AI-Powered Asset Management: Taking Investment Decisions to the Next Level

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About this talk:

8 of 10 investment managers are integrating AI into their investment process. As its adoption among institutional investors is on the rise, AI providers must raise the bar to ensure human and artificial intelligence go hand in hand. How can AI help asset managers navigate today’s market context? And how can it work alongside decision-makers to provide forward-looking inputs? During this panel, Ferdi Van Heerden (CEO of Momentum Global Investment Management), Tommaso Migliore (CEO & Co-founder of MDOTM Ltd.) and Marija Primorac (Senior Consultant at Casey Quirk) discuss how asset managers are using AI today, and the way it is providing forward-looking insights to help institutional investors stay ahead of market complexity.


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Topics covered:

06:16: Why AI is no longer a niche, but a go-to technology for modern asset managers

11:13: The difference between AI and traditional approaches in the investment process

14:02: Practical case study: What culture does an asset manager need to successfully adopt AI?

20:27: Where is AI heading in investments?

29:34: How MGIM is using MDOTM’s AI in its multi-asset investment process

35:51: MDOTM’s AI and track record: surviving Brexit, Covid-19, the Ukraine War and more

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Full Transcript:
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Marija:

Good afternoon, ladies and gentlemen, and a warm welcome to this webinar on the topic of Next Generation of Asset Management: AI-empowered decision-making. My name is Maria Primorac, I am a strategy consultant at Casey Quirk, a Deloitte Business and I will be your moderator for today.

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Before I introduce our panellists, I'd like to take a moment and share with you why this is an exciting opportunity and a topic that we will be addressing. Asset management is a very rewarding and exciting industry, but the complexity of doing business is exponentially rising and as a result, managers are faced with a lot of pressures, and their ambitions and growth potential are at risk.

So what we're seeing is a number of those firms starting to look at technology as ways of optimising their operations and building scale. But as always, there are a couple of firms that stand out and tend to be the front runners in the space. Those firms go beyond the operations. They look at technology and explore Artificial Intelligence strategies as a way of generating insights. Those insights are important because they can lead to better and more informed investment decision-making, better informed business decisions, and a better way to serve your clients. 

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So what's the catch here? The catch is that a majority of these firms are not tech companies. Their core value proposition is delivering superior investment outcomes to their clients. So what do you do in that case? You'll look for firms that have technology in their DNA. And you'll look for strategic partners. That makes for very exciting and compelling points and opportunity for Open Innovation partnerships. And today we are here to explore one of those cases. With that in mind, I'd like to welcome Ferdi Van Heerden, the CEO of Momentum Global Investment Management and Tommaso Migliore CEO and Co-founder of MDOTM and thank you for being here today.

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Of course, Tommaso and Ferdi, you recently announced and entered a strategic partnership, one that is based on Open Innovation exploitation. The audience perhaps is not as familiar with your firms, so would you mind taking a moment and give me one to two-sentence elevator pitch on what your firms actually do and what your unique selling points are. Ferdi please, please start.

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Ferdi:

Momentum Global Investment Management, we would describe it as a boutique, multi-asset investment manager. We've been in this business since 1998. I've got a parent company as much older, a large life insurance and investment business. But I guess our key focus or core focus is built around outcomes-based investing, and also advise listing. And advise-led investing. So everything we build is around delivering on those outcomes, making the journey palatable for clients and so forth.

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And in doing so we obviously always look at how can we enable that in this world of us, that's become I guess implicitly or exponentially more complex as you say, Marija, from now compared to 20 years ago.

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Tommaso:

I am Tommaso Migliore, I'm the CEO and co-founder at MDOTM. We are one of the three largest companies that focus on developing AI for the investments world. We specialize in putting all our effort in R&D coming up with with this technology and then we partner up with financial institutions from across the globe to support their investment process. And a key factor it's always how the integration happens, so it's interesting to see whereand how it cait can be applied. The company was founded by myself, and my co-founder, now seven years ago in London, where we keep our main office and now we employ almost 60 people from 12 geographies. So it's growing well.

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Marija:

One of the three leading companies. Wow. Congratulations!

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Tommaso, my understanding is that your clients tend to be not only more traditional asset managers, but also asset owners. Is that correct?

