Financial R&R: The Benefits of Data and Analytics to Mitigate Risk

Intro (00:01):
Welcome to Financial R&R, a show dedicated to financial insurance and risk management solutions and trends shaping the market today. Here are your hosts, Ron Borys and Ryan Farnsworth.

Ron Borys (00:13):
Welcome back, everyone. This is Ron Borys with the Financial Institutions Practice at Alliant. With me today are Ryan Farnsworth and Kevin Habash and we're talking data and analytics. Kevin leads Specialty’s Data and Analytics platform. We've been working with Kevin now for a while, just building out our platform and we know how important data and analytics are to the world we live in today with regards to how people are looking at risk and figuring out susceptibility to catastrophic losses, how much limits should people be considering buying. Historically, we've always used benchmarking. Data and benchmarking is still a very valuable tool that we use every day, but data and analytics is really more tailored towards individual clients, industry, sector, individual risk profile, be it revenues market cap. So, we're thrilled to have Kevin as part of our Alliant team and certainly really happy. Thanks for joining us today, Kevin.

Kevin Habash (01:03):
Thank you, Ron. Glad to be with you.

Ron Borys (01:05):
Well, why don't we get right into it, Kevin? I mean, you've been doing this for a long time. You have a Ph.D. in economics, certainly a brilliant mind when it comes to helping us look at all this data and how do we crunch it and make it come out in an output that's useful for our clients. And maybe you can just give a little background about how you got started in data and analytics and some of the work that you've been doing for us since you joined Alliant.

Kevin Habash (01:28):
Again, as you said, I have a Ph.D. in economics from Purdue with over 30 years, really in designing, developing, running, and presenting, actually a wide variety of risk quantification, insurance optimization and customizing the analytics, really, for our clients and prospects. Also, as you indicated, prior to my full employment really was aligned earlier this year, I've been, or I was a consultant with Alliant for over one and a half years doing the same thing really, I've been doing for the past also 25 to 30 years. The transition really to full employment was a natural progression and evolution of our relationship and long-term relationships, so to speak.

Ron Borys (02:13):
Listen, investments are important, obviously you've been instrumental in helping us build out this platform. Certainly, you and I have dealt with analytics in our prior lives. And obviously having Kevin here to work with you and I sort of in connection with helping, not just financial institutions, but even the broader world of specialty has been a welcome addition to the firm.

Ryan Farnsworth (02:35):
Absolutely, and Kevin, I think it's important for us to understand what is our approach within this Specialty analytics group at Alliant with respect to analytics? What are we doing now to help our clients?

Kevin Habash (02:51):
Well, basically we're shifting, really, the paradigm of buying commercial insurance, from a transactional process to more of a capital efficiency framework. Again, depending on the underlying risk, whether it's a P&C, professional or management liability, our clients and prospects, they want to know, how often they break even with the carriers, so to speak early. I mean, that boils down to that particular level, am I winning the game or not? So again, it's better protection of our client’s balance sheets, so to speak. So, understanding of the volatility of the underlying risk they're facing every day in their portfolio. And most importantly, it would be an objective and transparent decision support for our clients and prospects going to their board, or to the C-suite, if you will, would be extremely objective and transparent process.

Ryan Farnsworth (03:46):
It's interesting that you mentioned that process because we really didn't jump to limits and specifics about coverages. It's all focused on risk, and as we've talked about, on this podcast and others at Alliant, how we help our clients to find a more rewarding way to manage risk, we are trying to use analytics to drive decision making as part of that process and helping our clients understand why they should prioritize analytics is another mission that we have. Maybe, Kevin, speak a little bit to why you think the firm should prioritize that approach as part of their insurance procurement process.

Kevin Habash (04:26):
Ryan, it's extremely important to our clients and prospects to understand the underlying risk, and we want to bring them away or take them away really from the benchmark, as Ron was saying, and rely more on inward-looking, particularly our risk characteristic, whether it's again, professional management liability or anything in between, and to do so, we need to understand the current dynamic of the industry, whether it's a financial institution or otherwise, regardless, what the underlying risk. More importantly, we need to quantify the risk. The levels are moving at everything. If you will, again, we want to tie it up also to the risk appetite, as well as risk tolerance for the organization. That's how you protect the balance sheet. And when we identify all of those parameters, we can determine again how to quantify the risk and to move on to the next level of the process, which is overlaying different program structures with respect to retention, with respect to limit, with respect to all premium underlying premiums for those particular risks. And that could be on a standalone basis or on a blended basis, specifically with the FI,  what we see nowadays, given the market condition.

Ron Borys (05:41):
Yeah. I've really enjoyed using analytics, not only here, but in my prior lives. In the industry, unfortunately, many times people don't see the real value of insurance, unless they've had a claim, right? Everybody thinks it's just an expense, but you know, the modeling and the simulations that you're able to perform, Kevin, not only give people a good idea of the magnitude of the risk that they might have but, you know, the value that insurance might play over a period of time, right? I mean, you know, the reality of it is we're looking at this through the same lens that the underwriters are looking at it, right? So, this tool gives us a lot of very valuable ammunition, for lack of a better term, to really look at risk the same way the underwriters are looking at it, help our clients understand why underwriters are taking certain positions that they're taking, and then maybe even use the data in our negotiations to maximize the value of insurance for our clients.

