recent experience (such as a positive or negative stock market or portfolio experience) will influence how they answer personality questions about taking risks, he noted.

In other words, Friedenthal believes there is no completely objective way to translate someone’s personality into a risk profile just by answering questions. This is why his software includes a client’s cash flows into the process.

As told by Kitces:

The situation is further complicated by the fact that when we take RTQs, we tend to answer the questions calmly and rationally, but when risky events occur, we may respond emotionally (literally using a different part of our brain). Known as the dual self or dual process theory, this disconnect between how we react to risky events in real time, and our (rational) expectation of how we will react, makes it challenging to simply ask consumers (in the hopes of getting a good answer) about their tolerance for taking future risks.

So, why not eliminate people from the process entirely? Why not go full machine?

Understanding The Data

Well, it may not be that easy to correlate the data with human intentions. Even if you have every financial transaction someone ever made, there is still ambiguity to deal with. How do you accurately translate transactions into an investor’s preferences risk tolerance questionnaires

As Aaron Klein of Riskalyze points out:

When you look at a log of trades, holdings or spending activity, are you looking at the client making their own decisions, or were those decisions executed by [an advisor] on behalf of a client? Does the client embrace that decision still, or do they regret it?
That’s the real problem, we see the answers to those questions are not evident in the records of transactions, and thus, it’s really difficult to ascribe risk tolerance to someone based on that data.

Even with the complexities of analyzing the data, there must still be a value to the data that can be mined by the right algorithm. Artificial intelligence has tremendous potential because of all the variables involved in determining risk capacity, Finmetrica’s Nunnally pointed out.

Nunnally continued:

It is very difficult to ask an investor how much risk they can afford to take in a questionnaire because most people don’t know. There are so many variables involved, including time horizon, and countless complexities that relate to future projections like investment returns, income over time, health concerns, etc. that it goes well beyond what the human mind can fully comprehend.

AI is able to crunch numbers and decipher patterns that can lead to investment optimization that far surpasses what humans are capable of.

The passage of time throughout the data set also needs to be taken into consideration. Some decisions to increase or decrease risk are prompted by changes in the expected time horizon of usage, Friedenthal noted.

While it is true that an investor’s decisions when he/she is 25 will be much different from those when they are 55, I think there still should be some way to adjust the results to compensate for this.

Computers Plus Humans = Something Better

Perhaps the answer is not computers alone, but a combination of human intelligence enhanced by machine intelligencerisk tolerance questionnaires

Soon after Deep Blue beat Garry Kasparov, a new type of chess tournament called freestyle became popular. In freestyle events, teams compete using any combination of humans and computers. Kasparov observed that the teams of human plus machine dominated even the strongest computers.

This is echoed by Massie from Financial DNA:

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.

Massie believes that firms could leverage big data to give advisors a head start on knowing the client during the prospect phase. But he warns against building the planning process solely on 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 a strong insight to client emotions. Two clients can have similar Big Data attributes but a very different personality, which means they need to be communicated with differently and offered different solutions to meet different risk profiles and suitability requirements.

(See 3 Ways Personality Testing Crushes Risk Tolerance Questionnaires)

Thomas Oberlechner from FinPsy LLC also insisted that computer learning and expansive databases must be combined with specialized questionnaires in order to be effective at determining an investor’s?underlying behavioral traits and tendencies:

These are two main pathways to determine behavioral characteristics, both of individuals and of companies. You can collect new information by asking directly (for example using surveys) and you can analyze existing expressions or symptoms of past behavior. These two layers of information, explicit and implicit, complement each other. Moreover, possible differences between the two are actually important in understanding risk tendencies and dispositions more fully.

Of course Big Data analysis and AI can help determine behavioral characteristics and preferences. But you need to be able to integrate statistical applications with behavioral science expertise (personality, individual differences, motivation and cognition) to generate the most meaningful insight.

I’m not surprised that firms whose main product or service is built around questionnaires, either behavioral, psychometric or otherwise, are insisting that machines cannot replace them, but must be used in conjunction with them.

Friedenthal offered an alternative option:

Perhaps the better usage of [big] data might be in objectively analyzing savings and spending patterns. Consider that a thoughtful analysis of risk tolerance includes not only a measurement of personality (willingness to accept risk) but also the expected chronology of cash-flows (ability to take risk). Individuals often represent expected cash-flows based on memory and not a data driven account of their actual inflows and outflows. These imperfections may contribute to inaccurate analysis of their ability to take risk, which translates to a sub-optimal risk directive.

If your risk profiling process includes asking the client about their cash flows, then having direct access to their investment and bank accounts would provide you with much more accurate data than the person could provide from memory.

The Future is Now

To demonstrate that this concept is not as far in the future as some people think, Massie noted that his firm has already developed some algorithms for calculating an investor’s risk tolerance and risk capacity based on their financial history. They have even performed some analysis on the accuracy of the results, as Massie reported:

Big Data can predict [risk tolerance] 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 data base optimization using behavioral insights. However, adding a correctly structured RTQ to the mix will lift the accuracy to 80%+

This is exciting news! No word on whether Massie’s firm is planning to deploy these new algorithms or if they are just an experiment. But one thing is clear, if they are released, they will be as a compliment to questionnaires, not a replacement for them.

Massie stated:

Overall, we do not think that RTQs should be eliminated as they reflect the internal view of the investor’s risk tolerance. Big Data (if accurate) will reflect the external view and particularly [the investor's] 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.

Finmetrica’s Nunnally agrees that RTQs should stay:

At this point in the evolution of AI, an algorithm may be able to analyze transactional data to develop a profile relative to an investor’s personal financial history, but it cannot truly understand the emotional and psychological aspects that are uncovered through the course of a conversation (between the advisor and client]. In that regard, risk tolerance assessment is best served through a RTQ that is centered on human interaction.

A New Age of Human and Computer Cooperation

This is the point of the article where I usually include some generic statements about not being able to know what the future holds or if things continue this way we might see a major change, but then again it’s possible that everything will remain the same.

Not this time.

I’m going to predict that we will see some form of Big Data analytics included in risk profiling software becoming mainstream within the next 3-5 years. It will be a blending of questionnaires, data analysis and advisor insight to create a risk profile.

Even though only Financial DNA said they actually had something ready, all of the other vendors said that they were doing research into this area. If Financial DNA’s product starts to take off and becomes a reason that firms switch from competing products, we will quickly see a wave of similar product announcements.

I will leave you with this quote from Michael Kitces, the greatest opportunity of improving risk tolerance questionnaires and overall risk profiling may be the way it helps financial advisors to better manage ongoing client relationships.

All of this software should be focused on helping the advisor to improve the overall client experience. Isn’t that what it is all about?

BD vs QA

Could Big Data Replace Risk Tolerance Questionnaires?

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.

  1. 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
  2. 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.
  3. 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.