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Tommaso:

Yes absolutely, I do think that what we do is  supporting the investment process and seeing a way in how technology can enter those processes and so, it's not really just for asset management it really depends on a variety of institutions. Of course one of the most fascinating things I've now encountered by entering into this business of providing technology to these established firms is how much each one has its own nuances. Is on really added value. So it's really important to work side by side and understand where and how you can support the investment process. And that of course happens from asset management, insurance companies, pension funds, wealth managers, private banks, family offices, really, kind of across the board.

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Marija:

Thank you for that clarification. It sounds like you spent a lot of time going around and talking about Artificial Intelligence and the benefits of using it. But we both know, well all three of us know that Artificial Intelligence is not a novel topic that they have.  There have been asset managers, more quant-oriented jobs and also specialised hedge funds that have been using that technology for many years and actually have proven that it can deliver quite great results. So my question to you would be, why is it just now that Artificial Intelligence is becoming more than a buzzword for more traditional asset managers?

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Tommaso:

I do believe that it's happening because we are at a critical moment in which few things are happening altogether. You know this is not the first time I got asked this question and when I think about this I always go back to Leonardo Davinci, probably because I'm Italian and there's something in there. But if you think about it Leonardo da Vinci theorised flight 500 years before the Wright brothers actually were able to lift off and take off with their plane. Why? Two things. The first thing was that the engineering materials materials and power, it was not really there, and then the theory was not really there. Leonardo da Vinci's Flight machine is similar to what an airplane looks today, but they're quite different  when you get into the detail. Without Artificial Intelligence, it's kind of the same thing.

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If you think about it, AI has been theorised before computers were actually invented. Clever systems, able to learn from examples. The problem was that on one side the theory was not really there. So, we did not really understand how a computer can actually learn something relevant. And that's the first thing. And then the second thing, that the material, the technology, was not really there. So cloud computing now allows to have very powerful computing power on demand, when you need to do the calculation rather than building a big warehouse. So where AI at the beginning was just for, as you said, very specific, tech-focused, point-focused, sharp and now it becomes more democratised. It becomes available to a wider audience.

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And also at a very early age - think about when we started in 2015 - it was applied just to very detailed things. And as the technology improved and as the theory improved on how to make these systems work, that's where we saw a wider adoption. And of course as the technology becomes easier to implement then the use cases become easier to find and to bring value to the market effectively.

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Marija:

Thank you for that. Ferdi, please go ahead.

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Ferdi:

Yes Marija. I think from a from a business perspective, clearly, to all this speaks from a Fintech perspective, a specialist perspective. I think for us as an asset management business we've been, as you say, involved in quant strategies for a long time and indexing and things like that. For example, I think we've even, in our parent company, used  natural language processing in some of the call centers to identify better responses to client queries and the nature of those. But not so much in the investment processes like he says.

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You know, as MGIM, I think we've been involved in in quite a few things at the at the UK's Investment Association that is quite focused on I guess amplifying or exposing fund asset management  to the broader industry because I think there is an understanding definitely in the UK market that you know well.

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Firstly London is the center of authentic development also, and so it is an intent to  make sure that the industry really kind of get to grips with the opportunities that is provided by technology In a broader sense, I think for us as a momentum business, we have realised that to change or to digitalize your business is not a technology-focused, it's a way of doing business. It's a people strategy, it's how you engage with clientsand it's enabled by things like AI. And so, when we started the conversation with Tommaso, we were looking at a number of things to say, well, how can we simplify the complexity of data in our investment analytics? Because I think, you know, it's not only fundamentals anymore, you know as it used to be that drives investment decision-making.

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I think sentiment, emotion, the complexity of world affairs that's so intermingled. I think you need to understand at a very different level and I think it is a massive opportunity to deliver on that. And I think cloud technology certainly also. We've been in the cloud as engine I think for close to 10 years now and it's evolved over that time.

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We followed the partnership approach we outsourced this component is open architecture. So, so we are I think of the right mindset to look at opportunities and see how can we connect these nuances, these new things actually, so that we can deliver better outcomes for clients but also a better experience for our own staff. 

