What’s Happening in Today’s Mortgage Industry?

Today’s mortgage industry is anything but business as usual, with dropping rates, rising applications, and the widespread adoption of AI which is transforming the underwriting process. In this week’s podcast, Sapiens’ Marketing Director Mark Sidlauskas is joined by Michael Kelleher, Founder of Adopt The Brand LLC and Justin Patterson, Sales Engineer, Sapiens Decision, to explore how new underwriting technology is increasing transparency and efficiency for greater customer experience and market share.

Mark Sidlauskas|Michael Kelleher|Justin Patterson

Mark Sidlauskas: Hi everyone, and welcome to Sapiens Insurance 360 podcast. I’m your host, Mark Sidlauskas, Marketing Director at Sapiens, and I’m glad you’re out there listening. This is where we discuss the latest news, trends, and issues from across the insurance solutions and technology spectrum. Today we’ll be talking about the mortgage industry. This industry has always been of critical importance to the U.S. economy, as the financial engine that propels millions of borrowers towards the dream of a home ownership.

Mortgage loans themselves are a key barometer for the economy. And now that rates have dropped, mortgage activity is picking up. And we’re looking at a whole new set of challenges in underwriting security, sizing, and servicing these loans. We’re also beginning to see the adoption of AI in the industry, which has the potential to drive transformation across the industry.

Today, we have two subject matter experts joining us to discuss what’s happening in the mortgage loan industry and how new underwriting technology solutions are having an impact. Joining us today is Michael Kelleher, president and co-founder of Easy Mortgage Apps LLC. He’s also a recognized thought leader and entrepreneur who regularly consults with senior executives across the industry on innovative strategies to help them capture an increased market share and profitability.

Welcome, Michael!

Michael Kelleher: Thank you. Mark. It’s a pleasure to be here today.

Michael Kelleher: And also joining us today is Sapiens’ own Justin Patterson. Justin is a Sales Engineer at Sapiens Decision. He has over 15 years’ experience in mortgage lending and now 10 years’ experience in decision modeling. So welcome, Justin!

Justin Patterson: Thanks, Mark. Happy to be here.

Mark Sidlauskas: So thanks both. And I’d like to start with Michael. So Mike, what do you see as the biggest problem facing lenders today in the mortgage market?

Michael Kelleher: I see the biggest problem [as] siloing. So whether it is the labor or the different departments that go into a decision and the different checklists and procedures and tribal knowledge they try to bring into a project, or what’s becoming a more glaring issue, I find, is the siloing of the mortgage technology, especially when you have giants like ice growing larger and larger, and the reliance on PPE and certain point of sales.

So when you have the loan origination software to remove some acronyms here, and you have the point of sale, and then you have the pricing product and eligibility, these giant softwares that hold all of those decisions, you just get a larger and larger trend towards siloing. And the ultimate problem with that is it slows down speed, because I believe the number one problem the industry is facing, the future or to be future proof is speed. Ultimately, the Amazon’s the Zillow’s, the way they will come into the market is being able to compete at a speed that meets the instant gratification of where customers have been trained to be with smartphones and tablets and Amazon, Alexa. This is why you can’t compete at the speed others are if you’re constantly siloing.

Justin Patterson: So to kind of piggyback on what Michael is saying, we see that from a decision perspective in that the same decisions need to be made multiple times throughout the process. And when the mortgage origination process is siloed, as Michael [is] describing it, means the redundancy, it means inconsistency. So we see, we identify those as opportunities to more standardize and make the process more consistent. And that’s just, further exemplified with the separation of the tech. If you constantly have to code the same rules, and each system that Michael identified, Well, now we’re back to making changes [that] take longer inconsistencies throughout your process. So centralizing those things, removing those silos, by means of product-like decision can certainly help that that whole process.

Mark Sidlauskas: So can you explain that a little bit more, Justin?  We have all these systems. They’re all integrated. They all talk to each other. But yet, is there a need for centralization? Could you explain what that means?

