Closing a mortgage faster, with Eilon Shalev

The fact that it takes 45 days to close a mortgage in America today is not just a one-time hassle for home buyers, it can be a source of ongoing compounding costs. Nobody wants that, least of all Eilon Shalev my guest today and the co-founder and CEO of Elphi, a fintech that wants to bring that down to fifteen days.

“Elphi is a technology company. We provide enterprise software as a service to mortgage lenders, to help them close loans faster. That's the gist of it.”

You can find Elphi and more on its story at https://www.elphi.io/ (or on LinkedIn at https://www.linkedin.com/company/elphi-inc/ )

Eilon is on LinkedIn, too, though don’t forget to mention the podcast when you reach out (https://www.linkedin.com/in/eilonshalev/)

Eilon also shares his email in this podcast, so listen out for that if you prefer to make direct contact - or find it in the transcript at https://www.howtolendmoneytostrangers.show/episodes/episode-88

We also talk a lot about Eilon’s time at MIT Sloan, if an entrepreneurial MBA is on your 2023 wishlist, learn more here (). Need to fund it, perhaps my guests from episode 22: https://www.howtolendmoneytostrangers.show/episodes/episode-22?

You can learn more about myself, Brendan le Grange, on my LinkedIn page (feel free to connect), my action-adventure novels are on Amazon, some versions even for free, and my work with ConfirmU and our gamified psychometric scores is at https://confirmu.com/ and on episode 24 of this very show https://www.howtolendmoneytostrangers.show/episodes/episode-24

If you have any feedback, questions, or if you would like to participate in the show, please feel free to reach out to me via the contact page on this site.

Regards,

Brendan

The full written transcript, with timestamps, is below:

Eilon Shalev 0:00

Think about it, the idea that it takes 45 days to close a loan is very expensive, we want to take that average down to 15 days.

Brendan Le Grange 0:11

Welcome to How to Lend Money to Strangers with Brendan le Grange. There's been an unpleasant shift in the air around mortgages: rates are up significantly, home prices have lost their momentum and may well fall this year, and all of a sudden, borrowing and lending has become a lot more difficult to do well. Which is one of the reasons why there've been a few mortgage themed episodes on the show recently.

Today, though, we're not talking about consumers, at least not directly. We're talking B2B and how my guest can help lenders to reduce the time it takes them to close on a mortgage. Though, as it turns out, it actually has a very big and very positive impact, not just on the consumer experience, but even on the cost of the underlying loan.

Eilon Shalev, currently you're streamlining the mortgage lifecycle as CEO, and co-founder of Elphi, and at various times in the past, you've been an author, a crowdfunding pioneer, a major in the Israeli Air Force, an intrapreneur, and an MIT graduate.

So welcome to the show. What experiences have shaped you prior to making that big decision of yours to move to Boston to pursue your MBA?

Eilon Shalev 1:35

Well, I spent twelve and a half years in the Isreali Air Force, had a military career, spent years as a super admin of enterprise software, and also spent years as a commanding officer.

I always had a passion for entrepreneurship, I just didn't know exactly how to express it within the military boundaries.

I ended up writing a book, it took me six months to write the first draft. And then afterwards, I was a little bit lucky, because at the time, late 2011 if I remember correctly, crowdfunding was not a thing in Israel but there was an Israeli startup doing crowdfunding, they were a crowdfunding platform.

And I was super lucky to get introduced to the people over there and leverage their platform to finance the book. I think that end to end experience convinced me that entrepreneurship is something that I'd like to do.

Brendan Le Grange 2:23

I think one of the things I liked is that it was a novel. As a sort of hobbyist novelist myself, although it's been a long time since I got down to any writing.

But obviously, then from there, you took what would be a big step to move from Israel to Boston, what did you learn from the process of becoming an international student at one of the world's top schools?

Eilon Shalev 2:43

I knew I wanted to be an entrepreneur. was just a military person and I didn't know what I wanted to do, except that I want to be founding whatever it is I'm doing.