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Marija:

Great. And that is very helpful to understand. And maybe just for the sake of the audience, let's take a moment and perhaps just clarify what the difference is between using Artificial intelligence, what it means, and in the investment process and what Quant jobs really do, Ferdi or Tommaso, if either of you wants to just take a point to clarify for the audience,  that would be great. 

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Tommaso:

I can jump in and then leave early to to give more like the marketing, industry standpoint. From a data perspective, I do think that kind of further already highlighted a little bit, it's a different way to treat data and look I come from econometric and finance, that's my background. So the main difference, between AI and a traditional econometric model, it's really the ability of doing two things that as humans we do quite naturally but the technology is not immediate, and it's the ability of learning, critically from the past. So using the past experience not just as a as a 'copy and paste' to to represent the future, but as a powerful source of information from which we can derive ideas and prospects of how the future might evolve. And that's the first thing. So using the experience better and then secondly adapting quickly. Probably, the human mind is one of the most adaptable things in the world, right, we're really an adaptable animal. We change, we're really quick to come up with new things and so AI, when it's in continuous learning  - so when new data is produced every day, it can be put inside the model and the model is able to adjust with this new information. And continuous learning,  so it keeps adjusting and be more relevant and it gives the model that ability of being more relevant for the present.

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Often people think that as time passes by, AI will become better. It's not that it will become necessarily better, it becomes better because of the R&D efforts. It becomes more relevant to work in that dynamic. Then, three years from now, or three years prior than now. So I think from a technical standpoint, this is the main difference. 

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Ferdi:

I would support that. I think from our perspective, clients typically used to be easier replication technologies and so forth, I think  For us, the beauty of AI, I think it needs a learning experience. For us I think tomorrow and his business delivers it daily. I think for us, I mean it is, it's a newness, but it is making sense of uncorrelated data or traditionally uncorrelated data finding  those new connections and making sense of things that typically a human mind would not say almost intuitively that they are connected, for example, but they may very well be connected in the current context. And I think it's that learning that is quite important for us as a trigger event for example.

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Marija:

Thank you. And Tommaso, I believe that the recent panel that we both attended and presented at, you called this assisted decision-making and it's actually the term and the phrase that stuck with me this entire time, being able to make better decisions by knowing the data better and removing some of the human biases from the process of decision-making. Ferdi when you were considering partnering up with Tommaso, how did your culture at your firm react? and just to put it a bit into perspective, sometimes, with a bit more traditionally oriented firms, when you mention technology and the big transformation, the immediate reaction is: Is it removing 'human' from the process?, and a real world example is that we're seeing self-driving cars powered by technology. So how did you adapt your company's culture and DNA to this shift in the mindset?

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Ferdi:

No, thanks, Marija. I think it's a good question. I think depends on the on an organisation. I think the culture and engine has always been inquisitive and so forth. I think forward-looking. But I do think, I mean we've also added leadership conference earlier this year actually we had someone who could take mounted speak and I think the words that came out is very similar to what you see from us. It was augmented humanity, which is take and people integrated in a way, right. It's it's human + machine, somebody else, there's nothing else.

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Messages start to drop with people. And then I do think we're quite actively

involved in some of the investment association activities. All those things help people to get a closer to understanding that these are solutions that can help our business going forward. It's quite exciting and attracts new people due to the industry and certainly to our business. But I think it is leading from the front right. So I think for me, I've made the personal interest area right, So I try, to connect with firms like that of Tommaso and before we before we came across MDOTM, we had other discussions in the space but I think we realised that. to be successful in a business like ours, it can't be a plug and play, right. It can't be a black box.So it is this interrelationship between somebody that has the expertis and the knowledge to make this work in a business like ours.

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And it needs the right mindset in our business to engage with, with it, with the technicians that can really make that work from a development perspective or implementation perspective. And I think that's where we got to. I think we created almost a space for people to say well,I can participate in it, it's an exciting space, I became involved and I think so, as a result many people latched onto that. We often think people aren't focused on latching onto new technology ideas because they see it as a thing, I think it's more often than not business priorities, right, so if you if in a business like ours, we make this a business priority and we show how it can work and we lead from the front and then it becomes natural

and then people make time for that, but we've got to enable that nothing we've done that well.

