Bank FinTech Showcase Interview Series

Robert Albertson: Joining us is Shayne Skaff, Co-founder and CEO of Blooma. Also joining this interview is Carson Lappetito, CEO of Sunwest Bank a $2.2B asset bank based in Irvine, CA operating across four Western states.

RA: Shayne – Let’s start with you. Thank you for this opportunity. Let’s begin with your background and how you came to develop Blooma. Where did you begin your career, and why did you wind up in the commercial real estate space?

Shayne Skaff: Thanks for having me. I’m a career tech guy. So, I’ve spent most of my career building AI and ML platforms. My last company was a company called Maintenance Net. We automated how companies manage and transacted annuities. Mainly selling into kind of the service contract renewal and software license renewals space. I didn’t play in or really wasn’t around the lending or banking industry at all until January of 2018. My co-founder, a gentleman by the name of Mike Persall, is the chairman and founder of a bank called ABP Capital, out of Encinitas, California.

Mike and I have been friends for a number of years. In January of ’18 we began looking for insight into what was going on in and around the commercial lending space from a tech perspective. I had not been tracking that space. Over a series of conversations he got me interested enough to dig in a little bit. He was looking for a solution for his own banking needs. He wanted to automate how they originated loans and went through pre-flight within the bank on their commercial real estate loans, as well as automate how they monitor their current loan portfolio.

I looked at the solutions out there in the marketplace where there are gating factors that got me into this. When I looked at what was happening on the residential side, it seemed that commercial lending was at least 10 years behind from an innovation perspective. Yet, looking at those customers that were buying innovation on the residential side, they had massive commercial businesses that were just largely going unaddressed. That, for a technology sales guy, was huge. It told me there was just a lack of product in the marketplace. In June of 2018, Mike and I launched Blooma.

I brought over some of my old team members from my last company, specifically on the product side. Mike gave us access to all of their underwriters within the bank. We mapped out all of the manual processes that went into originating new loans. Then I asked my product and development team what can the machine do here to move these individuals to the art of their job? That was the summer of 2018. We started to build the product.

RA: You hit the bull’s eye in terms of banking, because commercial real estate is usually the largest asset class in any community or regional bank. Can you take a moment and describe the Blooma platform suite and make the case for data-driven analysis and decision making in the underwriting exercise? And then attaching an impressive dashboard for quality and performance indicators, all while reducing the paperwork stream and ultimately unnecessary labor costs? How well has this been working in real life since three years ago, compared to the more labor intensive model?

SS: When you look at the space, what you generally have everywhere from the national, even global banks down to the community banks, is lots of point solutions. You’ve got solutions that do OCR, you’ve got cash flow analyzer solutions, you’ve got your Excel spreadsheet models, you’ve got third party databases, like CoStar and REIS and such. None of them are really connected. What you end up having are humans bringing all of these disparate systems together, which really causes the massive time sink. We’ve bundled up all of these point solutions into our Blooma platform, our machine.

On the front end Blooma allows commercial banks, and really any CRE lender, the ability to actually create lending profiles. I can create profiles on Blooma like a Fannie profile, a Freddie profile, or maybe a regional lending profile. From aggressive to conservative. Those profiles have thousands of attributes that make up a lending profile, but most of our customers really only look at 10 to 15 items that really move their

lending decision. They can tell the machine basically what’s important to the bank within those profiles.

The machine learns what’s important to that lender.

On the side of OCR, we can read offer memorandums, broker packages, P&L unit mix, rent roll and the like so the machine can automate the way they’re digitized. We’re connected to a number of third-party databases, where we’re actually pulling in things like rent comparable sales, environmental data and tax assessor data. That’s all coming into the platform. We’re doing all of the cash flow analysis, all of the valuations in Blooma. On the borrower/guarantor side, we have the ability to read tax returns, K-1s, bank statements, do all the spreading.