I realised that an MBA, specifically at MIT Sloan, would be very beneficial for me to learn about business as well as how to run a company from the ground up, how to found the company with some discipline.

So then there's a whole, you know, get the GMAT done and all that stuff. But afterwards, you know, you need to fund this thing. And I was not ready for that to be as expensive as it is - there's a product, a shelf product in Israel, in Israeli banks, that they give for students who go abroad to study master's degrees, and PhDs, and all that stuff.

And apparently, I didn't know this, but you can negotiate the terms. Again, as a military person, I didn't know nothing about business, I didn't know you can negotiate anything. So we were like 40-something future MBA students, and we grouped up together. One person led the negotiations for us, as a group, he was creating some sort of SLABs (student loan asset backed security) by pulling us together as a group, and reducing the interest rates.

So we were able to get 50 basis points off from whatever it was. And may not sound a lot, but if the interest rate at the time was so low in Israel, so instead of having to pay 2.6%, we all got a 2.1% deal.

When we finally did that, and I went to the States to get the MBA done, I realised that in America, for the same purpose of the loan, for the same amount, same person, the equivalent American version of Eilon, they would take a loan for 6% at the time - relative to 2.1%.

I was just flabbergasted. And I thought this is this is super strange. And that kind of led me to to research that space.

Brendan Le Grange 4:32

And those basis points, when compounded over the life of the student loan, make a big difference between affordability and lifestyle and flexibility post-studying. I mean, we're going to talk soon about your move straight from MIT into entrepreneurship, but oftentimes it is the student loan that can hold somebody back, because suddenly they say, well, I need the salary to start paying the student loan back. Maybe I'll do it in a few years and we know how that all works a few years becomes 10 years becomes 20 years and never really happens.

That leads to the obvious question. Yeah, you took the big expense and the big risk and the big time commitment to move to Boston to pursue the MBA. I guess, in simple terms, was it worth taking on all that risk all that debt? Did it make you a better entrepreneur? Absolutely.

Eilon Shalev 5:18

Entrepreneurship is a profession, a profession that requires close attention to details, and discipline on multiple facets of the business. And if it is justifiable for a person to get an MBA and transition from finance to product management, or transition from product management, to consulting or transition from sales and marketing to something else, then it absolutely is justifiable to get an MBA to mass not to master maybe, but to be a jack of all trades of all of those aspects.

The likelihood of starting a company and being successful is very low already. So arming yourself with education that is tailored specifically for that purpose, that sounds to me like a very good investment.

And I have to say it was it was an immense investment in myself.

I learned business analysis capabilities that I did not have earlier. I learned communications for leaders in a business environment. And finally, I learned about culture, and even some quantitative and qualitative significance of diversity, equity and inclusion.

Definitely worth it. And if someone can afford it, I definitely recommend and specifically, MIT Sloan was an amazing experience for me, even learning about accounting and and I'm actually using that I mean, now January tax returns are up. I've been doing the bookkeeping for the company, and I feel really comfortable doing it.

Brendan Le Grange 6:38

Tell me maybe first, what is Elphi? What is this business that you founded coming out of the MBA? And what was that real world experience like of getting this thing going?

Eilon Shalev 6:48

Elphi is a technology company, software as a service.

We provide enterprise software, to mortgage lenders to help them close loans faster. That's the gist of it.

I will say that MIT Sloan is very action-learning oriented. The motto of the larger MIT is mens et manus, which is mind and hand. So we recognise the importance of the mind and the academic acumen, but also recognise the need to go out there and build with your hands that kept me connected to the real world.

Which leads to the second part of your question, I'll say that, because of that, I felt really comfortable going out to the real world after my MBA. And I was able to leverage a lot of classes and a lot of classmates helping me on group projects to verify and justify the plan moving forward with LP is a executable, B sound, and see a good use of my time in terms of what I wanted to achieve with it.