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But I also think that the people in MDOTM and the team that I have here, have connected incredibly well and I think it's that culture fit that you need. I mean with other firms it might not have happened right. Maybe that's the lesson that I've seen here, is in this case it was a natural kind of link up of people of meeting of the minds if you want, right. So a common learning approach and I think the matter for me that's what we're on, we're on a on a learning journey because you also say it's not a technology that will be finite at some point right. This is evolving learning that we are going through, it's building trust, in what it can deliver and so forth, and I think that's where we are, but the relationship and the partnership that we have between the teams, and that we've been able to forge in a fairly short space of time is that connectedness and working together and the strive to find something that really works well for the investment outcomes.

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Tommaso:

You know to add on top of this I do think that Ferdi kind of gave a probably, almost like a master class on what innovation is all about. Like the world today is moving so fast, there's new technologies popping up every day and at the same time the new technology, because the obvious things have already been done, the more time passes by, the technology that comes up, it's actually more detailed, it's more specific. So it's actually more complicated to a certain extent.

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So you need to have that kind of culture and it kind of comes across, and look the work that we do is help organizations to evolve and become, you know, more adaptive to a new world to a certain extent. But it all starts from the people, from the people at the very top

who have to have that openness of mind and of culture that we need to embrace new things, look at it with high curiosity, think about how we can bring it internally and how we can all grow as an organisation. So it all starts, I believe from the people and then it's a process and technology factor. So your question around culture is not just right, but it's the probably the most important thing and you can clearly see how much new technology, there are some companies that are just able to keep driving innovation, and how do they do it?

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It's because the people are able to be open-minded, work with high curiosity, and looking more of the opportunity than maybe the pitfall in the short term. And that's the best thing. Because if you think about it technology is never immediate, it's never that, especially in processes complicated such as the one that we're talking about. It's not that it's a plug-and-play, it's always that there's a technology and I have to learn it and understand how I can use it, and if you don't, it's not immediate. So if you don't have that curiosity mindset and the culture of the company has been around that, it's really hard to do it right, so that's why usually there are some companies that have that, you know, mindset and those are the ones actually that if you measure 2-3 years down the line, they're usually the ones leading the pack afterwards, but it all comes down to people. So it's funny if you think about it because we develop technology, but then it's always down to the people at the very end.

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Marija:

You've mentioned that it takes time to catch up, and usually that you start with some first ideas, with some innovation and then you see whether it will stick or not. So if you were to make any prediction about the road that AI will take going for going down next couple of years, let's say 5 to 10, where could you see AI playing a bigger role than it's playing right now? And this is a question to both of you, where you see that Tommaso, perhaps even your firm will go, and Ferdi, you say that you might expand the use of Artificial Intelligence in your firm.

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Tommaso:

So I'll probably start with the technology side, and then leave Ferdi for the kind of industry,

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and asset management perspective in general. But I do think that from a technological perspective, you mentioned it before, assisted decision-making, the new era that we're entering, What does it really mean? It means that before about until now, humanity in general, it's in what we call data-driven decision-making. Lots of data, lots of statistics and adding, and then it's just down to the human to connect these things, if I decided before. Connecting the dots, putting together the dots means making a decision. Right now we have computers, and AI is investing is just one of these fields, where they actually are able to read through this data, through these statistics and suggest a point of view.

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I do believe that every evolution happens when the technology helps you along the way, right. We've done this in every process before the technology takes away your job, it firstly helps a human right. So if you have to see or end the time is going and where I believe

the industry would be going down the line. Our goal is to create the most adaptive and user interactable to a certain extent, technology, possible. So that we can focus on developing this technology and then like-minded people, which have the ability of understanding how to use it, can implement it by themselves in their own process to scale.

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How does the industry look you know 2-3-5 years down the line? I believe it looks more efficient. People are really adding value where they're supposed to, they're not. you know, I won't say wasting their time, but investing their time in things that there's not really much of a point of doing it and actually making sure that how clients are served, how the industry is served is better and better. So the technological evolution in my point of view, is very much linked with processes, and understanding the client to the very, end, in a world that is becoming more personalized, faster and more complex to certain extent, that's where I see a technological development to be key in a company success from a technological standpoint. Ferdi, maybe you have a different vie on the industry in general.

