The Connector Podcast - Shaping the Future of FinTech with swissQuant's Innovative Banking Softwa...

Welcome to the Connector Podcast, an ongoing conversation connecting fintechs, banks, and regulators worldwide. Join CEO and founder Koen Vanderhoydonk as you learn more about the latest available trends and solutions in the markets.

Welcome to another podcast of the Connector, and today I've got a very nice new customer with me. It's SwissQuant, and represented by Salar. Can you introduce yourself and maybe afterwards also introduce SwissQuant?

Yeah, sure. Hi, Salar speaking. I'm heading the sales team at SwissQuant. I have my background in wealth management as a client advisor, but also from more strategic positions. And I'm looking forward to the discussion today.

Ooh, Salar, can you explain a little bit more about SwissQuant?

Yes, sure. So we've been in the market for now almost 19 years, and we help banks, clearinghouses, but also external asset managers with our software. And that is in context of advisory, but also discretionary management and analytics, which we back up with our quant DNA.

And while we're here in Zurich today, you're Swiss-based. Where do you provide your services? Which geographics and what's the ambition?

Yeah, of course, our home market, I would say, is Zurich and Europe. That's where we have been operating since the start. But luckily, we are now also represented in Australia, in Sydney, throughout the Middle East, which is a very attractive market for us. And hopefully soon also in South America, in particular in Colombia. Wow. In the advisory, when you look at it, probably every bank does it a bit different, but there are quite some overlaps. And what you want to ensure as a software provider, as a fintech, is that you really can cover the end-to-end journey for a bank. And When you do it, you want to do it nice. I know it sounds very naive and easy, but that's exactly where the difficulty is, right? Because everybody can build nice UIs and a front end, but you have to back it up with qualitative tier 1 grade data and analytics. And that's, I believe, where our strength is, that we can really not just cover the width but also the depth when it comes to advisory solutions.

And over time, what has been the, uh, the focus points, or how did SwissQuant grow? Was it more because of the front end, or was it more on the analytics part?

Yeah, absolutely the last one. So, uh, I would say when we started, and we were, we were a spin-off from ETH Zurich, right? Uh, we at the beginning, we were a quant house with some software, but now we are a software house with quant DNA, and that stays true to this day. Having said that, we understand and we are absolutely aware of the fact that a nice UI is more just than design, right? It's about ease of use. It's about addressing pain points from our users, which are maybe client advisors, and they don't want something that looks like 1980 MS-DOS style. They want something that looks nice, but also works in a very intuitive way.

You talked about advisory before, and I think that's an interesting part to zoom in a little bit, because some of the countries in Europe, they go more discretionary. How would you sort of combine advisory versus discretionary, and how does that actually fit within your solutions?

When we talk about discretionary, we see also there are quite heterogeneous landscape, what banks or our customers define as advisory. discretionary. So it could start with the SAA generation as a top point and then the assignment to the client portfolios. And then it's about very efficient rebalancing.

Mm-hmm.

And that's what, what is at the core of our optimizer engine. And when you look at both advisory and discretionary, ideally you want to have the same underlying data because you want consistency in your reporting, in your client conversations. And this is where we see the overlap. And then also the beauty of our— I'm not trying to make too much sales here or marketing, but the beauty of our Optimizer engine is that it's actually the flexibility it offers, right? We can cover with the Optimizer engine, we can cover the advisory use case, i.e., I'm a client advisor, I have 5 minutes time to create investment proposals. I can go and use the Optimizer. It gives me a ready-to-use PDF with investment trading— Yeah, with proposals. Exactly. The other use case we're covering is, I am the discretionary portfolio management specialist, and I have some very intelligent portfolio managers, and I don't want them to spend 10 days of the month by just simply rebalancing in a very repetitive way. But let's free up some resources so they can invest it on more strategic problems. And then the last part that I would also actually also consider as discretionary is what is known as robo-advisory.

Mhm.

We also cover that, but I'd like to make a point. It's when oftentimes when we speak about robo-advisory, it's maybe 5 to 10 ETFs in the background that where the weights get shifted around, and that's called robo-advisory. I'd like to disagree. Our Optimizer is actually really sourcing from an investment universe that is defined by the bank, and it invests in single instruments. Some of our clients already offer it at 200 francs starting investment, and they achieve that, of course, with fractional shares. But what I'm trying to say is that the Optimizer engine can really go the full range, and you have the same consistent data underlying it.

Well, talking about the Optimizer, I think that in the latest years, something very interesting happened because ESG came to the mix. The fact that the end investor can make a choice and has to make a choice, or they opt out, which a lot of the investors actually like. And I think the simple reason is because their systems are not ready to handle that. How are you guys doing that?

