Meet Kevin McLoughlin, Partner and Co-Founder MTech Capital

MTech Capital invests exclusively in companies in the InsurTech space

Venture capital used to be a cottage industry, with very few investing in tomorrow’s products and services. Oh, how times have changed! While there are more startups than ever, there’s also more money chasing them. In this series, we look at the new (or relatively new) VCs in the early stages: seed and Series A.

But just who are these funds and venture capitalists that run them? What kinds of investments do they like making, and how do they see themselves in the VC landscape?

We’re highlighting key members of the community to find out.

Kevin McLoughlin is a Partner and co-founder of MTech Capital.

McLoughlin is the former head of Global Insurance Investment Banking at Bank of America Merrill Lynch and former Head of Global Insurance at Citigroup. Prior to joining Citi, he was Head of European Insurance at UBS. His finance career spans over 30 years, including 22 years in investment banking working exclusively in the insurance sector. McLoughlin has worked with many of the largest insurance groups globally, advising on acquisitions and divestitures, raising of equity and debt capital, IPOs, investor communication and market valuation issues, and strategy. In 2018, he co-founded MTech Capital, an InsurTech venture firm. He began his career at AIG in finance and investments where he spent 7 years starting in 1988. 

McLoughlin graduated cum laude from Boston College with a BA in Economics & Political Science. He has an MBA in Finance from Columbia Business School. He is based in London.

VatorNews: Let’s start from the top on the big picture about MTech Capital and what you’re all about, your philosophy, your methodology. Give me the overview of where you fit into the ecosystem.

Kevin McLoughlin: We started MTech Capital about four years ago now. My partner, who was based in Santa Monica, is also my brother; I’m based in London. We saw an opportunity in the market where we could see that digital technology was due to transform the insurance industry, very much the way it has transformed the banking industry, but with about a 10 year lag. I started in the insurance industry at AIG, then went into investment banking, spent my career there covering the insurance industry. Brian, my partner, spent most of his career in venture capital in FinTech. And so, we architected MTech Capital to really address a need we saw in the market at insurance companies, namely that they were looking for greater insights, more information about what was happening in InsurTech, what the trends were, what technologies were getting traction. For a lot of them, they felt like they didn’t want to set up their own corporate venture capital arm and, even for those that might one, they appreciated that nobody has a complete 100% purview over the market, and so having additional eyes and ears would be helpful. 

We raised about $100 million, and our investors are insurance companies located globally. We are entirely independent in terms of our investment decisions, but we certainly utilize our investors as part of an ecosystem. We may look at an investment opportunity, we look at a company, and like a lot of things about it, but we think it would be really helpful to have some direct feedback from an insurer. We make that introduction and we get that feedback and we call on insurance companies who are investors with us, and sometimes outside, because we’ve got a large global network of relationships. And so, that really helps inform our investment decisions. From a financial standpoint, we think it will drive, and has driven, very good financial returns. So, that’s the background to MTech Capital.

In terms of our investment philosophy, we don’t really start with a particular thesis and then go out and look for startup companies that are aligned with it. We don’t feel like, “well, blockchain will be big one day in insurance, let’s go out and find a blockchain company.” That’s not the way we operate. We do have, of course, some views about where we see the industry going, and which technologies we think will prevail, but we tend to just evaluate companies as we meet them. So, we have a deal flow of 60 plus companies per month that we meet and we evaluate them off, firstly, on their merits. We might meet with a company, for example, that does embedded insurance. Let’s say you’re purchasing something, it could be electronics, and just before you check out online to purchase that camera, you’re offered extended warranty insurance; that’s our definition of embedded insurance. We like that as a market trend and we see it developing but, first and foremost, we will focus on the entrepreneur, on the leadership team. We’ll be comfortable with the market because we liked the idea of embedded insurance in this example, then, of course, we’re evaluating everything else that VCs do. So, what’s the total addressable market? How competitive is it? Is the company solving an important problem? Has it demonstrated product market fit? And we relate it to revenues there, so we ask for all those key questions. The fact that it’s embedded insurance, that gives us comfort around the market and the opportunity, but there’s just so much more that goes into our investment decision. To put it in a different way: we would rather invest in a B idea with an A team than the other way around; the idea of investing in a B team, hoping that they’ll find an A idea, no. That’s the emphasis that we put on the entrepreneur and the team.