Ferdi:

I think there's three areas where I can see AI in the next number of years will play an increasing role, right? The first one I think is our products connect with clients. So it's in the advice lead components. So we say, well we believe in advice-led investing. Then that's an important element that, you know, as we go forward for advisors and anybody who provides that, that part of the value chain will be able to benefit far more from AI and connecting a client with the right set of solutions at the back end. So that this list, I guess, uncertain outcomes for clients who are not in the right mindset. And so, I think the advice concerning that there will be lots of good developments and there has been.

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Secondly, of course in the investment engine inside it, I mean for us it's in making sure we have,  really the most efficient asset allocation analysis that we can have strategic asset allocation, tactical asset allocation, we manage global funds right. So, but it is a complex world, is lots of data. I want to make sure that when when the portfolio managers consider positions that don't spend too much time on the input, but actually they look at the output from something like the AI solutions that we that that Tommaso's team are building and developing with us, for us. And so that the decision-making can have the human element actually that we still need on top of those outputs, get better time I think sometimes I feel that a lot goes into the input and we have more cramped time to make a decision actually.

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So that's the second, but I think the third component that certainly, you know, well I can see the complexity is increasing and that is reporting on sustainability on all factors, ESG some of the information you can get instructed data right like the lifes of sustaining and kind of a lot of informatio on Bloomberg itself,  but there's a lot of unstructured data available. And increasingly a number of providers at night since also of three national language processing lifting up some elements that you can see on a lease quants basis. But more on today, based on a more qualitative basis of information in the markets. And I think then eventually how we report on the text taxonomy that lives in Europe versus the UK versus the US will simplify our lives for us as as a manager. So I can certainly tell you those those three areas are getting lots of air time also. In this situation, and certainly in my business.

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Marija:

That's a really good point that you called out, Tommaso, are any of those areas in the plans for MDOTM going forward?

So we mentioned sustainability is a key one, especially if you're operating in Europe, but also even globally, that divergence in how you define sustainability, ESG and so on. So absolutely,  ESG is something that we already do to a certain extent and we are very much looking into developing a big chunk of our technology around it. I think that Ferdi highlighted the problem, and because we are at the very core data scientists, for us the lacking of structure and we could say coherence, between data, between scoring, between information is, is really painful. So the way that we're looking at this problem is really how can we have a very synthesised version that actually checks out for these anomalies in the data. 

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Because ultimately there's a nice concept that applies to technology and applies to human decision-making. Garbage in, garbage out. If you look, if you have poor-quality data you will never have a great output, right. So when it comes to ESG for example, the very first step is making sure that the data is correct, is meaningful, is not contradictory. It's really a good representation of what we're looking at. And of course unstructured data plays a role, but also structured data and how these are looked through by different engines does play a role as well. So the word that we're currently doing this is again in our R&D efforts it's around making sure that the Garbage is not garbage when it comes in. So it's quality data in so that we can build up and then hopefully very soon we are able to provide an additional point of view that it's really a data based and fact based and not just a bit chaotic and inconsistent. So yeah, that's what we're doing in thein this space.

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Marija:

Great. Thank you so much for that point, especially around sustainability. It's a very crucial one for anyone operating in Europe. I like to make sure that I have taken all the key takeaways from this discussion. And I think when we think of going forward and how we will see AI being used is very much, it will lead to more efficient processes and the firms will become more efficient using them, which will enable scale. Second point. I took away was that the interaction of technology and humans will be crucial. That human element will still be an important one, and that technology is there mainly to empower the human decision-making, so it won't remove the people aspect of the business out of it.

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And then I think Ferdi you raised a pretty good point, which is that AI and technology

will enable personalization. It will bring products and clients closer together. It will connect them.I would like to thank you for making all those points because this has bee a very insightful discussion for me and hopefully for our audience and being cognizant of time, I wanted to leave some time to address our audience's questions. So if you're ready, I'll start with one that we just got in for Ferdi, and it's actually a great one, because it will provide everything, put everything in a little bit more perspective and make it more tangible.