Justin Patterson: Sure. So if you’re trying to determine if someone qualifies for, a loan product, whatever the product is, and you have to document those rules so that the underwriters know it so that the loan origination systems know that. So that broker portal knows it. And maybe your, your pricing system. Well, that’s three too many systems that you’re having to recreate that same code. So if you’re able to just document that once and let all the systems use the same documented code, now we can make changes, introduce new products more quickly, and just enforce consistency.

Mark Sidlauskas: Okay. And Michael, do you see a need for this in the industry as just as described?

Michael Kelleher: Yeah, a lot of the work I have done over the past couple of years with some brilliant minds has to do with interoperability of these different systems using in an API-first solution. However, I believe there needs to be a better connective tissue within all of these softwares, because the APIs don’t always meet the decisions that need to be made. And when you can create decision models, or as I try to tell the lenders I’m speaking with, decisions is the universal language. Whether we’re talking to a software or a person. I cannot think of a more centralized, unique identifier than what is the decision. And so I’m excited to see decisioning, really be that layer or that connective tissue that brings it all together.

Mark Sidlauskas: Great. So, Justin, what is this this connective tissue, this central layer that puts it all together? We’ve talked about having it centralized. We talked about decision models. But what does that all mean in terms of operations? What should I be thinking about if I’m a lender?

Justin Patterson: Sure. Yeah. So it’s, we can kind of scale that. So on the one hand, I could say that, decision, is an excellent way of documenting and identifying gaps in your business logic, but that only generates so much excitement. So perhaps a better way to say it is that once your business logic is documented, I can automate it. That starts to get a little more interesting, where it starts to really kind of unfold it. Now, if we can talk to a department head and say, now that your logic is documented and automated, now you can enforce a consistent process. Now you can really affect change within your department. And then if we take that up another level and talk to the CFO, we can say, let’s try and make your origination process the whole operation. Be more consistent, focus on automating the tasks, and really develop a consistent, repeatable process of producing loans.

Mark Sidlauskas: Am I able to optimize?

Justin Patterson: All along the way? You’ll be optimizing because, really, the cost per loan comes down to the number of touches you have to make. So if we can make each touch, whether that’s a person’s checklist or the underwriting of a loan reviewing to see if a loan is ready for closing. But if we can optimize all of those touchpoints so that you’re doing it right the first time and only need to do it right once, we can optimize your process and then, by extension, reduce your cost per loan.

Mark Sidlauskas: Okay. So that would mean if I’m in marketing, I can market easy. If I’m in sales, I have better access to my CRM and can understand what my sales need. If I’m in quality control, I can eliminate some of those checklists and on it goes. So, Mike, what’s your thought here? It’s just something that the industry is demanding?

Michael Kelleher: Yes. And if it isn’t demanding it, it’s they just don’t see it yet. And that’s why I am out there trying to knock on every door and help them understand. I call this software after seeing it really an “iPhone moment.” What defines an iPhone moment? It was the moment when people went from a BlackBerry to seeing an iPhone for the first time and couldn’t go back to not seeing that iPhone, not visioning what was possible with [that] touchscreen in your pocket. And I believe this is the same way you talked about, optimizing point of touch. [And here’s] the real takeaway we’re seeing from some of the larger banks we’re already talking with. And I know we’ll get to this, a little bit later as far as labor goes. But it’s not just being more efficient in the touches by automating it for your workers, but it’s how do you reverse-engineer it so that those touches are by less expensive or workers with a smaller salary, and then let the underwriters of the world in mortgage with the larger salaries, their touches can be more geared towards what their expertise is. So they’re not doing small labor tasks like they are today all over the place. The decisions will help them just make decisions on items that an underwriter is trained and licensed to do.

Mark Sidlauskas: So we’re all familiar with this whole boom-bust cycle in mortgage. You gear up, you hire lots of people, and the bust comes, you let people go. That seems to be the way that the industry runs. So how could you take this, this technology and apply it to that labor issue that you’re talking about? Should I start at underwriting or someplace else?

