Transcript

Matt Heine (MH) (01:13):

Welcome to this episode of Between Meetings. Today we're in for a bit of a special treat. We are joined by Shaha from the Snowfields of California, I believe, and Bernard is dialling in from New Zealand. Welcome to you both. Thanks so much, Matt. It's been a little while since we last caught up. Shaha you probably don't remember, but back in 2016 I was actually lucky enough to join one of your lectures in Berkeley in your hometown, which is really where this conversation around risk profiling and some of the research that you are doing kicked off. So really looking forward to chatting to you both today, hearing how things have advanced. But before we start, would be really good just to understand your journey for both of you, if that's all right. And understand why you've ended up at Berkeley and why you saw Berkeley as the natural place to conduct your research.

Shachar Kariv (SK) (01:55):

So I got my PhD 20 years ago. Time flies I guess when you are having fun and surprise, surprise. I actually had some choice between Berkeley and other universities with Berkeley folks we call Princeton Prison and Yale we call jail. So I ended up at the University of California Berkeley. It's appealing to me that this is the best public university in the world. I could keep my job. So I got tenure. I was the chair of the department actually twice, and Berkeley is my home and don't tell it to my dean. She always need to think that she needs to retain me. But the economics department at Berkeley and Berkeley in general, we have always been in the cutting edge of research. We argue that Berkeley is the mecca of behavioural economics and what we call risk profiling is part of behavioural economics.

MH (02:53):

So I think Berkeley has approached things quite differently. And I may have got this wrong, but at the time I recall that you'd actually merged the social sciences department with the economics department, which has allowed you to actually do a lot of the research that you are now doing. Is that correct?

SK (03:06):

The economics department is in social sciences, but just to show you how innovative Berkeley is now, there are discussions to actually move statistics from mathematical sciences, move computer science from engineering and move economics from the social sciences and putting us all together, which is actually very, very relevant to our discussion today because understanding clients, understanding people would think about it. This is something that has to do only with psychology. Actually, no, it has to do a lot with economic statistics and computer science. And I think a lot of my research, I am an economist, is actually in the intersection of this field and this is the research that informs what we're doing in capital preferences.

MH (03:58):

Bernard, you're obviously based in New Zealand at the moment. How did you and Shahar end up meeting or getting together all the way across the world? Yeah,

Bernard Del Ray (BDR) (04:06):

It's a fascinating story. Shahar is not an easy man to get a first date with, but I was introduced by a professor at Northwestern University who in my background being the head of strategy of a private client group for a major bank and a CMO for a large asset manager in the us I was curious how we would identify opportunistic clients, how would we find someone who has the right profile, so to speak, to be interested in certain types of investments. And I found my way through to a professor in Northwestern and then he referred me to Shahar and I tried to get ahold of him. We ultimately made it happen through this intermediary. And I met him actually at the University of New South Wales where he was doing a lecture and we spent some time together and then he invited me back to Berkeley and I became maybe his first industry student because s shahar had not really left I would say the golden gauge of academia. And I was introducing him to this world of financial services where I thought his work could be so relevant. But since then we've become both best friends and obviously business partners. So it's been a great journey.

MH (05:15):

So you literally had to chase him around the world for a meeting? I

BDR (05:17):

Did. He did buy me coffee on that first date. So it was a good one. As any good Australian coffee should be.

MH (05:24):

And Shahar, you obviously had a choice of lots of different things that you could start researching and thinking about. What was it that attracted you to this particular part of the industry and what you're doing now? It's quite niche even though it's part of our industry and that fundamental part,

SK (05:39):

This is really a great question and as Bernard said, I love the golden cage of academia and I was able, until this guy tracked me down, I was able to resist the temptation of going out of academia and outside of Berkeley, economics is about improving people's wellbeing. Many people think that economics is about setting the interest rates. No, I'm sorry, macro economist. It's not. You hope that by setting the interest rates right, you are actually going to improve the wellbeing of many people. Your wellbeing depends on many, many things. It depends on your physical wellbeing, it depends on your emotional wellbeing, but it also depends on your financial wellbeing. Now there are a lot of debates in the social sciences in academia, how do you actually measure wellbeing? But I think that there is one thing that there is consensus about financial wellbeing becoming a larger and larger part of overall wellbeing.

