Lykken on Lending

Assessing loan quality has always been the realm of underwriters. But for the past couple of years, AI-enabled technologies have taken over the bulk of the work from human underwriters, making them more efficient and productive at their jobs. This is what technological innovations like Candor do. In this episode, Sara Knochel, CEO of the tech company’s new data and analytics business, explains how they are using Candor’s wealth of proprietary data to create unique software products for the mortgage industry. The year 2023 is going to be marked by a lot of significant developments in the further digitization of the whole industry, and Candor is right in the forefront of making that change happen. Tune in and learn how they’re doing it and what we can expect from them on the horizon.

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Watch the episode here

 

New Loan Quality Services: What’s On The Horizon For 2023 With Sara Knochel Of Candor Technology

I’m excited to have Sara Knochel here with Candor. We had this conversation at their event at the National Mortgage Banking Conference in Nashville. I have a feel for the brilliance of this team. I’m excited to have Sara come to the show and have her here because there was so much you were sharing Sara. Welcome to the show. It’s good to have you back.

Thank you very much, David. I’m glad to be here.

For those that may not know who you are, could you give us a little bit of a background of your journey to where you’re at? You play a key role there at Candor. Tell us how you got there.

My passion has always been data and analytics. My entire career from my undergraduate degree onward was around technology and data. I started to focus more on the secondary space of mortgage. I worked for a company called Loan Performance, which eventually got bought by CoreLogic. I was at a place called Digital Risk, which did forensic audits but always worked with data. The funny thing about working with data is it’s only as powerful as your understanding of the business that it’s coming from, though I had always liked to sit between business and technology.

Data is only as powerful as your understanding of the business that it's coming from. Share on X

I went and got an MBA several years ago and when I came out of school, I decided to consult for a while and built a data and analytics practice in Atlanta consulting for many different companies, including Candor Technology, which was one of my clients. I knew the founders from my Digital Risk and CoreLogic days. They became my client at that time. Finally, in 2022, I came on full-time to join them in building a data and analytics company for Candor.

 

 

We’re so excited to have you here, sharing the vision and what you’re developing there, which is so exciting but it’s so much more than just the technology. Technology in and of itself, Sara, is quite astounding. It’s so well-designed that it does underwriting and it is doing it in such a way that you are able to reobtain a patent on it. The company Candor is something much more than just technology. We touched on this with someone when we were doing that last interview but I’m excited about sharing that. Marc, you’re going to appreciate this. Marc Helm, welcome to the show. He is joining me for the interview. Marc, it’s good to have you here.

I’m glad to be here and learn from Sara. This is going to be outstanding.

Especially the part which you’re about ready to hear Marc, which is something near and dear. It is something that you’re working on in your doctorate but it’s the health of the organization that makes it so unique. Tell us a little bit about what makes Candor special beyond the technology, Sara.

First, it was founded with a very specific purpose. All decisions in terms of what we make, how we go to market and even whom we hire and who works here all come from a central decision. The whole purpose of the company was to enable lenders to make the best decision possible at any point in the lending lifecycle. Quality was our focus. Make quality and enable quality decisions. With that being the case, a couple of things were very important to us at Candor, information excellence but the truth is also very important.

 

One of the hardest challenges in the mortgage is the data I’m looking at is true and correct because that will influence the quality of my decision. The truth has always been very important to us. We have a little saying, “The truth is our friend.” We have tried to staff the company with people who also believe that, who have a pursuit of truth and a pursuit of excellence and a commitment to the greater good.

We do believe that the company and its mission for quality are not only going to make life better for lenders trying to make a loan but it’s also going to make the capital markets better. The economies are impacted by our capital market. That’s the largest in the world for mortgages. Homeownership too has an impact on the average American and their access to credit. It’s funny how something tiny ends up having this ripple effect on all these other pieces.

When we first started talking Sara, I always thought of Candor as the ability to get greater productivity out of your underwriters. That’s how I first started viewing it. It certainly does that but it’s quite a bit more.