Michael Kelleher: I believe it’s a mindset. So you start wherever you were planning on hiring next. The reality is and [I] hate to be the one to deliver it. But there are certain people in this industry that remind us we have decreased from a top where we were during Covid, as far as volume and units and production goes, we are down 70% on that number, yet we’ve only cut 30% of the workforce. So there’s a 40% delta there. And this is why we’ve had eight consecutive quarters of non-profitability for many lenders. Now these numbers are probably a quarter too old. We’ve seen a jump in production this year. But oftentimes what happens is we take those great moments and we put our blinders on and move forward. And that’s why we fail. When it comes to cyclical hiring, I do think one place the lending industry has done well, or starting to at least get fine-tuned on, is the ability to do business process outsourcing overseas. I believe the next frontier is decisioning, decision model, Sapiens Decision. This is where you’ll be able to when you go to hire. So you ask the question mark. I think whatever job posting you are about to put out there, you would ask, could we come up with a way to have exception based, whatever that is? Do we have a way to automate to make that person better? Are we thinking about our service levels? And all of that typically comes back down to what decisions have been made and what decisions do you want the person you’re about to hire to make?

Mark Sidlauskas: So back to you, Justin, on this, as we make this shift and make the best use of the labor that we have. That means people have to be trained in decision management to really understand the processes and how they want to automate that. How easy is it to learn decision modeling and actually do the work that you’ve been talking about?

Justin Patterson: Sure. Yeah. No, the process for decision modeling, modeling is, very easy. It’s, [a] completely no-code platform. There is a change in, in thought. It takes a little bit of coaching. but we can teach anyone. And really, what you’re doing is you’re focusing on the question you want to answer with the model. And throughout the whole mortgage origination process, it’s just people trying to answer their question to advance the loan. Do I need to disclose, can I approve this loan? Is the mortgage insurance set up right? Everything we can phrase as a question, it’s all answered by data, and the whole process is just a different person evaluating a different set of data to answer a different question. And each and every time that’s the case, we can document that with the decision model and automate that with the decision model. And just if we’re playing buzzword bingo, I think what I hear a lot is we’re trying to make smart people smarter and really, when it comes to the hiring/firing loop, what we really want to do is scale the tech and not the people. And from a project perspective, what I’ve always wanted to do is walk into a QA department and say, what’s the issue you faced most often? Take the requirements to produce that issue document and a decision model, and it’s automated, and now it goes away. And then I’d really want to say, all right, just give me the entire checklist. Such a short thing because I’m just going to automate the entirety of that and put those checks in place, automated when the person is doing the work, and other areas that get really interesting and say, okay, take me to the process that has the longest cycle time, because I know if we went through and automated it, we could make it more consistent and cut it back. And then to Michael’s earlier point, I probably would also want to say, where is your biggest department? Where do you have your most people? Because there’s another opportunity to say, okay, what is causing all of that work that requires that many folks? Perhaps we can automate those checklists and tasks, and then not only make that process faster, more efficient with less people.

Mark Sidlauskas: Right. So, Michael, we started the discussion around mortgage lenders. But can these principles be applied or the same challenges applicable in secondary markets, servicing and so on?

Michael Kelleher: Anywhere there is a decision, I have come to find, you can deploy Sapiens Decision. And the beauty of it is there are certain areas when you get further away from production. It’s not always the larger budget to be able to do it, and it’s not always that first cost to enter price, but it is the implementation process. It is the maintenance process. It is the when you’re getting into coding, it’s hiring developers and then giving up your tribal knowledge to somebody that has never originated before. And when it comes back to you having to be told, you can do this, you cannot do that. Sapiens Decision eliminates 99% of that. And it’s what attracted me to it is I could sit down with the subject matter expert and with the no code-low code, [and} we could begin working. And the only, the only in this applies to [the] secondary market and the servicing, the only restrictions that could possibly keep us from doing what we want is our ability to think it. And so, yes, I believe when it goes back to that connective tissue and siloing, why does origination get siloed from servicing if you decide to retain the entire service? Or maybe you sell the the loan, but you retain the servicing rights, that’s still your customer. Maybe on the secondary market, you have it on a wholesale line while you’re deciding which investors [are] the best fit. Or maybe you’re just trying to expand your product mix so that you can differentiate and not just resell GSE. All of those are siloed opportunities. Departments and software that do those tasks. It’s further and further apart. And this again is why I’m so bullish and excited about Sapiens Decision. It’s the first connective piece, connective layer that I have seen. Period. I know a lot of people try to create data lakes and data warehouses to be able to at least bring some of these departments and software together, but that requires a lot of developers, a lot of people involved, a lot of cost savings. Decision eliminates all of that, allows you to move quick. And it’s what really excites someone like me that can move now without needing a huge development team or Json and Java and all this knowledge that I currently don’t have.