(06:45):

And the reason why is actually simple because many problems can be solved by throwing money at them. Suppose that you were the richest person on earth 200 years ago and you had a problem with your tooth money could not buy dental care because there wasn't dental care they just told you hold something tight and they pull the tooth out. Today so many things are actually solved by having enough money. Now what is enough and improving people's financial wellbeing at all ages and across all societies is tremendously important. And I think we are the business for improving people's wellbeing. I actually argue that for me, I'm healthy. So for me, my financial advisor is actually more important for my wellbeing than my physician. So I'm always saying to people in the industry when I meet them, I'm holding you guys at the same level that I'm holding providers of medical care. This is that important.

MH (07:52):

And that probably comes down to I guess the root of your research, which is whilst certainly money can't necessarily make you happy, it can help you live a fulfilled life. But if you're operating within the risk frameworks that you are comfortable with, otherwise it can actually no doubt add significant stress to your life.

SK (08:07):

Absolutely, no doubt.

BDR (08:09):

I think we're living in an age where the industry has spent so much time perfecting investment solutions and optimising them and adding new sense of options but less time really on the science of understanding the client. I think that's where a lot of the future client experience benefits for advisors and for clients lie is in this ability to look at that wide range of investment options and say, given who you are, given how you think about risk given where your social values are given, how you prioritise your goals, given how we understand your time preferences and consumption preferences, this is the right fit for you. Science should simplify the client experience. It should make it more powerful, not complicated. And I think that's what we're really excited about is that we think we're at that inflexion point. Now I'm also just like shahar, although not the scientific background, but just from what science can mean for the industry, I think I'm super excited

MH (09:10):

About, we've certainly had this conversation a number of times over the years. Shahar I don't think we have, but I've always personally had a fundamental issue with the way that risk profiling has been done in the industry. And if I pick one or two of the questions that I think are completely misleading is if you ask someone what they would do if their portfolio fell by 20% and they've never experienced their portfolio falling by 20%, in my view, they're unqualified to answer that. What was it that you found in your research or what was your original hypothesis that led you to go down the track that you have, which I guess in your mind at least gives you a much better outcome than the standard set of questions that we've always dealt with.

SK (09:44):

One of the most interesting things about economics is actually asking the right questions because if you don't ask questions that are not well defined, then you cannot answer them. And a large part of answering a question is actually posing the question in the correct way. I wanted to study math and physics and I actually found myself by pure coincidence in a game theory course and I said to myself, wow, this is wonderful. I never thought that you can actually talk about human being using mathematical terms. Just to put everything in perspective. If you ask a physician, what do you need to know about a patient in order to say, I feel comfortable that I know that the patient give me the physician, gender, age, medical history, et cetera, all physicians will know exactly what they need. Why? Because medicine is a hard science. There are microscopes, there are blood tests, there are MRIs.

(10:46):

Economics is not a hard science. We don't have hardware, we only have software. And the software that we are using are the software in order to understand clients that are making tradeoff. The tradeoff between risk and return is one of these tradeoffs, but there is also other tradeoffs, the tradeoffs between today and tomorrow, my welfare today versus my welfare tomorrow, should I buy this cow or should I save more for my retirement? But also should I eat the cheesecake or eat the salad just before dinner or should I go to the gym or should I sit in front of the TV and watch another reality show? That's another trade-off. So I think to your point, the industry is actually doing two things wrong. One, we are only focusing on a slice of what we need to know about the client. It's a very important slice, but it's not the only slice, it's the slice about risk attitudes.

(11:45):

And secondly, we very poorly measuring it. We're measuring it using surveys or questionnaires. And as you pointed out, the problem with these questionnaires is that they require enormous amount of imagination. I never been in a downturn, how would I know? So we need to find something else to do. And I think that for me, this journey, Bernard is always saying that he will make me blush. We have maybe a competition who will make who blush at the beginning, my research informed capital preferences. Now the question that we are actually facing and trying to solve for the industry informed my research. It's became a two-way street. There is no better research and research motivated from important real world questions.