Kevin Habash (06:34):
Of course, Ron, I mean, this is exactly what we do at the end of the day. Really, what is the value of the current program, structured basically against the out-of-pocket cost, which is the premium really, and how often you're going to break even with the carrier, so to speak? So, I mean, this is extremely important given, again, the pressure on the market assessing higher premiums than anything we've witnessed in the past few years, especially on the management liability side.

Ryan Farnsworth (07:00):
The reality is when clients are needing to focus even more on their retention structure, the pricing for the limits that they buy, whether they should consider additional limits, it's uber important to focus on their own risks and their own appetite. And we sometimes use data and analytics in one specific phrase, meaning one thing, but they really are two things, right? The first of it is trying to aggregate and understand the data and then analyze that and make decisions based upon that data and based upon the information that we have and the way that the market is changing. As you say, Kevin, the data is changing all the time for our clients. And as we focus on the risks and their approach, you know, going away from the traditional methods of assessing coverage limits just are not adequate.

Ryan Farnsworth (07:54):
Our clients are focused on making sure that the data-driven nature of how they evaluate their investments or running their day-to-day business is in line with how they also buy insurance. And one of the great examples of that is the asset management D&O model, as well as a few of the other models that you're building that we can speak to as well down the road. But today we wanted to focus exclusively on the asset management D&O model and how we've built that data of five plus years with 260 claims being analyzed, you know, close to 2.5 billion in losses. And how do we use that, that data now to help our clients make decisions specifically within the asset management sector, where they are focused every day on gathering as much data as they possibly can and helping them make decisions within their investment portfolios. Why wouldn't they also have that same approach when it comes to assessing the need for and the scope of their insurance? And the asset management D&O model that we've built together with you does exactly that.

Ryan Farnsworth (09:02):
Maybe speak to that a little bit, Kevin, about our approach for the asset management D&O model and how it benefits our asset management clients.

Kevin Habash (09:10):
Absolutely. Ryan, I mean the asset management D&O model though, that we basically worked on exclusively for the past, at least a year and a half to two years, since we were able to gather all the benchmark circle D&O losses. It’s extremely important for the FI industry and the model focuses on few things, first of all, it segregates losses or historical losses based on AUM band or asset vendor management bands the firm type, whether it's a bank, asset management, private equity or hedge fund or anything in between. Also the investment type, what type of investment the companies focuses on like being a multi-strategy credit or commodities or fund of funds and so on and so forth. So again, the model is extremely comprehensive, if you will. It assesses or looks at a broad segment of the FI industry and based on these historical losses, which we do is we use that particular database and we scale it to a specific client, then based on their AUM, based on their investment type, based on the firm type, if you will. And we use that again to determine or to quantify the risk of the D&O given all the characteristics of the firm that we're dealing with. We scale those losses to the firm characteristics, and we quantify the risk for them. The rest is easy, from the risk modeling. It’s how you overlay again, different program structures with respect, limits, retention, and premium to determine the optimal program structure that the company would be interested in knowing, given the market condition.

Ron Borys (10:49):
We're in a very fortunate position. You know, our firm has been recognized as a leader when it comes to serving the asset management industry for many, many years. As a result of representing some of the largest asset managers in the world, both, registered and unregistered, we just have a plethora of data that we're able to use. And I think while we've always shared our data, I think in a great way, I think having you and being able to take the modeling and some of the work that you do, sort of behind the scenes, and not only take it and use it but put it in a user-friendly output so that people can understand it. I just think that's the part of it that I've been the most impressed with.

Kevin Habash (11:26):
Thank you. I mean, we're trying to communicate to the client and also the prospect, the most efficient program structure at the end of the day. That's what they're interested in. We're not trying to bring any really sophisticated model in which they're not accustomed to; trying to just hit to the point. What is it for me as a client? We know that as Ryan was talking about, we are acting as an underwriter for the risk, but we are only looking at the single risk, not the portfolio of client and risk, so to speak. So again, then how the markets price everything is based on their portfolio, as well as the market, as well as the client, or basic characteristics. But again, here, we're focusing solely on the clients and their exposure and their characteristics.

Ryan Farnsworth (12:12):
Thanks, Kevin. We really, really appreciate you being available today to talk through a lot of the issues that we're seeing in analytics and how we're utilizing the data that we have and helping our clients make decisions. Identifying the firms and a client's true exposures really goes a long way to helping them arrive on a decision-making process for insurance programs that they buy and that they may want to consider. The modeling tools that you're building, that we're building together, is a big part of that, and we look forward to many discussions with you and our clients going forward and thanks again for your time, Kevin.

Ron Borys (12:49):
Well, Ryan, Kevin, thanks so much. I certainly feel like anyone who's listening in is going to take away a tremendous amount from this analytics discussion and certainly be interested in learning more about ways that we're finding a more rewarding way to manage risk for our clients, using analytics in a challenging market. You've been a tremendous asset, both to our brokers and to our clients, and helping them rationalize sort of the true value of insurance. And for those of you listening, who are interested in learning more about what Alliant is in both in the financial institution space and analytics, please visit our website at www.alliant.com.