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Ferdi, could you elaborate a bit on how you use or plan to use MDOTM's AI

in the multi-asset investment process

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Ferdi:

Yeah, I think, I mean we start with, so when we started the journey, the question is always well where do you start? right. So do you look to maybe set up a portfolio and you just run that through the AI engine or do you use it more fundamentally? And I think we've decided to use it in a more fundamental way, as to the core input or as echo input to our investment decision-making processes. And so we when we look at multi-asset, I mean these are, our long term strategies, through investment cycles you look at at multiple asset classes across the globe, right. So it's not only geographies but it's all these different asset classes alternatives and diversification, is important and so forth. And so you start at almost the outcome that you have in mind, and for us, it's typically a real outcome. So inflation plus target outcomes and with Tommaso's team if we set up a few models that says this is the long term way in which we want to construct a portfolio and these are the targets, and this is the journey that we typically want.

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So what's the drawdowns like, like what is the volatility in a fantasy, certain parameters and then MDOTM brings the magic, right. And so once that's constructed and in the market context that we are it starts to deliver certain outputs. strategic asset allocation outputs. That deviates from our most long-term view, which is more fundamentally based, right? So and technically the same. And I think we then use that as as input into our monthly tactical decision making forums and quarterly strategic asset allocation views. And I can say actually the initial outputs of course needs a bit of development for our own team, right. So it is one to what extent do you know to take this input versus our traditional input and that often it could be different, right. 

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So it's less intuitive like maybe it's also a lot more triggers, right, that AI would give you compared to our own analysis because it's kind of, it's not as active. And so it's we are in the process of taking these outputs and saying wow, "that's an interesting output" and they try to understand what it is. And I think the beauty of our partnership with your team is, is that we can try to delve in to understand where did this trigger come from actually? Because it's not intuitive at this point in time and it's making that sense and that's we I think the team, Marija, is really building up trust in what this can be, so that in time we only take the output of this model and deliberate the output and how we should invest according to the output or not, right.

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So as opposed to saying well is this a valid output or not. And I think that's

a fantastic journey to be on and I think it will certainly elevate our discussions, at those meetings based on a set of inputs that we've not had before, or that look differently before because we can only do so many of these analysis and so many of these covariances. I mean the technology can do multiples of that and I think we're on a journey but at the right end it's in the center of our investment engine and it's multi asset. I think often AI has been used in the Equity space predominantly I think the Tommaso's team has already been focusing on multi asset, we are multi asset and I think the two  sets of expertise that we have really feed of one another at this point in time.And I think I have high hopes for for what it will bring to our investment engine and the eventual outcomes. 

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Tommaso:

If I may, sorry Marija I do think that Ferdi kind of gave a good idea on how to really embrace innovation. Think about this. He had two options on the table, he could either run a parallel portfolio and just look it side by side and probably if you really understand the technology as we do that's not the most useful thing about doing it because it creates just what another layer on the side but really what he's done and you know. Working with the team, it's it, it's quite evident. He's embracing the technology within the core process, from a more fundamental way, not really having side by side almost like a competition, but actually how can I implement it within the activity that I do every day. And of course, it takes both work.

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It takes work from their team to understand how to change and evolve and how to implement it from a day-to-day standpoint. It takes work from our side to make sure that the output is relevant for them. And it's not just a generic output because otherwise it would be a bit meaningful. Meaningless, sorry, in their specific use but I do think that this is wher we said before the human plays a major factor, and first of all happens in how you embrace innovation which is just not something on the side that I tested. But it's something that I bring inside and look with curiosity and I can see how it can help me within the process that I do on a day-to-day standpoint. And so that is a very precious insight into how AI in the very core activities that each one does in our own daily lives because we just represent

the investment world, but there are many applications out there can be applied and it all starts by having the right mindset, I do believe.

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Marija:

Thank you for that. And as you mentioned earlier, it is an adaptive learning process and it's not only for the AI, it's also for the investment professionals that are using it. So it's a continuous involvement. I think what you mentioned now specifically within the multi-asset process looking at how volatility, how the market is evolving, making more tactical allocation decisions. This next question is a great segway and it's targeted at you Tommaso MDOTM's AI has one of the longest track records in the industry. You mentioned you started off in 2015, so how did your platform react to some of the Black Swan events and increased market stress like the 2020 pandemic, the Ukraine war, the Brexit or the US elections? How can your technology detect shifts in market regimes just by looking at the data, such as fundamental, macro and lastly market data?