BDR (12:34):

And just to add there, Shahar was way ahead of his time. And when I saw his work, what really spoke to me was for those of you that haven't seen capital preferences work, it's a interactive stimulating activity that gets your hands dirty both as the client. So it really puts you in the middle of the core trade-offs that an advisor would really want to know how you solve in order to advise you. Or if you're in a self-directed environment, you'd want to go through to stress test your own preferences. That aspect of actions speak louder than words and getting your hands dirty I think is just for me, what was super, super appealing when I met Shahar, I just thought, wow, this is going to change the client advisor dynamic. It's going to change the level of engagement of the end investor itself by having them understand kind of what's behind the advice they're going to be given and the kind of trade-offs that they're going to be exposed to during that journey. So that's just my perspective on that.

 

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MH (13:34):

When using the capital preferences tool, I guess the power is really in its simplicity. So perhaps for those that haven't seen the tool, John will attempt to describe it as best you can.

BDR (13:43):

I'll take the first shot Shahar, but first is open your mind to the multiple dimensions of things you as an advisor or someone in the industry would want to know about a client. And just let's go through those quickly. You have how does this client make the trade off between risk versus reward? How do they make trade-offs between consumption today and consumption tomorrow? How do they make tradeoffs between themselves and the environment or social justice or values based elements? How do they prioritise their goals? These are all things that our conversation can't uncover precisely. So instead, we're going to put you in a wind tunnel and interactive gain where let's say you have a million dollars to invest. I'm going to say, okay, in the following scenarios, show me how much of that million dollars you would put at risk in each of these scenarios given a varying set of opportunities or positive gains that are available. And the client moves the slider and then they're exposed to a few different scenarios that allow us to stress test. It takes the client normally about 60 to 90 seconds to complete.

BDR (14:50):

And in that 60 to 90 seconds, let's just say in the domain of risk we're recovering, what's their willingness to take risk? How sensitive are they to losses? So different from taking risk, what were the reactions likely be when the markets are volatile or they have downside experiences? And I guess very, very important what and how consistent is this decision maker or this client in this area of risk taking at all? Are they even advisable? And then from that we're able to use that very, very short client interaction to be able to look at the available investment options and say, given what we've just learned about you and your spouse or partner or if that exists, this is what would be the best fit for you as a household. Now that could be a portfolio, it could be a portfolio and an annuitization strategy. And then we can look at that compared to the client's goals.

MH (16:16):

So Bernard, I might just localise the conversation for a moment. And if we think about the Australian framework, you go through a risk profile, you answer five questions, which I think we agree could be potentially meaningless and it puts you into a nice bucket. You're either a conservative investor or a high growth investor. What's the process with capital preferences? Is there a similar outcome at the end or are you allocating to asset classes? How does it work?

BDR (16:36):

Yeah, so for any particular advice firm that has their own efficient frontier of model portfolios, that's a really important part of their value proposition. So the client will go through the process, we'll recover their decisions, and then we will be able to look across that efficient frontier that they've designed and that they have assumptions around and say, given the client's decisions, this client is a 87% fit for the moderately aggressive portfolio, but their comfort zone might sit around that and each portfolio in the investment lineup would be ranked by its fit with the client's preferences. So it just leads to usually a richer conversation. And then when we layer in the goal component of our solution, that tension between the client's comfort zone being let's say an 80% fit and the goal achievability being maybe only a 70% fit at that particular point in the portfolio lineup that the advisor gets to have that conversation about, okay, well we can move you out of your comfort zone potentially, or you could save more in order to achieve the higher rate of goal. And so I think we definitely take that kind of construct to the next level by introducing what we call this fit concept or comfort

MH (17:53):

Zone. Shahar, perhaps a question for you. You've talked about comfort zone. I'd love to know through your research, what are the key drivers of decision making as it relates to the risk profiling? Is it education, age? What have you found to be the most powerful indicators

SK (18:09):

You can always find correlates between preferences and between socio demographics? Let me just give you one that is actually very interesting. There is a difference that by the way, in any standard questionnaire you can never disentangle. And that's the difference between risk aversion and loss aversion and ambiguity aversion. Lemme just illustrate one risk aversion and loss aversion suppose meant that you had a hundred dollars in your pocket and you walk in the street and you lost this a hundred dollars. Think to yourself how it'll make you unhappy, right? You just lost a hundred dollars.