VN: You said that you don’t necessarily invest in specific technologies, but there are technologies that you see shaping the insurance industry. What are some of those technologies? And what’s exciting about those?

KM: The overarching technology that is transformative to the industry is, broadly, artificial intelligence. Then, under that arch, you’ve got machine learning, computer vision, natural language processing, so we see all of those. With respect to most parts of insurance, it’s really, primarily, machine learning. Insurance is the quintessential business in terms of data; the whole industry is built on data and, up to now, it has not really taken advantage of digitization and cloud computing. You can do analytics today and sift through a lot of data that previously you just kept on your mainframe. AI is the key technology that’s driving what we think will be transformation of the industry. 

I’ll give you an example in underwriting: so the insurer is making a decision whether or not to insure a property, to insure a vehicle, and they’re looking at claims information and looking at demographic data. What the industry hasn’t done for a long time is take advantage of the additional data that company has on that nature of risk, and it certainly hasn’t taken advantage of the fact that, right now, there’s so much big data available, third party data available, because it hasn’t had the tools to sift through it. Maybe they’ll find correlations in that data, that predictive power, that would make the underwriter much more effective about predicting whether or not this property or this individual is going to file a claim. That’s an example of where we see the industry going. It’s far from there yet, but that is the direction of travel. 

We have one company in the portfolio, for example, that uses computer vision. For example, it will work in a warehouse and it will utilize pre-existing security cameras to monitor what workers are doing. It doesn’t require new cameras at all, but they’re using AI to analyze all of the video that comes through with an ability to flag any dangerous behavior from workers. Someone’s speeding on a forklift, someone is lifting boxes the wrong way, somebody is throwing material. Then it flags that behavior, and will actually send out an email to that person’s manager, or the warehouse foreman, and say, “Okay, this is the video clip. This is the individual.” It’s quite powerful, as you could imagine, in terms of preventing accidents. Where’s the linkage with insurance there? It’s workers compensation insurance. It costs employers a ton of money when their employees get injured on the job. So, that’s an example of how computer vision, again, under that arc of AI, is being employed for insurance.

VN: Let’s talk about your current fund. What’s the size of the fund? How many investments do you make per year?

KM: It’s about $100 million. We make about five or six investments a year. There’s, probably, over 600 companies a year we meet with, so we invest in about 1% of the companies we meet.

VN: What stage is that? And what is it now in dollar amount?

KM: We say that we’re stage agnostic, we will invest at any stage. So, we have invested in a couple of seed stage and also at the Series C stage. Our sweet spot would probably be Series A. In terms of amount, about $3 to $4 million per investment.

VN: If you’re investing mostly in the A round, I would imagine at that point they need to have a certain amount of ARR, or a certain number of customers or whatever it is. Are there specific numbers that you typically want to see from a company if you’re going to be investing?

KM: No, there aren’t specific numbers because there’s too much variation. I mean, it depends on when we see the company; it could be at the beginning stages of where we see product market fit, so they might not have many customers, but we think there’s enough evidence there. And we are probably familiar with the product and the problem they’re solving, so we would have enough confidence there to invest without seeing a ton of revenue at that stage. So, I couldn’t say that there are specific numerical targets we have for ARR before we invest. It also depends on the rate: it could be at $5 million of ARR but if it’s taken them two years to do it, versus a company that’s taken four months to do it, that’s a big difference.

VNL: How do you determine that product market fit, especially if they don’t have many customers yet? What’s the due diligence there? 