At the end of that process, we have the capability of providing a lending score, which tells our client the probability that the deal meets one or multiple profiles which they’ve set up in the system. They can go in and interact with that analysis. Even if they’ve got an LOS system in place we can actually deliver all of that data back into a loan origination system that they’re using or even more importantly, Excel models. We can actually populate their own Excel models with the resulting analysis that we come up with within Blooma. For our customers, they experienced anywhere from an 80% to 85% reduction in man hours on the pre-flight side. More importantly, what they get is the increased production on the business side. Now the client can do a lot more with the same workforce that they have in place because they now have a machine that can help reduce those man hours.

RA: You’ve developed a pretty broad-blanket, technological solution for data analysis. I think a lot of real estate professionals are concerned with the paucity of quantitatively comparable information that could be very useful for the lending decision. Why do you think this lack of data developed in the first place? Analysts in the industry often wail about the repeatability of real estate credit loss cycles that seem to only become obvious well after the fact. You seem to have addressed this.

SS: We certainly are trying. The data side is probably the most complex piece of this. There are a number of different data systems out there. I would say that some of them match up and some of them don’t. So the complexity is in how you gather all of this data, how you normalize that data, and how you bring together a better, more complete data set that you can work off of. That’s where I’ve spent my career, in a very nerdy place.

These are the things that we know how to do really, really well. We look to build up our data library, and find the best pieces of data in very specific places. For instance, via asset class. There are some third party data systems that have better information on say, multifamily, than other third party data systems. We go looking for and finding the best-in-class data in very specific spaces. So, when it comes together in Blooma, our customers are experiencing a very complete and accurate data set that they can work from. When you get better rent comparables and better sales comparables, you get better valuations. That’s really what we tried to produce out of Blooma.

RA: I take it you can do this pretty much agnostic to geography. Are there any restrictions?

SS: Only by geography within the US. We are US only today. We certainly have plans to move out but right now we’re US only.

RA: So, is Blooma’s secret sauce basically the ability for artificial intelligence to synthesize various sources of data into hard, usable data for underwriting decisions?

SS: I’d say there are a couple of pieces where we have extreme IP on the AI side. We’re not using AI to make decisions for people. We’re really using AI in two main places. One is in the parsing of data. Two, is when users are interacting with things like rent comparables, sales comparables. They’re actually training the system on what’s important for each of those asset classes that they’re looking at. On the data side, as we talked about, how we aggregate and bring this data together, and normalize it, we’re doing some really interesting things there. We have a product called Blooma Central, where all of that data structure is coming together. I’d say that piece is as impactful and as important as what we’re doing on the artificial intelligence side.

RA: Artificial Intelligence is increasingly ubiquitous in FinTech, from what I can see. Yet, the artificial intelligence concept is probably not comfortably or sufficiently understood by your average banker. Can you take us into the machine learning weeds for an example, perhaps, that bears on your applications?

SS: The uncomfortable feeling that people everywhere – not just in banking – have with artificial intelligence or machine learning, is really around the replacement of people. We don’t look at that at all when we build or when I have ever been involved with building out an AI platform. It’s really about moving people to the art of their job and actually making their job better. Utilizing the machine to do a lot of the monotonous boring work that a person doesn’t need to do, and really move them to the place where people are needed. One specific example would be around new construction lending: There’s more nuance in new construction lending than in some other asset classes, like vanilla multifamily loans. Where we can actually provide freedom for people to move to the art of their jobs is needed within underwriting and analytics. That’s really what our goal is. It’s about providing results that a person could use to make quicker, better decisions off of, instead of having the machine make the decision for them.

RA: Tell us what your client list looks like at this juncture. Is there a range by size or geography or type? Do you have pilot programs for the larger banks in the system? How many clients are on the Blooma platform now?

SS: We have a dozen clients on the platform at present and they range anywhere from the $1B to upwards of $10B. I’d say most of them are between $3 to $10 billion in CRE AUM at this point.

RA: Any pushback when you’re trying to put this together? In your view is there a typical profile of a bank to make the most of the Blooma platform or is it pretty broad?

SS: I wouldn’t even consider any feedback to be pushback. We’ve gotten such an amazing positive response. I’d say that really the time it takes to get to a yes is primarily because there’s so much involved

with vendor onboarding. This is more on the commercial bank side. On the private bank side there’s less of that that we have to go through. So, I’d say that our hurdle on the commercial banking side is really regulation and going through the vendor onboarding process. Going back to where the art of lending lies. There’s a lot more art on the $100M+ loans. I’d say our sweet spot sits between the $5M to $100M mark. I say that only because that’s most of what we have in our system today. It seems to be where we’re seeing the most activity within the Blooma platform.