Justin Patterson: And so I would kind of piggyback on Michael’s comments there. So I was in, mortgage, as you said, and I was actually a decision manager. So I was a client before I worked for the company, and the siloing was kind of interesting in that, I had someone from secondary come to me and say underwriting is not labeling the kind of classifications for our loans correctly. And the agencies are rejecting our pools, and it’s going to start to affect our cost if we can’t get the kind of classification. Right. So right off the bat, I knew the underwriters didn’t know that when they were putting in the class classification, how it was affecting downstream, the pooling and then the tribal knowledge. What I came to find out was that all of the underrated are chained to just if they didn’t know what the classification should be, just use Q secondary is telling me write a rule that says the underwriters can’t use Q anymore because they’re getting it wrong all the time. So tribal knowledge had failed us. And what I found was no one really knew all the rules. But I found the condo classification code gurus and the company documented the logic, automated it, and the problem just went away. Now the underwriters couldn’t approve the loan without getting the classification correct, and all our pools went through much more smoothly.

Mark Sidlauskas: Excellent. I don’t want to get, get rid of the letter Q, that’s important. And, right, let’s have some more iPhone moments out there, too. So thank you both for your thoughts. Mike and Justin, we covered a lot of ground here. Thanks to you both for providing a comprehensive view of today’s mortgage industry from a high level and a technology perspective, and how Sapiens, of course, can help manage underwriting cost per loan and cycle times with new tools. So to our listeners, thanks as always for spending time with us here today. We love hearing from you. So if you have any comments or like to follow us on social media, please reach out to us on our channels and don’t forget to subscribe to the podcast. We’ve got more coming, so be sure to tune in next time to Sapiens Insurance 360.

Season 3, Episode 2

Today’s mortgage industry is anything but business as usual, with dropping rates, rising applications, and the widespread adoption of AI which is transforming the underwriting process. In this week’s podcast, Sapiens’ Marketing Director Mark Sidlauskas is joined by Michael Kelleher, Founder of Adopt The Brand LLC and Justin Patterson, Sales Engineer, Sapiens Decision, to explore how new underwriting technology is increasing transparency and efficiency for greater customer experience and market share.

Mark Sidlauskas|Michael Kelleher|Justin Patterson

Mark Sidlauskas: Hi everyone, and welcome to Sapiens Insurance 360 podcast. I’m your host, Mark Sidlauskas, Marketing Director at Sapiens, and I’m glad you’re out there listening. This is where we discuss the latest news, trends, and issues from across the insurance solutions and technology spectrum. Today we’ll be talking about the mortgage industry. This industry has always been of critical importance to the U.S. economy, as the financial engine that propels millions of borrowers towards the dream of a home ownership.

Mortgage loans themselves are a key barometer for the economy. And now that rates have dropped, mortgage activity is picking up. And we’re looking at a whole new set of challenges in underwriting security, sizing, and servicing these loans. We’re also beginning to see the adoption of AI in the industry, which has the potential to drive transformation across the industry.

Today, we have two subject matter experts joining us to discuss what’s happening in the mortgage loan industry and how new underwriting technology solutions are having an impact. Joining us today is Michael Kelleher, president and co-founder of Easy Mortgage Apps LLC. He’s also a recognized thought leader and entrepreneur who regularly consults with senior executives across the industry on innovative strategies to help them capture an increased market share and profitability.

Welcome, Michael!

Michael Kelleher: Thank you. Mark. It’s a pleasure to be here today.

Michael Kelleher: And also joining us today is Sapiens’ own Justin Patterson. Justin is a Sales Engineer at Sapiens Decision. He has over 15 years’ experience in mortgage lending and now 10 years’ experience in decision modeling. So welcome, Justin!

Justin Patterson: Thanks, Mark. Happy to be here.