KM: We look at a lot of companies so we have a good sense, but we also don’t want to rely on instinct, of course, entirely. And so, that is where we will often call upon our insurance company relationships to have a look. Right now, for example, we’re looking at possibly investing in an ESG business; this is a business for fund managers. And, by the way, our definition of InsurTech includes asset management; an insurance company might have third party asset management businesses, but even if they’re just managing their own invested assets, it’s a lot. And so, this is an ESG business where they provide ESG ratings on companies. What is their carbon footprint? What does their gender balance look like at the board and within the company? They certainly seem to have some product market fit, they’ve got about a dozen customers, and some larger asset managers, but we said, “we need more data points.” We’ve sent out probably six or eight emails to insurance companies saying, “here’s a company you might be interested in knowing more about.” Inevitably, they say “yes,” because they are interested as customers. Maybe this is a product for our asset management area that they would be interested in. And so, it’s a source of additional data for due diligence that not many VCs have.

VN: Do you ever invest pre-product?

KM: We did once because it wasn’t really a stretch for us since the product didn’t require a lot of cutting edge technology. It was just about pre-existing technology and the founder had done it before. And so, this was 2.0. and we thought, “okay, he knows exactly who he needs to call.” He had assembled three or four of his key former colleagues around them. And we thought, “he knew what he’s building.” But, generally, we wouldn’t do it. 

We just passed on a really interesting company, actually, but we’ll follow it. That’s an advantage of being stage agnostic: the company’s raising a Series A now, but we’ll track it, and then, in a year’s time, we’ll be in touch with them and when they come back with their Series B, maybe we’ll be investing. We’re investing, actually, in a Series C as we speak, and we had originally looked at the company for a Series A. 

VN: It just wasn’t a good fit at the time?

KM: That was a longer story. It was another VC that came in, over the top of us, and was preparing to do the entire round, we were doing part of the round. And so, the founder just decided, “I’m going to go for the low risk option here and get my Series A done in one go.” 

But this other company that we just passed on, they have developed software for insurance companies, again, around underwriting activity. We got a demo of what you would expect from the company and we do pretty rigorous due diligence. We’ve had lots of phone calls with them to understand the business but we know insurance companies are not good buyers of software, in the sense that they’re really conservative and they seem to always have other priorities. So, we introduced the company to about four or five insurance relationships and one of our investors is actually signing up to be a customer, which was a really positive data point. But the other four thought, “we have our own underwriting system. We think it basically does the job. We can see advantages with this software, but we’re not prepared to rip and replace,” or, “we have other priorities at the moment, we’ll maybe return and look at that at some other point. We just moved our whole IP stack and spent $30 million for that transition to Guidewire’s platform.” These are examples of all the excuses that you get and we thought, “this is solving a problem but maybe this problem is just not viewed as big enough to the industry players.” You have to be careful of that in InsurTech as an investor, because you will get people, really smart people, who know the insurance industry, who will develop a point solution for a problem and it will solve that problem, but maybe that problem is just not big enough in the context. You have to be cautious about investing in what are point solutions where it’s always going to be a small business because the total addressable market is just not large enough.

VN: You mentioned the team earlier and the importance of that. What do you look for in that team when you want to invest? What are some of the characteristics of those founders or the CEO that makes you want to put money into them?

KM: He or she has to be very good at leadership, in management, and bringing in really good people, and then giving them space to develop and contribute. We’re on the board of this company, for example, and we were in a board meeting yesterday. I just see the way he operates, where he’s always putting his people up in the board meetings to speak at different points. And you get through a board meeting, and you think, “the CEO actually didn’t spend that much time speaking, it was mainly his team.” I really admire that; this company just got voted on a Forbes survey as one of the top 10 rated best employers in the country. It’s the leadership and just management skills and giving people space. Secondly, it’s understanding that raising capital is a really important part of the job. This individual is almost always thinking about the market and about capital and he spends his time every week speaking to investors, not because he’s raising capital, but just because he knows that, every two years or so, he will raise capital. And so, he maintains a Rolodex of investors where he just thinks, “every week, I need to spend a couple of hours speaking to potential investors to develop these relationships.” It’s really important that founders understand that raising capital is critical to their jobs and, as a result, it’s a criteria that we really focus on. 