MH (18:47):

Absolutely.

SK (18:48):

Think about how unhappy it'll make you now think that you actually found a hundred dollars on the floor and find their people that will make you happy, right? But think in absolute value, in absolute value, finding a hundred dollars in absolute value will make you more happy than the loss of a hundred. And the answer is not We're much more sensitive to losses loss aversion in additional to risk aversion. It's the additional fear anxiety that we feel when markets are down when my portfolio is losing. So for example, older people are not more risk averse than younger people. They're actually more loss averse. However you can think about it, that they should be why they should be because if you make a bad decision, a bad investment decisions, when you are young, it's usually a small amount of money and you have time to recover If you make a bad investment decision when you are old, it's a larger amount of money and it's harder to recover.

(19:54):

So I think that the key in what we do is precision. And when I say precision, I mean statistical precision because everyone comes with confidence intervals. So we are actually measuring with precision. How do you feel about the tradeoff between expected return and volatility as a result? We can put you directly on a point on the efficient frontier on a continuum later on. If you have five model portfolios and we need to put you in a bucket, of course we can do it because we have richer information underneath. Moreover, it might be that I'll put both you and Bernard into the same bucket because that's the best portfolio, but I would know that it's a better fit for you than a better fit for Bernard. So I will take more care of Bernard. In addition, I will say Bernard is more loss because he's older, so I need to take care of him differently. And I think that the key here is suppose that you went to your physician and the physician told you your cholesterol is medium. You are asking what do you mean by medium? What's the range of medium? So we put everything in buckets because we need to organise the world for ourselves, but underneath, if there is nothing that is coming from a continuum, it basically means you don't have enough information through

MH (21:30):

Your research. Was there anything that surprised you around loss aversion that led to either greater or lower loss aversion that you wouldn't have expected initially?

SK (21:37):

Yes, absolutely. You would actually think to yourself that loss aversion might be concentrated in individual, not wealthy individual because wealthy individual can lose a lot and they're not going to be homeless, they're not going to starve. What surprised me in not only my research, lemme make clear, I'm standing on very high shoulders. This is the greatest mind in social sciences and there are other people that are involved. Our head of research was my undergraduate students at Berkeley and then was a PhD student at Princeton. So there is a young generation, there are people before me and there are people after me. What I was mostly surprised is actually how widespread loss aversion is and we need to take it into account. And we haven't even started speaking about ambiguity aversion.

MH (22:31):

I wouldn't mind if we can spending a little bit of time on that. I think most of us understand the other types of aversion, but ambiguity aversion is not one that I've come across.

SK (22:39):

We distinguish between risk aversion and ambiguity aversion. Risk aversion is where there is uncertainty, but the probabilities of different outcomes are known. Like for example, when you go to Las Vegas and you play the roulette, there are precise probabilities to exactly, maybe you cannot calculate them. But if I now flip an even coin, it's 50 50 most situations in the world, they don't only involve risk, they also involve what we call ambiguity, which you can think about. It is an additional degree of risk. It's the fact that you actually dunno, don't have precise probabilities in your mind. If I tell you what's the probability that the temperatures tomorrow in Barcelona are going to be above 25 degrees, there is no precise probability to this many people. They're actually not adverse to risk. They are much more to ambiguity. Think about it in investments, people are actually sometimes saying that stocks in emerging markets are riskier than stocks in non emerging markets. No, no, no, no, no. They're not riskier. They're more ambiguous because there is less information about these markets. So we have an entire research agenda about also profiling people in terms of their ambiguity version, which is something that the industry doesn't do at all.

MH (24:07):

And ambiguity aversion based on the description, which is very clear, can be solved to some extent through education and therefore a key aspect of what an advisor can and should do with their client base.