It’s quite common for people to have noticed the productivity that it brings first, especially in the last few years when we had such high volumes and people were only looking for a way to cope. Our technology performed exceptionally well. You had a five times increase in files you could process a day or underwrite a day. You had massive increases in pull-through and all that stuff but those were not the company’s goals when it set out to develop our artificial intelligence for underwriting. We call it CogniTech. That’s what the platform is.

CogniTech wasn’t built to reduce cost and increase speed initially. It was built to simply make a loan you could trust, make sure it was high quality and that the data was based on truth. That was the whole point. What it turns out is when you do that, you have this dramatic impact on the speed though because up until the invention of our technology, the only way that you could do that analysis was with humans.

Humans can only work so fast, especially if you want them to be at their best. No matter how much automation and digitization you add to the loan-making process, you still have this essential human that was necessary. What we’ve been able to do is take a large part of the human piece, especially the tedious ones.

The things that need to be executed repeatedly but to a high level of consistency and accuracy, we’ve been able to take care of those. You have your humans freed up to do the even harder tasks and the sophisticated looks and then you don’t have them doing all this tedious work. That’s where you get all these productivity and cost improvements.

 

 

I’m focusing on the technology aspect from my viewpoint having been involved with it for several years. With the pedigree you have out there, Sara, with Digital Risk, Loan Performance and CoreLogic, that’s about as heavy-duty as you can get. One of the things I’ve always focused on seems to be that no matter how much you try to take people out of the formula when you’re dealing with technology, they still are a very important point in that. You’ve already alluded to that in this conversation about people and their involvement.

How big a challenge is that day-to-day in dealing with the people aspect of making your technology work? How does that play into the type of people that you hire? What kind of workforce do you have at Candor to meet those goals? It takes technical people but it takes people that can communicate and get on the right level to make sure you’re getting all the pieces out that you need to make your technology work the best it possibly can.

It takes a wide variety of people with different expertise, technology only being one part of all of that. What you said about you can never take humans out of the process is true. I don’t expect us to ever not have humans in the mortgage manufacturing process but I do expect us to be at a state where we can fully empower those humans to use their time and skills most effectively by making as much of making process automation and digitization as possible already done for them.

We can’t expect us to not have humans in the mortgage manufacturing process, but we can fully empower those humans to use their time and skills most effectively by making as much of the decision making process, automation, digitization as possible… Share on X

To that effect, for us to figure out how to deliver that, we have brought into our company people from all different parts of the mortgage industry who’ve been in many different roles. For one thing, we have a robust team of underwriters that are on our product development team. They don’t underwrite mortgages. They write product specifications and test the product too but they have to work collaboratively with people who have been processors and loan officers because we have to think about the process.

Your technology is only as good as it can interact with the business process it’s trying to affect. We have people from all different parts of the process. We’ve been getting into loan quality services as well, which is outside fulfillment. We’ve also started talking to folks that work in that space. Some of our underwriters have been QC underwriters in the past as well. We have some people joining our staff who have managed QC in different organizations before. It takes this 360-degree view to get it right and to try and deliver the most value.

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I have one small follow-up question. Being a technology company, you have a lot of metrics on many things. Do you have metrics surrounding the performance and your end product to determine very quickly whether the focus should be to make it bigger, better and faster or should be on the technology side or is it on that management of that human side we talked about?

What you’re asking is what’s the next area that’s going to deliver the most value? Is it the human experience part and then interaction or is it the technology itself? The more you focus on the process, the better. Granted, the technology has to be pristine. The way that the engine comes to its conclusions has to be flawless. The way we get data into our system from documents or other sources has to be flawless. All of that has to be state-of-the-art and up-to-date but the process is key.