One of our portfolio companies needed to pivot after about 18 months because it was not seeing the product market fit. This CEO had the charisma and the presence and the investor relationships so that he was able to raise quite a lot of money, $40 or $50 million, going into a pivot. With another founder, investors would probably say, “Let’s talk after your pivot so I can see whether it’s actually going to work or not.” That’s the difference between an outstanding founder who, in addition to that leadership and management skills, comes at it with that X factor in being able to raise capital. It gives a new lease on life for the company.

VN: Talk to me a bit about your differentiation, starting with your LPs. You said that they’re the insurance companies, so that’s pretty unique. A lot of LPs are fund of funds or pension funds and that kind of thing. So, what do you offer them?

KM: We offer them strategic insight. Insurance companies do not really invest in venture capital; they’ll invest in private equity and buyout funds that offer more of a current return but, from an investment standpoint, the risk in venture is too high and their dividends for the life of the fund are all predicated on capital gains. And so, there are a number of reasons why they’re not, as a group, major investors in the venture capital asset class. We have managed to get these investors into a venture capital fund, primarily because, and I’m being candid with you, they want the strategic insight. They also want the financial return but, primarily, the first hook is the strategic insight. And we have a whole communication program with our investors that a generalist VC just doesn’t have. It wouldn’t make sense and they might not be prepared to devote the time to it. But, for example, every month we have calls with our investors, and individually we go through the deal flow, talk about businesses that have strategic interest for them, where they might want an introduction, and a lot of them want to become a customer. We organize demo days for them in a particular area; let’s say it’s insurance claims, so we invite in five or six claims startups on a Zoom call, we’ll have dozens of people on it, and they present for seven or eight minutes each and we get it done in an hour. Our investors love that. 

We do deep dives for them on certain topics that they’re really interested in. It might be an area of their operations that they’re working on and trying to improve, so they’re looking for technology ideas and what’s out there in the market. We’ll provide them with a detailed report about what we’re seeing in the market. We’re not guided by the technology itself, but where we’re allowed to speak to validate that technology is where the investment dollars are going. The investment dollars will follow those companies that are demonstrating product market fit, so there’s perfect alignment here. And that’s an important point, because you want to be careful about not following the next shiny thing that people are talking about, like blockchain insurance; blockchain insurance hasn’t done anything, it’s gone nowhere for three years. We’ve seen very few companies doing it. But if you started out with, “we like this thesis that blockchain will be in insurance,” and ignored what was going on in the market, you probably wouldn’t be in a good place right now. So, we feel pretty good that when we do these deep dive reports for our investors, since they’re all predicated on, “This is not just MTech Capital’s views on what we think is an interesting technology,” but, rather, “this is where we see investment dollars going.”

VN: What’s your differentiation for entrepreneurs? The really good companies have a wide range of options of firms that they can take money from, so what do you say to those companies to convince them that you’re a good partner?

KM: This is maybe our greatest strength, actually: we get into deals that we have no right getting into for a fund of our size. We now have 23 companies in our portfolio and we were involved in a couple of Series B financings that were $35 or $40 million. They were closed, and we spoke to the founder, and we told him about MTech Capital, how we think we can be supportive to their business and how, if their business is serving insurance companies, we can help with a lot of introductions there. Frankly, we come at this with a combination of corporate finance and insurance industry experience, and that’s a rarity in terms of the investors that you meet. And so, they tend to like our combination of experience, and they can see how we can help grow their business. 