Brendan Le Grange 7:42

When listeners go to elphi.io your homepage, they'll see, front and centre, so basically the headline of the homepage when you arrive is that you're all about streamlining the mortgage origination process.

And I guess, it's something that's almost just felt inevitable to us in the real world as consumers that if you're going to apply for a mortgage, it's going to take forever to close. And maybe it will get a day or two faster, but it was always going to be this big, slow thing, what's your vision for the space and what could and maybe even should be possible in terms of speeding up their closing process with the help of companies like Elphi.

Eilon Shalev 8:23

We can take a look at 2016 to 2019 - I don't think we should talk too much about 2020, '21, '22, and even 2023, because of the because of the different, different environments - but if we look at the data 2016 to 2019, pretty stable 45 day average to close a loan. And when I say to close a loan, I mean from application to closing, which when you get the money as a borrower is 45 days. In the mortgage industry, an application constitutes six very distinct data points, that's when the clock starts. If the lender doesn't receive those six data points, it is not considered an application. And on closing date, that's when we stop the clock.

So that's 45 days 2016 to 2019. Sometimes, you know a little bit higher, or sometimes a bit lower, but that's the average.

The idea that it takes 45 days to close a mortgage is problematic. For several reasons. One, borrowers sometimes need to get the loan earlier. Otherwise, they're going to lose the house because someone else is going to close and bring the money.

The second thing and that's something that not a lot of people maybe know about - it took me a lot of time to realise - is that the sooner the loan is closed, the easier it is for the lender to sell the loan downstream and get a higher premium for that loan. To unpack that we need to go two and a half steps backwards.

When a borrower goes to a lender, let's say Joe Schmoe goes to ABC mortgage company and they asked for a mortgage, they apply for a mortgage. At some point they need to get a rate, let's just say 4% interest rate on the loan, that 4% came from the following calculation that the lender did: the lender received information from the borrower, plugged it into an automated underwriting system, a AUS, is connected to investors, those who will actually buy the loan afterwards to put that loan in a mortgage backed security and mortgage backed securities a collection of loans that together reduce the risk of forfeiting the entire money.

So that you with the automated underwriting system spins back different results, as well as different price that the investor will pay based on the time that it will take the close the loan and deliver it to the investor. So on a 45 day application to close average versus a 15 day average, we're talking about 30 days difference, those 30 days could become 30 basis points of difference that the investor will pay.

Brendan Le Grange 10:53

And I imagine that's kind of a volatility premium, that they set the price today, but the loan isn't closed for 45 days, they carrying some risk in the market. And hence if you can say, well, this loans gonna appear and be booked much sooner, that narrows that risk for them, and therefore they can offer a bit more.

Eilon Shalev 11:11

It's literally a market risk calculation.

So the investors commit to whatever they commit to that translates into 4%. And that's locked. You know, if we look now at what the Fed funds rate did, and think about it, if you lock the rates 45 day in advance, and then for 45 days later, the market is already at 6% or 7%, you made the deal of a life because this is a 30 year fixed rate mortgage.

So the idea that it takes 45 days to close a loan is very expensive.

And to answer your question about the vision is to take that average down to 15 days. This is a once in a lifetime decision to most borrowers buying a house taking a mortgage. It's not like buying some groceries over Uber Eats or something, it's a serious financial decision that has tremendous effect on people's lives. To assume that should take two or five days or ten days, I think it's a stretch, but 15 days is very reasonable to aim for.

And some lenders actually are able to lend in 15 days average, but it doesn't scale. There's some hacks behind the scenes that they do that do not scale.

So if you're doing $100 million a year, and you want to grow to $200 - $300 million a year and keep your 15 day average, it's impossible to do so at the same costs. But we want to make it viable, we want to make $40 billion lender be able to support the $80 billion demand that they have, to translate that into impact.