RA: I assume you’ve had contact with bank regulators. What are their attitudes about your platform and their general receptivity? Have you brought them into the tent?

SS: It’s a great question. We haven’t had direct contact with bank regulators. Through the clients there’s been that contact, but not directly with Blooma. We’ve had no pushback so far. We’ve moved through vendor onboarding, in which case there’ve been regulators that have seen what we’re doing. We’ve had no real obstacles that have held us back from onboarding a customer to date.

RA: How do you price Blooma to the client?

SS: Today, our pricing is based on the AUM that’s actually flowing through our system. So, it’s not user based. We want as many users in our system as possible within our client base. It’s a fixed subscription fee, based on AUM. It’s pretty simple.

RA: Let’s move to the profit and loss impact. I know you say there’s about 80%+ reduction in costs. What does that do for a CRE-oriented bank’s bottom line? Can you turn it into dollars or some sort of impact on profitability?

SS: That’s probably a better question for Carson. From an ROI perspective the 80 to 85% is on man hours. I think because our costs in general come in somewhere around a basis point on AUM, we’re fairly inexpensive for the value that we’re providing our client. We’ve had no real pushback on our pricing model. From a bottom-line perspective, we’ve heard less, and again, Carson, it’d be great to get your comments on this. We’ve heard less interest on the cost take out and way more interest on the production increase. They’re not letting people go by using Blooma. They’re just doing a whole lot more with less. Carson does that resonate with you?

Carson Lappetito: Absolutely, I can add on two ways. One being on the loan origination side, I think about it the same way I think about my salesforce cost. It’s very easy to do the math of what the Blooma platform is costing you and put it into the spread you’re originating off a loan. You divide the cost of Blooma divided in by your 2% spread, dropping to the pretax income line item. You say am I going to drive, incrementally, that many more loans, by having this platform? When you do that math on a platform like Blooma, that increases your efficiency and your ability to deliver term sheets and lending at speed, which is ultimately how we differentiate ourselves in the relationship and the commercial real estate lending business. It’s a no brainer decision.

RA: Thank you, Carson. Tell us how long you’ve been on the platform and what was it like to implement it? How are the results versus your initial expectations? What’s different now?

CL: I’ll caveat this answer with I’m different than most bankers. I have approached Blooma a bit differently than a lot of other bankers. My answer is not indicative of all of Shayne’s clients. I was very focused on leveraging Blooma as a portfolio management tool, less so on the front end origination to start. We’re running our pre-flight through that process, in order to get to a quicker decision on putting a term sheet out to a client.

We’ve been engaged for just about three months now. We have our entire portfolio loaded up. A big chunk of our rent rolls, a big chunk of our appraisals have been OCR’d through their AI into the platform. Basically, we can look at our entire loan portfolio with real time comps. With real time cap rates from transactions generating real time values of what the portfolio looks like, both as a trend line from what the appraisal was when we originated it. What is the Blooma value today based on their public comps? Especially when we load in our rent rolls. Then we have a rating score of how we feel about the current rating compared to where we originated it. How does it stack in our aggregate portfolio? Should we be spending more time on this credit or less time, depending on how that ranking looks.

What we’re really driving here is the fact that banks, historically, go and look at how their portfolio performs on two occasions: one during the annual review, or two, when a payment is missed. They don’t do any portfolio management in between. In real estate, usually, you have a one-time check on a real estate loan. If you go back to the real estate world. You really aren’t tracking what’s happening in that local economy, that intersection, etc. on an interim basis between your annual reviews, and so especially in an environment like we’re in right now where we’re going through COVID, different asset classes are performing differently. Traffic patterns have changed.

You don’t necessarily know what the exact per square foot rental comp is happening on that corner, that sub market of the city. Blooma is pulling that data real time and telling you where you need to focus. So, for us, it puts us in the catbird seat of looking at our portfolio and saying, “What are we very comfortable with?”, “What are the emerging trends we have to be aware of?” It allows us to be really proactive in managing the portfolio as opposed to being reactive, which is how almost the entire banking industry manages their loan portfolio.