Mark Sidlauskas: So thanks both. And I’d like to start with Michael. So Mike, what do you see as the biggest problem facing lenders today in the mortgage market?

Michael Kelleher: I see the biggest problem [as] siloing. So whether it is the labor or the different departments that go into a decision and the different checklists and procedures and tribal knowledge they try to bring into a project, or what’s becoming a more glaring issue, I find, is the siloing of the mortgage technology, especially when you have giants like ice growing larger and larger, and the reliance on PPE and certain point of sales.

So when you have the loan origination software to remove some acronyms here, and you have the point of sale, and then you have the pricing product and eligibility, these giant softwares that hold all of those decisions, you just get a larger and larger trend towards siloing. And the ultimate problem with that is it slows down speed, because I believe the number one problem the industry is facing, the future or to be future proof is speed. Ultimately, the Amazon’s the Zillow’s, the way they will come into the market is being able to compete at a speed that meets the instant gratification of where customers have been trained to be with smartphones and tablets and Amazon, Alexa. This is why you can’t compete at the speed others are if you’re constantly siloing.

Justin Patterson: So to kind of piggyback on what Michael is saying, we see that from a decision perspective in that the same decisions need to be made multiple times throughout the process. And when the mortgage origination process is siloed, as Michael [is] describing it, means the redundancy, it means inconsistency. So we see, we identify those as opportunities to more standardize and make the process more consistent. And that’s just, further exemplified with the separation of the tech. If you constantly have to code the same rules, and each system that Michael identified, Well, now we’re back to making changes [that] take longer inconsistencies throughout your process. So centralizing those things, removing those silos, by means of product-like decision can certainly help that that whole process.

Mark Sidlauskas: So can you explain that a little bit more, Justin?  We have all these systems. They’re all integrated. They all talk to each other. But yet, is there a need for centralization? Could you explain what that means?

Justin Patterson: Sure. So if you’re trying to determine if someone qualifies for, a loan product, whatever the product is, and you have to document those rules so that the underwriters know it so that the loan origination systems know that. So that broker portal knows it. And maybe your, your pricing system. Well, that’s three too many systems that you’re having to recreate that same code. So if you’re able to just document that once and let all the systems use the same documented code, now we can make changes, introduce new products more quickly, and just enforce consistency.

Mark Sidlauskas: Okay. And Michael, do you see a need for this in the industry as just as described?

Michael Kelleher: Yeah, a lot of the work I have done over the past couple of years with some brilliant minds has to do with interoperability of these different systems using in an API-first solution. However, I believe there needs to be a better connective tissue within all of these softwares, because the APIs don’t always meet the decisions that need to be made. And when you can create decision models, or as I try to tell the lenders I’m speaking with, decisions is the universal language. Whether we’re talking to a software or a person. I cannot think of a more centralized, unique identifier than what is the decision. And so I’m excited to see decisioning, really be that layer or that connective tissue that brings it all together.

Mark Sidlauskas: Great. So, Justin, what is this this connective tissue, this central layer that puts it all together? We’ve talked about having it centralized. We talked about decision models. But what does that all mean in terms of operations? What should I be thinking about if I’m a lender?

Justin Patterson: Sure. Yeah. So it’s, we can kind of scale that. So on the one hand, I could say that, decision, is an excellent way of documenting and identifying gaps in your business logic, but that only generates so much excitement. So perhaps a better way to say it is that once your business logic is documented, I can automate it. That starts to get a little more interesting, where it starts to really kind of unfold it. Now, if we can talk to a department head and say, now that your logic is documented and automated, now you can enforce a consistent process. Now you can really affect change within your department. And then if we take that up another level and talk to the CFO, we can say, let’s try and make your origination process the whole operation. Be more consistent, focus on automating the tasks, and really develop a consistent, repeatable process of producing loans.

Mark Sidlauskas: Am I able to optimize?

Justin Patterson: All along the way? You’ll be optimizing because, really, the cost per loan comes down to the number of touches you have to make. So if we can make each touch, whether that’s a person’s checklist or the underwriting of a loan reviewing to see if a loan is ready for closing. But if we can optimize all of those touchpoints so that you’re doing it right the first time and only need to do it right once, we can optimize your process and then, by extension, reduce your cost per loan.