Now I am going to use the 2020 or 2021 examples. We're talking about more than $4 trillion were originated in the United States of America. $4 trillion. So you take 30 basis points multiply by $4 trillion, you get $12 billion surplus, this is insane, right?

It's insane.

Brendan Le Grange 12:51

And the thing is, it's not this is not like profit. So this is not a case of 12 billion that someone's losing, they don't want to pay it either. They would rather not pay that 12 billion. So it is just it's free money, free money to be to be put back in the system, which obviously in a time of cost of living crisis is very gratefully received.

And of course, in a time of huge interest rate volatility from the market, I guess it's equally well received to let's, let's not sit around for 45 days waiting for interest rates to change anymore.

So what is involved in the Elphi product suite that helps make this sort of streamlining this false closure of mortgages, a possibility at scale, as you said?

Eilon Shalev 13:27

Very early on in our research, we realised that there's a dearth of innovation specifically in the back office of mortgage lenders.

And to translate that into more real terms, we're talking about three major functions, we're talking about the loan processor, the loan, underwriter and the loan closer. Loan processor normally will collect the data, verify the data and prepare the data to be underwritten by the underwriter. The underwriter takes that information, underwrite to the loan, assesses the risk, and this decides whether or not this loan should move down the stream to get closed. And then the closer basically is the last line of defence to again, verify data that all the is correct, and then have a bunch of other things they need to collect and then verify. So we're talking about collection of data, verification of data, assessing data and preparing the data for closing.

That's all it is.

The tricky part is that every lender has their own agenda of how to run the operations to achieve the same result. The result is very strict. There's a mortgage, there's a note, and there's like a bunch of things.

And it's very standardised the output is standardised, the input is also standardised, in between is definitely not.

And that's based on the operational agenda of the lender to try to save costs.

I'll give you one tiny example just to give it some colour. So some lenders like to pull a soft credit report. It costs money. Let's say I'm just making up numbers. Let's say it costs $5. Would you pay $5 per every loan that goes through your system, or will you wait until later to do that? It really depends on the capabilities of the operation to actually close loans that hit the pipeline. So one thing that they will do is they're going to look at their funnel this

Okay, from from 100 loans that we get as an application, how many actually go to the finish line. And when they go to the finish line, or when they don't go to the finish line is it because something related to reverse score if it is, and maybe there's a way it's not front loaded, pay $5 on a soft pull and not pay $25 on a hard pull that is 100% required. And this is just one tiny example. There's like 100 of procedures that happen behind the scenes.

So there needs to be some sort of product that allows operations and managers to decide what they want to do when they want to do it, and dynamically changed, whatever they decided, based on financial results. There are legitimate reasons why that level of granularity of flexibility does not exist in the market.

And there's a void that can only be filled by startups at this point in time. And Elphi is just one of those startups trying to do exactly that.

Brendan Le Grange 15:56

Clearly, you know, we're in a world where analytics and tech is making big strides. I think you've dropped some hints there, but is there a role in there for also just better analytics or the data in designing and operating those processes?

Eilon Shalev 16:10

Definitely, definitely, there's a role. First, there's a lot of insights that could be harvested and leveraged and used to improve procedures in real time, in some cases, but I'll say there are two elements to any data analytics scenario.

The first one is managerial knowledge about what's going on just like your financial statements, you probably have some metrics that you want to review, time per closed per type of employee, and all that stuff. Normally, today, those reports do not exist out of the box inside the software providers tool, but they are exported into a file that then can be consumed by a third party that needs to turn that into graphs. And you know what? The problem with that is that as soon as you export data, it is immediately not in sync with what actually happened, that data is no longer relevant. So what we already have proven in the past is, that's pretty simple. You just see that with other software providers where you have data analytics embedded within the product, and it's connected in real time to the database. So you can in real time, see graphs and visualisations of, of key metrics that you care about.