That's a great question. I would say, in all fairness, there has been optimizers in the market that could always cover some restrictions, right? There were optimizers that could exclude certain industries, for example. But what not a lot of optimizers— I don't know many at least— can do is proactively reflecting ESG preferences into the equation. And important to mention, without any downside in terms of risk-return expectations, right? Yeah. And we can go into the depths of it. I don't know if it's maybe too much detail right now. Mathematically, yes, it's true. There is only one optimal risk-return profile for each of the clients. But it doesn't matter if the return expectations is at 3% or at 3.0000001, and then has a better ESG preference rating on that one. And that's where we think the innovation is. So we're reflecting those ESG preferences proactively, not just as a passive or let's say as a secondary restriction. I think that is where you can also drive the discussion as a client advisor differently and add value.

So you change from profile business to personalization business.

Absolutely. Bridging that gap between industrialization and individualization. Because we cannot forget, right, banks are under enormous pressure in terms of margins. They have to increase efficiency. We are not anymore in the late '90s, or there has to have some improvement happen. And this is a nice tool that allows it to actually achieve that goal.

And very relevant. I think a lot of banks don't have a suitability engine that has been prepared for this new situation. What intrigued me about SwissQuant is that you use obviously your analytic powers in the wealth management industry, but you go far beyond that. Can you explain to us what do you do and how do you actually use that capability in other markets?

Yes, absolutely. And your observation is absolutely right. Traditionally, we served banks. These were our main clients. But throughout the years, we also understood that What we have built, the risk engine, for example, the use cases are actually much wider and broader. And for example, if you look at impact analytics, this is our investment analysis suite that we offer for external asset managers, multifamily offices, but also CIOs in some banks. And The reason why we decided to go into that market is that with the risk engine that we've built, this is a banking-native, a wealth management-native risk engine that allows a holistic view. And this is an important point, right? Because I know there are engines out there, there are solutions out there, they cover one asset class perfectly, but a wealth management client is interested in this holistic—

It's always multi. Absolutely.

So we're talking, so on the one hand, On hand, it's the the width again of the asset classes we cover. So we talk about equities, fixed income, but also illiquids such as private equity, private debt, and that's that's something where we can add value. But then also depending on the value proposition of our clients, they they they don't just want to show let's say a very simple volatility KPI, right? They want to they want to show their expertise by being able to discuss a variety of KPIs such as C. They want to do stress testing. And these are things that we always had in the pocket, but we understand only since maybe 2 years now that actually external asset managers and family offices are super interested in it. And that's something I'm actually looking forward to a lot.

Oh, nice. And what else is on shop? What else is the future for SwissQuant?

When you talk about future and don't mention AI, probably that's very naive.

Did I mention AI?

So AI is definitely one topic that we, I must say, traditionally always looked into because our quantitative methods always required—

It's a very thin line between analytics and AI in a way, right?

Absolutely, right? And our risk engine, for example, always has been applying, let's say, Monte Carlo simulations and very close nearby neighbors. And, and what we have done in the, over the past 2 years before, I think, or with and before ChatGPT, we were looking into actually use cases that add value. And it's not just because it says AI. And, and we have identified actually some use cases, for example, campaign management. Everybody knows campaign management. You have a trading idea from a central sales team that gets distributed to client advisors. I was a client advisor. I know the pain when my central sales team sends me a trading idea that is super irrelevant to me, but I get the extra work because I have to say why I didn't invest in it, and that that's just frustrating. So this is actually a field where we think AI makes sense. So we enhance our buy probability estimations for a campaign for each portfolio on portfolio level. And then combine it with the optimizer we talked about earlier. And then you get a very customized investment proposal for a trading idea. And if it didn't work this time, we make sure that we applied machine learning to improve the results for the next time. So this is one, I think, one of the use cases that AI comes into place and it's going to be.

Seller, are you almost saying that you're augmenting the journey of the advisor? By giving more insights that are actually on top of the actual small between brackets proposal.

Augmenting is the perfect word because if you know the wealth management industry, it's still a people business and it's still the banks. If you look at the big players, they invest a lot in the advisors and they bring a lot of to the table. So you don't want to necessarily fully replace them. You want to help them. You want to be a copilot. You want to augment their work. And that is exactly what we do. We bring maybe 80%, and then the final 20%, the important call is with the client advisor, and they can decide if they want to go for it or not.

Well, well, makes a lot of sense to me. We're almost at the end of our podcast, and I think I have one very important question still to mention. Is how can people contact you?

It's quite easy, actually. So the easiest way would be our website, which is www.swissquant.com. Other than that, we are active on LinkedIn and also present at several conferences.

And I think you also have a very nice podcast and some good white papers as well on the website.

Yes. Thank you for bringing that up, Koen. Our marketing team is going to be very happy about this and they put a lot of effort into it. So please make sure to check it out.

All right, Salah, thank you very much for having you. And thank you also to the listeners. And stay tuned, more fintech news from the Connector Podcast. Thank you very much.

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