Mark Sidlauskas: Okay. So that would mean if I’m in marketing, I can market easy. If I’m in sales, I have better access to my CRM and can understand what my sales need. If I’m in quality control, I can eliminate some of those checklists and on it goes. So, Mike, what’s your thought here? It’s just something that the industry is demanding?

Michael Kelleher: Yes. And if it isn’t demanding it, it’s they just don’t see it yet. And that’s why I am out there trying to knock on every door and help them understand. I call this software after seeing it really an “iPhone moment.” What defines an iPhone moment? It was the moment when people went from a BlackBerry to seeing an iPhone for the first time and couldn’t go back to not seeing that iPhone, not visioning what was possible with [that] touchscreen in your pocket. And I believe this is the same way you talked about, optimizing point of touch. [And here’s] the real takeaway we’re seeing from some of the larger banks we’re already talking with. And I know we’ll get to this, a little bit later as far as labor goes. But it’s not just being more efficient in the touches by automating it for your workers, but it’s how do you reverse-engineer it so that those touches are by less expensive or workers with a smaller salary, and then let the underwriters of the world in mortgage with the larger salaries, their touches can be more geared towards what their expertise is. So they’re not doing small labor tasks like they are today all over the place. The decisions will help them just make decisions on items that an underwriter is trained and licensed to do.

Mark Sidlauskas: So we’re all familiar with this whole boom-bust cycle in mortgage. You gear up, you hire lots of people, and the bust comes, you let people go. That seems to be the way that the industry runs. So how could you take this, this technology and apply it to that labor issue that you’re talking about? Should I start at underwriting or someplace else?

Michael Kelleher: I believe it’s a mindset. So you start wherever you were planning on hiring next. The reality is and [I] hate to be the one to deliver it. But there are certain people in this industry that remind us we have decreased from a top where we were during Covid, as far as volume and units and production goes, we are down 70% on that number, yet we’ve only cut 30% of the workforce. So there’s a 40% delta there. And this is why we’ve had eight consecutive quarters of non-profitability for many lenders. Now these numbers are probably a quarter too old. We’ve seen a jump in production this year. But oftentimes what happens is we take those great moments and we put our blinders on and move forward. And that’s why we fail. When it comes to cyclical hiring, I do think one place the lending industry has done well, or starting to at least get fine-tuned on, is the ability to do business process outsourcing overseas. I believe the next frontier is decisioning, decision model, Sapiens Decision. This is where you’ll be able to when you go to hire. So you ask the question mark. I think whatever job posting you are about to put out there, you would ask, could we come up with a way to have exception based, whatever that is? Do we have a way to automate to make that person better? Are we thinking about our service levels? And all of that typically comes back down to what decisions have been made and what decisions do you want the person you’re about to hire to make?

Mark Sidlauskas: So back to you, Justin, on this, as we make this shift and make the best use of the labor that we have. That means people have to be trained in decision management to really understand the processes and how they want to automate that. How easy is it to learn decision modeling and actually do the work that you’ve been talking about?

Justin Patterson: Sure. Yeah. No, the process for decision modeling, modeling is, very easy. It’s, [a] completely no-code platform. There is a change in, in thought. It takes a little bit of coaching. but we can teach anyone. And really, what you’re doing is you’re focusing on the question you want to answer with the model. And throughout the whole mortgage origination process, it’s just people trying to answer their question to advance the loan. Do I need to disclose, can I approve this loan? Is the mortgage insurance set up right? Everything we can phrase as a question, it’s all answered by data, and the whole process is just a different person evaluating a different set of data to answer a different question. And each and every time that’s the case, we can document that with the decision model and automate that with the decision model. And just if we’re playing buzzword bingo, I think what I hear a lot is we’re trying to make smart people smarter and really, when it comes to the hiring/firing loop, what we really want to do is scale the tech and not the people. And from a project perspective, what I’ve always wanted to do is walk into a QA department and say, what’s the issue you faced most often? Take the requirements to produce that issue document and a decision model, and it’s automated, and now it goes away. And then I’d really want to say, all right, just give me the entire checklist. Such a short thing because I’m just going to automate the entirety of that and put those checks in place, automated when the person is doing the work, and other areas that get really interesting and say, okay, take me to the process that has the longest cycle time, because I know if we went through and automated it, we could make it more consistent and cut it back. And then to Michael’s earlier point, I probably would also want to say, where is your biggest department? Where do you have your most people? Because there’s another opportunity to say, okay, what is causing all of that work that requires that many folks? Perhaps we can automate those checklists and tasks, and then not only make that process faster, more efficient with less people.