SK (24:18):

Absolutely. I ask my advisor all the time because he knows the market better than I do, give me information, reduce my ambiguity. This is a very important way to move people to their comfort zone.

MH (24:31):

That's fascinating. And Bernard, in your travels, what are some of the trends that you've seen?

BDR (24:35):

What I've noticed more is just how much more complex the advice is becoming where advisors have to integrate multiple preferences for clients across multiple domains. So clients are walking in now and saying, I want portfolios that match my values and I want it to be within my comfort zone and I wanted to achieve my goals with a certain probability, or that's what they define as a good outcome for themselves. Or I want my retirement income to have this level of certainty. It's becoming a really interesting and challenging problem for the next generation of advisors to solve. And for me it's super exciting because I do think science has some of those answers in making it clearer that the value proposition that we all make within financial services, that the solution that I'm about to give you is fit for you. Putting some real metrics around that, putting real clarity around that so that there's lots of transparency and lots of trust that comes with it. So just to continually impressed with how quickly that personalization journey is evolving around the world

MH (25:46):

And maybe an extension of the ambiguity aversion and your comment just around the values might just talk ESG for a moment. And I know that it's a dirty word in some states of America now, but that difference between intention and action is something that I know a lot of our clients are struggling with and certainly the industry where the intention is, right? And you might have a younger client that wants ESG and we'll maybe avoid the definition or defining it right now, but they're looking for ethical investments for argument's sake. But when it actually comes down to it, the intentional the action are actually quite different. And we saw this certainly in the early days where industry funds were surveying members and it was 99% of members wanted ESG ethical options and when it actually came to picking it or investing the actual takeup was sort of 0.1 of a percent. Just love any comments on that.

BDR (26:31):

It's very important what you're talking about here is that there's multiple dimensions to a person's preferences when they are exploring how to better align their portfolios to their values of those dimensions is what do they actually care most about? And understanding the utility they might derive from seeing that type of better alignment. But there is another dimension, and Shahar has some amazing papers on this that have been published in the top journals and in the world, which is the dimension of just how altruistic are you and what types of trade-offs are you willing to make to achieve alignment. And I think it's entirely fair, and I don't want to contradict research that says good sustainable and ESG outcomes are associated with better returns, but there are some other transaction costs that do emerge when you move your portfolio around. It could be taxation in the us it could be other types of diversification considerations and so you would want as an advisor to explore those as well.

(27:36):

And we see from our research, and we've got doing a lot in this space now that clients have varying levels of altruism and they think very differently about the level of friction they would be willing to absorb in order to achieve better values alignment. So I think the right place for advisors is to explore that. It's a place where clients can be somewhat uncertain themselves. And so again, through some of these gamified interactive experiences, it allows us to recover that parameter and say, okay, I understand where your values lie, but I also understand that when we think about some of the considerations of making these portfolio changes that we have to be balanced in our approach. Maybe we can drop it into the podcast notes or whatever. We've done some really interesting work globally on ESG preferences and altruism preferences around the world in different geographies including Australia. So you could look at how various geographies compare as well.

SK (28:34):

Lemme add to it when we are talking about the client's risk attitudes, at the end of the day it's something simple. It's how you trade off expected return and volatility. When we are talking about something so abstract as values, this is something multidimensional much, much, much more complicated. I might care about education, I might care about the environment, I might care about other things because it's so multidimensional as said, even I don't know it. So when a financial advisor is coming and giving me some ESG portfolio, it's like it tells me shahar, you need to eat vegetables, here is vegetables, but I like tomatoes but I don't like cucumbers or I want this type of a salad and not that type of a salad. If you have a restaurant, you cannot have on the menu vegetables, no one will buy vegetables. You actually need to be more precise and you need to define the dish such that if you give me the right dish with I will eat healthy and I will eat vegetables. But you need to be more specific and in order to be more specific, you really need to understand what we call in behavioural economics, social preferences. And these social preferences on the dimension of how many dimensions are involved are more complicated than risk preferences. If we're unable to understand the client, you'll not be able to actually get them to do the right thing, not only for them but also for society. I think entirely that this is the right way to go, but we need to make it all work together.