Out of 23 companies, we are on the board of 19 as a director or an observer, and, again, you probably have no right to be on the board as an observer when you’re putting in, say, $1 million or $3 million on a $40 million dollar financing but that’s what has happened. That’s the way we differentiate ourselves to entrepreneurs: we think we bring quite a bit more than just more money.

VN: Do you want to talk to me about a couple of those companies? Maybe highlight two or three of them and tell me what was trading about those companies and why you wanted to invest.

KM: Because I had mentioned embedded insurance earlier on as an example, I’ll mention Matic. They distribute homeowners insurance through mortgage servicing companies. So, you have your mortgage, and every month you have to speak to them or write a check or do a bank transfer; Matic has gone into these mortgage servicing companies and said, “Listen, we have a proposition where we will pay you a commission to resell. You share with us the information on the home, and the homeowner, and their history on paying the mortgage, and we can do real-time underwriting.” It’s not them doing the underwriting; they’ve assembled a panel of insurance companies they work with, and so they can provide that real-time underwriting experience to a homeowner either when they’re on the phone paying their mortgage, or they send them something in advance saying, “Your insurance is going to expire in three weeks, you need to renew.” For us, embedded insurance is defined as where, on the insurance sell, you’re taking advantage of pre-existing information on that risk and on the customer so that you don’t have to ask them a whole series of questions. Ideally, you ask them nothing. And so, obviously, for the mortgage servicer, you’ve got all the necessary information on the house and the individual, so it’s a very seamless customer experience. That company is a good sample of the embedded insurance concept. 

Another one would be CyberCube because when we think about insurance, generally, whether it’s PNC insurance or life, the industry is not growing much. It’s mature in the US, and it’s mature across Europe, so if we just focus on the PNC industry, its grows annually by about the rate of inflation. Whenever an insurer can find an opportunity for a new line of business that’s high growth, that’s exciting, cyber insurance is that line of business. It is going to be, by some accounts, including ours, the single biggest line of insurance in probably 10 years time; larger than auto, larger than property because it touches just so many areas. CyberCube is a company that provides data analytics to insurance companies, so they can better analyze cyber insurance risk and, therefore, price it with more accuracy. Insurance companies, for hundreds of years, have relied on claims history to write insurance. When they set prices, and they make underwriting decisions at prices, they’re using historical claims. If it’s a new line of business, like cyber, there are no historical claims, or are very few. And so, you need to be accessing additional data and looking for correlations that insurance companies don’t have. So, they developed this data business that provides cyber insurance data analytics. We think they are positioned, over time, to be the leading provider of this service data analytics to the insurance industry, in a line of business that’s growing exponentially.

VN: What are some of the lessons that you’ve learned in your career as a VC?

KM: I guess one that comes to mind is beware of the hyper articulate founder. If they come in and they communicate so well and they’re so articulate, and, “this is the problem I’m solving,” and they know all of the points that a VC wants to hear, and they just serve it right up, you have to be really cautious there. Sometimes it can be that the other founder, even though he may not have that hyper articulariculate gene, but we think there’s just real substance there that they’re bringing to bear on a problem. We put a high value on that. It really does inform the way we think about investing: we’re really rigorous on due diligence, we like to get to know people. And so, where it’s an instinct, or a cautionary instinct, that I’ve learned over time. That first or second meeting can go really well and then start to learn more and see actually there are issues.

VN: What’s the part of the job that you really love the most? When you go to work every day as a venture capitalist, what’s the thing that really motivates you to do this?

KM: I mean, undoubtedly, it’s meeting new founders every day who are passionate, who have a lot at stake. We’re like a startup company too. I mean, we have to go out and raise a fund. We know how hard it is and everything’s to play for at those moments. We respect founders who are taking these risks. We respect the fact that they’re talking with us, and we love to hear the stories. We learn a lot every day. So, I find that really refreshing. People who just have an idea and have the ambition and vision to go out and pursue it and take those risks, I find that very encouraging.

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