Mark Sidlauskas: Right. So, Michael, we started the discussion around mortgage lenders. But can these principles be applied or the same challenges applicable in secondary markets, servicing and so on?

Michael Kelleher: Anywhere there is a decision, I have come to find, you can deploy Sapiens Decision. And the beauty of it is there are certain areas when you get further away from production. It’s not always the larger budget to be able to do it, and it’s not always that first cost to enter price, but it is the implementation process. It is the maintenance process. It is the when you’re getting into coding, it’s hiring developers and then giving up your tribal knowledge to somebody that has never originated before. And when it comes back to you having to be told, you can do this, you cannot do that. Sapiens Decision eliminates 99% of that. And it’s what attracted me to it is I could sit down with the subject matter expert and with the no code-low code, [and} we could begin working. And the only, the only in this applies to [the] secondary market and the servicing, the only restrictions that could possibly keep us from doing what we want is our ability to think it. And so, yes, I believe when it goes back to that connective tissue and siloing, why does origination get siloed from servicing if you decide to retain the entire service? Or maybe you sell the the loan, but you retain the servicing rights, that’s still your customer. Maybe on the secondary market, you have it on a wholesale line while you’re deciding which investors [are] the best fit. Or maybe you’re just trying to expand your product mix so that you can differentiate and not just resell GSE. All of those are siloed opportunities. Departments and software that do those tasks. It’s further and further apart. And this again is why I’m so bullish and excited about Sapiens Decision. It’s the first connective piece, connective layer that I have seen. Period. I know a lot of people try to create data lakes and data warehouses to be able to at least bring some of these departments and software together, but that requires a lot of developers, a lot of people involved, a lot of cost savings. Decision eliminates all of that, allows you to move quick. And it’s what really excites someone like me that can move now without needing a huge development team or Json and Java and all this knowledge that I currently don’t have.

Justin Patterson: And so I would kind of piggyback on Michael’s comments there. So I was in, mortgage, as you said, and I was actually a decision manager. So I was a client before I worked for the company, and the siloing was kind of interesting in that, I had someone from secondary come to me and say underwriting is not labeling the kind of classifications for our loans correctly. And the agencies are rejecting our pools, and it’s going to start to affect our cost if we can’t get the kind of classification. Right. So right off the bat, I knew the underwriters didn’t know that when they were putting in the class classification, how it was affecting downstream, the pooling and then the tribal knowledge. What I came to find out was that all of the underrated are chained to just if they didn’t know what the classification should be, just use Q secondary is telling me write a rule that says the underwriters can’t use Q anymore because they’re getting it wrong all the time. So tribal knowledge had failed us. And what I found was no one really knew all the rules. But I found the condo classification code gurus and the company documented the logic, automated it, and the problem just went away. Now the underwriters couldn’t approve the loan without getting the classification correct, and all our pools went through much more smoothly.

Mark Sidlauskas: Excellent. I don’t want to get, get rid of the letter Q, that’s important. And, right, let’s have some more iPhone moments out there, too. So thank you both for your thoughts. Mike and Justin, we covered a lot of ground here. Thanks to you both for providing a comprehensive view of today’s mortgage industry from a high level and a technology perspective, and how Sapiens, of course, can help manage underwriting cost per loan and cycle times with new tools. So to our listeners, thanks as always for spending time with us here today. We love hearing from you. So if you have any comments or like to follow us on social media, please reach out to us on our channels and don’t forget to subscribe to the podcast. We’ve got more coming, so be sure to tune in next time to Sapiens Insurance 360.