But then the question is, who decides which graphs are there and who decides what the data structure is to provide information to those graphs and provide insights. And what we wanted to prove which we did a proof of concept was to give users the ability to create graphs based on third party solutions that live inside our software connected to our database, and bet idli in the product. We're going to roll that out sometime in 2023.

There's several tools that we're looking at, we've proven it with two different tools, we need to decide because pricing wise, it's very expensive abuse, some tools versus other ones. And this is a third party risk data thing that we need to figure out. But that's the boring stuff.

To be honest, the cool stuff is leveraging machine learning. And specifically, I'm saying machine learning not AI, because it is what it is. It's machine learning.

There are many schools of thought. But specifically, one example is there. There's three models for machine learning, descriptive, prescriptive and predictive. So descriptive side is describe information from the past, prescriptive is you offer recommendations what to do based on information from the past. And predictive is what you predict that will happen based on information from the past.

And we've dabbled with the last two, that were as soon as there are loans living in the system and in an active mode in progress, you know, from application to closing any stage throughout that process, there must be a way to assess the likelihood of that loan to actually close leveraging historical data that you have. And based on that likelihood, you can extrapolate very important insights into pipeline prioritisation, and I'll give you an example. I am a loan processor. I have 100 loans in my pipeline. I know that in a month, I can address maybe 50 of them. How do I prioritise based on what based on loan amount based on who sends me more emails based on the date I received the application based on estimated closing date? How do I prioritise my work? So there is another tool that you can use, which is the likelihood of closing the loan. And then you can extrapolate, maybe you want to optimise revenue for the company. So you can take the likelihood of closing the loan, multiplied by the revenue based on the interest rate of the loan, and then all of a sudden, you have a very strong insight that you can prioritise by then the question is, how accurate is that prediction?

So just to give you a little bit of numbers, we ran with like 2,000 loans. I think real data have very poor information was there like maybe maybe it was six data points that we can actually use to train the model. We did a regression model non machine learning, and we got to I forget the number was exactly what sounds between 60 and 70 something percent of accuracy in terms of predicting the likelihood of closing alone. And when we use machine machine learning, we use three different models, the highest performing one was 97% accurate, and, and the lowest was like 92%.

Now it's possible that it's, you know, maybe 2,000 is not enough and, but the machine learning models were, by far beating the regression models, and I was very big on regressions. When I studied economics, I really enjoyed statistics and, and regressions. And I was just blown away by the capabilities.

And when we introduced that capability, or that plant introduced the capability into our software to potential customers, they were amazed by it. And so I can definitely see machine learning, helping out with predictive models to prioritise pipelines, the prescriptive model, I think, and help out with not recommending what to do, but sharing what, in other cases, based on historical data, what the next step would have been in order to drive forward the loan so we can close.

Brendan Le Grange 21:03

Clearly, the more complex it becomes, the more we do need support from algorithms. And yeah, we can see it in all aspects of life at the moment. And I think that's where we're going to be in a world right, where it's supported by models. And yeah, I think the old tools straight up accuracy, they can probably still do a decent job.

Yeah, 60-70% accuracy is still much better than nothing, but it's not 97%, which obviously introduces some of its own challenges about self fulfilling prophecies and things when you start investing all your time in the ones that the model says will close. And you know, that makes him more likely to close. So you do need to be careful of those sort of feedback loops. But yeah, I think it's an exciting stage for for the analytics.

And to wrap up, I guess, analytics gear, as I said, my backgrounds in the credit world, and I didn't work for FICO but I worked with similar scores - I hear no mention of you looking or at all interested in building credit scores. So is that a space you're looking in? Are you more about the process and movement of the data, then risk management risk prediction?

Eilon Shalev 22:08

Yes, in this case, we definitely steer clear from anything related to risk assessment, we are providing the users with the tools to review any material that they need to and assess the risk on their own. This is a business of its own, there's so many companies just focusing on risk assessment. So for us to focus not only on what we're doing, but also on areas that companies put 100% of their resources on, would even feel arrogant from our end.