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MH (30:18):

I'm conscious that we're seeking to unpack a lot in a short period of time, but I want to make sure that we do leave enough time to discuss the next topic, which is advising the modern couple. I think we've spent time trying to understand an individual, but when you multiply that individual by two very different personalities, different experiences, different risk profiles, it starts to become quite complex. So again, love to hear your thoughts on how you're tackling this

SK (30:41):

Combining preferences of more than one person. It might be a couple or it might be a family. It can be it's a very, very complicated problem, very complicated problem and it's a great research problem. And this is what I mean by actually things that are coming from the industry are informing the research. And we actually came with something that I thought is fantastic because the entire process is based that I actually profile you and I profile your spouse separately. So no influence, no independence, everything is independent. And then under the hood we have a mathematical model to basically merge this two together and find a compromise. It'll be a compromise because if you love risk like Austin Powers, you like to live dangerously and your spouse, well you need to compromise and it'll be a compromise. But what we would find is the kind of soft spot which will be the best compromise and we can find it mathematically. Then of course we need an advisor that really, really understand what we have done and also have the human interface to interact with the capital and explain the solution.

MH (32:09):

So presumably rather than just finding the solution, you then need to highlight where the differences are and the conversations happens in the gap.

BDR (32:16):

That's exactly right. I mean I think the most powerful moment for a couple's trust factor with the advisor is when they both feel seen. I personally hate when I go to any sort of interaction and I feel like the person on the other side of the table isn't quite being equitable in the balance of their attention or advice. And so I think what the best place for technology role they can play is elevating equity in the conversation. The first place where the science can do that is by making sure both parties are seen and their risk preferences are seen. And then the second part is exactly as you said, Matt helping the advisor and the couple to understand where that compromise lies and what that means for them each as individuals and then as a couple. So we in our work will always look at the individual and then we'll move to, okay, well as a household this is what your best compromise would be.

(33:14):

I know I was just speaking to an advisor where he was describing going through that process with a couple and the portfolio that the client was in was definitely outside of one of the party's comfort zone. And when she went through this experience and saw where she sat versus her husband, she became to better understand why that was the case. And then the advisor explained where their goals were focused and why that was a necessary thing and what he would do to partner with her better. And that was a great unlock for their relationship and both parties, the husband and the wife were both really, really glad that they had gone through that type of experience. So yeah, making sure the client is seen and it's a household they understand where they each sit is just like one of those aha moments or insights that's possible through that revealed preference method.

MH (34:16):

Because I think the risk, as you've highlighted when you say the word compromise, it can mean that both parties leave unhappy. When it comes to risk conversion, there's generally a way forward, but often with the social preferences it becomes very challenging for both parties and the advisor. Yeah,

BDR (34:30):

Absolutely. We've definitely seen cases, we have our ESG and impact sustainable solution implemented in the UK now where you might have one party who does get a little bit of a carve out portfolio to address some of the things that they care about and then there might be a central portfolio that has aligned to other value priorities and so there is always a way forward, but it all begins with, to your point earlier, shahar, understanding the client, understanding the household and where their preferences lie.

SK (35:03):

One of the best compliments I ever got about my research, I'm doing some work with medical doctors understanding their social preferences. Medicine, we expect them to be altruistic, they even have a hope in oath. And in my work with these medical doctors, when I showed them results of one of our experimental games, one of these medical doctors told me it's like the first time that I see an X-ray of someone's preferences. And then he said something interesting that when X-Ray were introduced to medicine, sometimes it was too much information. So we're really, really looking, going back to what we said in the beginning, it's important financial wellbeing is important and we are looking for partners and advisors that are not shying away from information. Most of the time this information is good, you can actually find a better product, a better outcome. If this information, for example, reveal that there are tensions or mismatch, let's say between a portfolio and goals for example, well, we need to handle it in order to actually get people to the financial wellbeing that they can be.