RA: So, is it fair to say that you feel a lot more comfortable with your credit exposure and your portfolio than you did before? Is it essentially no longer an annual review issue? It’s a real time, ongoing review.

CL: Yes, I feel a lot more comfortable. I’m an incredibly conservative underwriter. I would say the proper term would be that I feel a lot more knowledgeable. Therefore, the deals I feel uncomfortable about I’m on top of and dealing with, and the deals I feel comfortable about my team’s not spending time on. They’re spending time either managing the deals we’re worried about, or finding new business, which is ultimately how you pay for the whole platform.

RA: Okay, a question for you both. What is Blooma’s differentiation to the competition? Carson, you go first please. Why did you pick Blooma, and what advice or argument would you give to prospective clients?

CL: I don’t know of another platform in the space that is doing this. So, the decision really is, am I going to do it the way I’ve always done it, or am I going to implement Blooma? I think we’re on the edge of more companies and more technology entering our banking space to help make our whole industry more efficient. Today, if you want to do what I’m talking about here when Shayne was talking about either on the front end, or the portfolio management side, there are no other options.

SS: I’ll just add to that, Robert. We’re the only ones actually taking all of these point solutions out there and bringing them together. I’d say if you take any of those point solutions, there are all kinds of competition going on within them. I would say we’re not really competing against any of them, even the third-party data providers that we don’t have contracts with. I don’t look at them as competition. The reality is we may decide to make them part of our library six months from now because we like the data that they have. Right now, we’re in a pretty good position from a competitive perspective. There’s a lot of big players out there. I generally am looking at what they’re doing. It’s mostly the guy or the girl that’s sitting in a dorm room right now who’s building new crazy products that are going to come up that we’re going to see. I think we’re going to see a lot happening over the next couple of years as PropTech is really moving quickly now. Since 2018, when we started Blooma, I’m starting to see a lot more activity happening within the PropTech space specific to commercial.

RA: So, is it fair to say you’re unique at the moment? You’re the full package?

SS: Yes, I’d say so. I haven’t seen somebody that we’re competing head to head with that has the same solution as Blooma. There’s lots of companies that have pieces of our solution, but we’re kind of packaging up lots of these point solutions in a very unique way. I’ll add it’s not just the product build itself, but also how we’re implementing the product within our customer base. We recognized really early on the complexity of the commercial banks and the core systems that they had in place. We are very sensitive to not having to forklift out one system to put in our system. What we’ve done is very unique.

We’ve built a very flexible platform that can be integrated with, for instance, nCino, or any other LOS system that can feed your Excel model that you might want. Again, the results of our data are going into some other system that the bank might have. On the preflight side you can literally access our system in a few days. We can have a system up and running for a bank in a few days. They could be going in and loading OMs and actually doing deals in our system with very little effort and no implementation. The only time sink that we have is if we’re integrating into a system that the bank has that we haven’t seen before. Generally, if there’s an open API, it’s a very quick build to get us connected.

RA: You’re basically core agnostic?

SS: Yes.

RA: Let’s move over to the auditing and monitoring function of Blooma. How does that work?

SS: The portfolio monitoring? So basically, I walked you through the whole analysis when we’re doing the initial cash flow analysis and the valuation of that asset. When we consume a customer’s portfolio, we’re literally doing that every single day on every asset in that portfolio. What does that mean? Does it mean that they’re going to see a change in the lending score every day? No, because the times when there are changes are generally when there’s new data to be presented to the machine for the analysis. If that data changes, like an updated rent roll, if it changes to the positive, then it’s generally going to move the lending score up. If it changes to the negative, it’s going to move the lending score down. If there’s some dramatic increase in vacancy rates in a specific region and it impacts some of the assets that a bank might have there, then that will drive the lending score down. That’s happening every day. Our system is looking for the changes that might exist with that asset, or if they upload new financials for the borrower that could also have an impact on the monitoring of that specific loan.