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Tommaso:

So I don't know if, I always make a joke, I don't know if I'm very unlucky, because we started the business in 2015. So probably at any moment in time that was wrong the, the worst one because we went through at the beginning of Brexit. I don't know if you remember the 2016, 4th of June, that was already a quite out of the spectrum event and then we went through the election of Donald Trump which of course were very positive market but way different condition. If you're really looking at the market structure, then the 2018 rebound, the sudden rebound in 2019, then the pandemic, the rebound, the big stepping of government and central banks like we've never seen before and now a war.

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So it's not been really the best moment to start a data business in finance probably, but that's one way to look at it. The other way to look at it has been the best moment, because we could actually test it and learn and get a lot of experience, and in a nutshell what we've done and I think that's the critical element of our technology and it's that it's non-chronological. So we try to extrapolate data,  Ferdi said it well in a very easy way before, it's a way to look at the larger amount of data that is out there and simplified in inside. And so it's not necessarily looking back in time, and trying to say, okay, this has happened, so it's going to happen again, but it's more like there have been certain dynamics

that are that have evolved in the past. What can we learn from that and how that and how that is relevant as of today.

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Now we all know that we live in a challenging time  and so the way we look at data when we look at the World needs to evolve quickly but that's the right way to do it. So that's I think has been a core strength and also been through this event we learned a lot and luckily our technology has been very consistent and perform well. But again, it's the learning the most important thing and the experience, that not just us, but also our AI has done through the years, and that's why I think it's moved away from just being equities or specific, into being a broader view, like multi-asset and now working alongside with Ferdi's team again, for every specific need, there are new things that need to be developed, and those may be the very specific things that actually  you wouldn't think how much it actually changed the overall input but that's another important thing. So altogether I think it's a, it's a learning experience again. And that's a great way of putting it.

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Marija:

We are running out of time. So I would like to direct the last question to Ferdi, and I think this is an important one because what I've seen working with clients is that technology is being elevated to the level of the leadership team, having to spearhead it, and it's becoming increasingly important on the strategic agenda of CEO's. And you brought that up earlier in the conversation Ferdi, you are a person that is in favour of innovation. You support it. You sound like a change pioneer, even from your background in the work that you've done with the Investment Association. What would be your advice for the CEO's out there, they're a bit more risk averse and they're not sure on how to brin the technology into their culture, because from this conversation it seems like one way or another you'll have to start adopting it.

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Ferdi:

Well, first, if you're risk averse, this is exactly what you should do, right? You should get involved in technology because not doing it is the biggest risk of all. I think so. So I think there's always an imperative to do it, even if you don't like it. #1, but #2 I think you know

the world around us is massively changing, right, in terms of capabilities and that is driven by technology enablement and whether it's AI, whether it's distributed Ledger technology, whether it's tokenization of assets, for example, these will all change our industry and our businesses and financial services, massively open architecture, for example, open finance and open banking, so I think. The advice is to personally adopt it, to get involved, to start to learn, or make sure at least that two peopl start to learn and get excited about it. But I think it's difficult for a CEO to expect your team to be excited about opportunities around you if you're not yourself excited about it and I do you think it's getting that excitement internally  and then really starting to look and I think that's imperative for all CEO's of all financial services firms and back in fact all firms I think need to start to think very differently about the business model, about how the business model will need to change over the next 5 to 10 years, and I think maybe we long time ago spoke about.

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And in the next few years or so, I think change is with us and upon us and around us. And I think we have to adopt it and it's exciting. Maybe that's why my last point is if you get involved and you get to learn about it and you see the opportunities, that's where the excitement is. 

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Marija:

Great. I think this has been the great summary and conclusion of this webinar and not one I could beat. So I would like to thank you both for joining this webinar and sharing your story on how the next generation of asset management could look like and how humans could be empowered by AI and make better investment decisions, business decisions, serve their clients better. I hope that we reconvene very soon and discuss on how successful your partnership has been. For our audience and my participants, thank you so much for listening and I wish you all a great rest of the day.

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