MH (36:24):

What you're asking advisors to do in the conversations you're asking advisors to have are a long way from the risk profiling questions that they're probably doing now, which in some ways is a hygiene factor. It's something that needs to be done for compliance. How are you supporting advisors? And maybe this is a question for Bernard to understand the intricacies of what you're doing, the outcomes and the type of conversations they're going to be facing into. Yeah,

BDR (36:45):

I mean, so for an individual advisor, we always onboard them with a 30 minute session where we kind of unpack, here's what's happening inside of the activity. We can give them as much science as they want, but the simple thing that we give them that's the most powerful is the following narrative. We say What you would want to tell your client is, or your prospect that you're trying to get to know is there's really no better way for me to look deep inside who you are and how you make decisions with your money than to put you inside of a quick interactive experience to let you make some trade-offs. And then the insights that come from that are actually quite intuitive. We unpack that for them and we kind of step them through an eight to 10 minute conversation they can have with a client on an acquisition experience for an annual review.

(37:35):

We do the same thing. This is how you would use these types of interactive experiences to kind of enliven a annual review or how you would reengage or engage at all the next generation, so the kids of your clients and exploring their preferences. So I would definitely recommend for anyone that's interested, they do that onboarding call. It's 30 minutes while spent and it really takes you from curious to confident with the whole use of these types of experiences. And I would also say no need to throw away your favourite questions. If you've got a favourite question you always like to ask a client, just work that into the conversation as well.

MH (38:18):

Before we go, is there anything that you haven't solved for that you are wanting to solve for?

BDR (38:22):

Something that we just solved for that I just would mention is this whole retirement income certainty challenge that really, I know superannuations in the retirement income covenant obligations face, and so I'm just super excited about that and maybe we can put that on the podcast link as well. For me, this whole space of integrating preferences and combining risk preferences and social preferences and income certainty preferences into one holistic playback for the advisor and the client, that's the next place we're going and I think it's going to be super exciting, the merging of these preferences to give a client a clear and transparent read where they stand on all of those dimensions

SK (39:09):

In this entire journey. I say that the most significant thing for me was that we were able to take my research and first do good with it. It's a wonderful feeling for a researcher on something that I actually think is tremendously important, but I think that we can do even better and the way that we can do even better is finding the right partners and the right partners of the right financial advisors. I always like the conversations with financial advisors listening, what works for me, what doesn't work for me? There is enormous amount of information that we can actually get from the clients in a very short amount of time and also by basically doing our experimental game. There is learning by doing, the client is actually learning about himself, which can actually bring the entire conversation to a different level. However, squeezing everything that we can from the information that we have. I actually think, even though I'm very nicely surprised by the way that we are doing it, we are able to present very complicated and very rich results both to clients and advisors and get them to engage on multiple dimensions. We can always do better in order to do better. We need the right patterns.

MH (40:36):

How many advice firms would be using your tools at the moment? Both, I guess in America, Australia, and maybe globally

BDR (40:42):

This year we surpassed half a million clients in the ecosystem, so those would be individual clients, which I don't have any statistics on other profiling techniques, but I tend to think it's one of the fastest growing in the world in Australia. We've just gotten started with our new suite and I don't have the exact statistics, but I think there's about 42 different firms on the platform. As of right now, we call our target advisor the pioneering page. If you're an advisor who's looking to grow your business, wants a better client experience, wants to modernise it, this is actually doable. There aren't many doable tech things out there. Some of them are quite involved, but this is actually quite doable. So yeah, I'm excited about where we'll go in Australia in particular.

MH (41:31):

Unfortunately we have run out of time. I've got plenty of other questions, so we might need to do a follow up. But for those advisors that are interested in finding out more capital preference website, presumably Shahar, that's got your research papers on there, if everything

BDR (41:43):

Is on there, it does. If you go to capital preferences.com au, you can take a look at the solution that's in Australia. There's some, the science tab will get you some of the videos and papers and other things or book a demo and we'll take you through it. One-on-one.

MH (41:59):

Wonderful. Well, I appreciate you spending the time. I know it's late in the evening for you, Shahar. I hope the snow is good for you tomorrow and hopefully we'll get you both out to Australia soon to continue the conversation.

BDR (42:09):

Thanks so much, Matt. Thanks for hosting us, man. Thank you very much for moving us.

MH (42:13):

Great to chat.