RA: Carson, you’re unique as well, in that you cover a very large swath of geography in the west with a very thin branch network. Are there any examples so far of how this function has been turning up things that you appreciate in terms of warning signs or something you might otherwise have missed?

CL: There’ve been a lot of great examples. The first exercise our team did was a COVID analysis on all of our loans. We ranked them by risk level and in various tiers. We took that and compared it to the risk scoring that came out of Blooma. We looked at the differences and asked “Why?”. There were a number of loans on that list that we thought were better than they actually were. We’re very strong underwriters, but the goal is to figure out what you don’t know so you can solve it, and there were a handful of loans that had higher LTV than we had thought based on rental rate compression in their market. As a result, they’re a bit higher risk than you initially anticipated. Then you can adjust accordingly when you have your covenant testing periods.

You want to make sure you’re on top of those from a portfolio management perspective. Banking’s a risk- based model. You can’t be a hawkeye on everything when you have a big portfolio. You have to make sure you’re on top of everything that’s got a little more risk to it. Just the fact that it is teasing out something that you should be looking at and directing an RM towards is very valuable. Helping that RM focus on the art of their job.

RA: I will compliment you on your speed of implementation. It is unusually short for within the FinTech arena, which I think is quite impressive.

Let’s finish off with a question to Shayne on scope and scalability of your organization. He recently received an injection of capital from Canapi. How comfortable are you with your funding to date and your scalability going forward? Specifically, where do you think your client base will be in another year or two?

SS: Very comfortable with the capital that we have right now. We have a very strong balance sheet and will need it to do the things that I’m forecasting to do over the next 18 to 24 months. We’re using that

capital to bring on a sales and marketing organization. We’ve been heavily driven by R&D and product. Bringing on sales and marketing is something that I started doing at the beginning of this year. We brought in a head of sales, we brought in a head of marketing, we brought in sales directors, we’ve increased our customer experience (onboarding) team. They’re the ones that bring our customers on and train them.

It’s a pretty easy onboarding because we’ve got a platform that’s fairly intuitive. Users only really require an hour or two of training. The Canapi guys have been phenomenal to us. I believe in raising capital where capital is the least of what you get. It’s really the intellectual knowledge and the help on the other side. That’s when you know you’ve got a great partner, and that’s what we’ve got with Canapi. They’ve given us great introductions. Not just into clients, but into resources which are extremely important for a company of our size.

We’re looking to basically triple the quantity of customers over the next 12 months. I feel very, very good about doing that. I’ve got a great operator in place that understands what we need to get to certain places. He was my COO at my last company. So, we’ve been together a long time. I’ve also operated with my CTO before. This is the team that I have in place – the core team that has been here since the beginning. We’ve all actually operated together and that makes for much easier momentum. I tell them what we’re doing from a sales and marketing perspective. They know what I need from a product and R&D perspective to make that happen.

RA: It sounds to me like the Blooma story has a lot of impact, and Canapi is a great partner. They’re best in class in terms of industry knowledge and connections.

Have I missed anything, anything either of you want to mention?

SS: I think you’ve done a great job, Robert, of hitting some key points that I would want to put out there. I want to thank Carson for his time for being a part of this. It’s always wonderful to have a client doing these interviews with me. So, thanks, Carson for spending the time especially what it sounds like in a very crowded environment that you’re in out there.

CL: Happy Shane brought a product like this to market. The more innovation we can drive in our industry, the better. I’m a happy client, and thanks for including me in the interview.

SS: I’ll just say, Robert, for those banks that are going to view this, the process is probably abnormal for them. I think this specific space has been stuck in a software environment that has been a super heavy lift for many decades. This is not that. That’s very important for them to understand. Initially, when we come in and start talking to a bank they don’t understand until we get through our meeting and they actually understand that this is a technology that’s built in a very different way than the system that they’re running on today. I look forward to talking to a lot of those banks over the next year.

RA: This has been a very helpful, thoughtful interview. I appreciate your time. Both of you. Shayne, if a financial institution reading this would like more information or maybe connect with you, would you be willing to provide your phone and email coordinates here?

SS: Yes. You can email me at ShayneSkaff@blooma.ai. I also can be reached by phone at 858-442-3696. I am happy to talk to anybody interested.

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