Our forte is focusing on the process automation, but also mentioned, in terms of risk assessment. Again, it goes back to the mind blowing description of the way the pricing of the interest rate happens behind the scenes. The risk actually is taken by the investors, not even the lenders, the lenders are not assuming any risk, I mean, the only risk they're assuming is whether or not they will be able to sell the loan afterwards, and they already getting an approval to sell the loan afterwards, before the loan has even been originated. So the investors are taking the risk, which means the investors are supposed to assess the risk. And what they do is they do that with a very, very fancy us providing that tool through our platform to the lender. So the lender can use the guidelines from the investor to underwrite the loan. On top of that, sometimes they won't do a loan, even though theoretically, they could sell that loan for whatever reason, they can decide what their own thing as long as it's fair. And so that's when they put their own risk assessment. So it's kind of unlikely that we can come up with a better risk assessment tool, convince the large investors, you know, Fannie Mae or Freddie Mac, to use our tool, and then enforce that on all of our competitors who also provide software.

So it just doesn't sound right to me to focus on it. At this point in time. In our in our journey,

Brendan Le Grange 23:54

You mentioned that there are other risks a keyboard in there. And obviously lenders are naturally oftentimes conservative people, as a you've got built in checks and tools to help with compliance and things. So this is not about the fastest possible loan and we're gonna race to one day, same day delivery, there are natural checks that have been built in there to say, well, there's a reasonable minimum as about getting rid of the unnecessary parts.

For a conservative lender who's saying, I don't want to move too fast. What what in the Elphi tool suite is there to help protect and make sure that the necessary i's are dotted and t's are crossed?

Eilon Shalev 24:31

That is an interesting question.

And I'll say that, again, we are hosting the process. And there are many different procedures that need to happen within the process. So first, we enforce some sort of milestone structure. Where does there's a workload, first milestone second milestone, thermals and all that stuff? That by itself, by the way, even is configurable but the idea is that some things can happen only when they need to happen, and some things cannot happen before something else happens.

Another thing that is important and I think and because you mentioned some compliance, there are obviously compliance layers that need to be taken into consideration when closing a loan is specifically a use case that we haven't touched yet. We're aiming to touch it in 2023 is actually the loans that go to Fannie and Freddie those those mortgage those loans to become mortgage backed securities after the fact. Currently, we're supporting business purpose lenders, which are fix and flip loans and rental property loans and new construction loans versus the consumers who actually take the mortgage and buy a house and live in it. And those loans that go into Fannie and Freddie, these loans are under severe scrutiny.

So there needs to be a compliance layer sitting on top of the of our solution to make sure that the t's are crossed, and the i's are dotted.

It's interesting, you know, the 10 day goal, I think there's some compliance requirements to do something within 10 days of closing the loan. So I'm thinking if the loan is closed in two days, you may not have enough time to actually do what you need to do during those two days. It takes it takes time to verify stuff.

Brendan Le Grange 25:59

Eilon, Elphi is a young and nimble startup, so you're able to adjust based on on what you're seeing developing so so as you think through what your plans are for 2023 and beyond what are some of the big trends may be that you're watching closely in the mortgage space? And where are you focusing Elphi's ambitions.

Eilon Shalev 26:18

So the first and foremost thing that we're looking at is the interest rate and the ramifications of that on the mortgage market, more than mortgage backed securities prices, as well as the 10 year treasury and all the way down to the actual individual interest rates on a loan level basis. It's pretty obvious that the market is has taken a downturn and slowed.

I mean, definitely not $4 trillion, are going to be originated in 2023. But I think the predictions are $2.3 trillion, which I find it interesting, you know, before 2019, you would see a good year would be a $2 trillion year, the bad year would be $1.4 trillion. An average year when I when I wrote my thesis about this business, I was referring to $1.8 trillion years an average. But $2.3 is a pretty nice number.

However, and that's a big however, if you don't consider only volume leave the number to $2.3 trillion. But you consider the number of units, you realise that house prices went up so hard, so much. So the number of units go down. So the number of units go down, there's fewer loans that are being created normally, before 2020, you would see in some publicly traded companies portray seven to 10 million loans a year in the industry.

And now we're talking about potentially four or five, six. So it's a pretty large hit, especially if your way you're making money as a software provider is based on closed loan. So if you're used to 10 million loans to be out there, now you expect five, you basically half the or total addressable market.

However, this is an opportunity, actually, for everybody wanted to say both lenders and software providers, but actually consumers as well. And and here's why interest rates are up dramatically, dramatically up. I assume, based on following the news that it's going to stay high Fed funds rate 5% or so. And by the end of 2023. But at some point, it will go back down to promote growth and prosperity.

And when it does, there's going to be at least a year worth if not two years worth of borrowers who took mortgages because they wanted to buy a house and live in it that are super high 7% 8%.

And guess what these people are going to do that are going to refinance their loans.

And when they do, there's going to be a boom of refi, which exactly happened in 2020/ 21 when interest rates went down. So the same wave is going to happen, maybe not $4 trillion, but it's going to be a wave. And that will happen somewhere in 2024 2025, I have to assume, and we want to be there when that happens. So we could support again.

It's supporting the lenders who are tech savvy and forward looking with their operational aspirations so they can double or triple their volumes with the same amount of staff and capitalise on their ability to be more efficient. By so doing, reducing the number of days digs into clothes alone by so doing getting better premiums on those loans. And as a result being able to provide lower interest rates to those end consumers, which will have a ripple effect again on increasing demand on their end because their interests are lower than their competitors.

We want to be there when that happens. And we are fortunate enough to have enough runway to be there when it happens. So in 2023, we need to continue our developer product developments.

As I mentioned, we do not support currently defended for any use cases, we need to add the data schema that follows the standards and connect a few integrations compliance for example, we talked about compliance earlier. That's the first thing that we need to do on the product side of things. And the second thing is improved even better to infrastructure so we could get 30 clients immediately up and running 50 clients in a matter of weeks, which is something unheard of in the mortgage industry. And we believe we can be there

Brendan Le Grange 29:59

Eilon, it's been fantastic chatting to you and fantastic hearing but of the the behind the scenes as it were of the American mortgage market. You know, all those nuances are actually quite intriguing and affect customers in ways you might not expect.

So thank you, again for your time.

If people want to reach out to you to talk about Elphi, perhaps they're in the mortgage industry or they just want to learn a bit more about the business. Where can they go to contact you? And where can they go to track its progress,

Eilon Shalev 30:26

reach out via email, eilon@elphi.io They can also go to the website, www.elphi.io. and read more about what we're doing. And also follow us on LinkedIn. If you do reach out to me on LinkedIn, just say that you heard me on the podcast and I'm trying to connect with people who have a real material reason to meet with me.

Brendan Le Grange 30:52

I'm just one thing in terms of the name and the elephant mascot. Is there a story behind that at all?

Eilon Shalev 30:59

Well, at the beginning, I was researching blockchain and it really believed into technology. I still do not for the mortgage industry, to be honest, but learning about blockchain and the immutable record that was touted time and time again. And the fact that Elphi is supposed to be the system of record. The idea was that elephants never forget, and neither does our system of record.

Brendan Le Grange 31:20

Thank you very much again. Yeah, it's honestly been a great chat. And thank you all for listening. Please do look for and follow the show on your favourite podcast platform, and share the updates widely on LinkedIn where lending nerds are found in our largest concentration. Plus, send me a connection request while you're there.

This show is written and recorded by myself Brendan Le Grange in Brighton England, and edited by Fina Charleson of FC Productions. Show music is by Iam_Wake and you can find show notes and written transcripts at www.HowtoLendMoneytoStrangers.show

And I'll see you again next Thursday.