When you’re talking about an underwrite, what you’re talking about is a content analysis problem. Those are nuanced. In most technology solutions, when you program for something, you have a known outcome space. You know all the possible input parameters and that defines your outcome space. The challenge with underwriting is the outcome space is infinite. There are infinite paths that a loan can take. You’re asking a human to interact in that space, even with the support of technology. The human experience in how you help them come to conclusions about that content analysis and what it means is important.

One of the things I value so much about Candor is the fact that you underwrite very well. You have an insurance product. If the technology misses something along the way and a repurchase is required, you have insurance around that. What’s even more impressive is you’ve never had to use any of the insurance for any of the decisions you’ve made.

I picked this up when I was talking to your Founder, Tom Showalter. There’s so much more about what goes into this technology. It’s the people and I look at people. People are what make up your organization. You guys have a unique hiring practice and it starts with Tom, with whom you have a very unique relationship.

Yes, I do. If you hadn’t mentioned already, Tom is my father. I’ve not just been raised by him. I’ve had the pleasure of working with him on and off throughout my entire career. While he has been one of the more demanding bosses I’ve ever had, I’ve learned tremendous amounts from him. One of the things I’ve learned is the way he views hiring people and what he’s looking for. That has inadvertently permeated our organization.

Everyone that’s been hired into Candor has had an interview with Tom. Nobody gets hired without talking to him. You picked up in our last interview that Tom also talks to everyone in the company every week. We’re getting close to 80 people and we still have a team meeting every week. Everybody gets to chat with Tom.

This is what I found most interesting, Marc. Some of the questions that he ends up asking people are not normal questions. Go into that a little bit. Give us some insights into what those meetings or those questions sound like.

One of the things he’s always interested in is if somebody considers themselves a pathfinder, a problem solver or an execution person because you need all three but they perform differently under different circumstances. Depending on what problem you’re going to put them on, each of those different personas may or may not be appropriate. He always tries to understand when he is hiring someone, which one they are and what role they’re going into because that might impact their effectiveness.

What Tom is a huge fan of is putting people into positions where they will be able to leverage their skills most effectively and have the greatest chance for success. Also, growth because it is important. That’s another thing he’s a big believer in. He wants everyone in the company to have a path of continued growth and development, both in their hard skills and soft skills. Those are some of the ways you might phrase it.

 

We’ve also had some interesting promotional paths of folks within the company who’ve learned different skillsets and moved into different roles. It’s been incredibly encouraging to see but it’s an unusual approach to interviewing. We also take an approach where each interviewer is focusing on something a little bit different. We communicate a lot so that we try to get a holistic picture of this person. It’s not only what their skillset is and whether can they do this job but if this company is right for them.

We’re still in a high growth phase. We have to be very flexible because we’re evaluating the market that we’re in, the opportunity and making sure that we’re heading in the right direction. Sometimes we have to adjust our course. People need to be interested in being in that place. If they’re not, they might not be happy. This might not be the best place for them.

One of the things that Tom said to me in my very first interview with him was, “I come out of NASA. I’ve worked at NASA.” His background is so fascinating and how he helped select which fighter pilots had the greatest probability of being successful in the military as a pilot. It went from there into NASA, which one of the astronauts would have the greatest probability of being successful there.

Having predictable outcomes and looking at people is one of the things he specialized in and it was fascinating. The first statement he ever made that was so profound was, “Everyone thinks being a rocket scientist is complicated.” Sara, one of the things that impress me is the diversity of talent and skills inside your company. How do you get all of this to collaboratively work in a way to produce the quality product that you have?

For one thing, we have to. There is no way to build our product without the input of all these different kinds of experts that we have. Part of that is solved in the hiring process. We hire people who collaborate well and who work autonomously but also take responsibility well. We have a thing called a Pod, which is a certain kind of cross-functional team. They’re like a bunch of little SWAT teams. Each has their area of expertise and they do this very concentrated divide and conquer in terms of the way that they work together.

That has been an incredibly productive unit that turns out very high-quality products very quickly. We have the Pods but the other thing is when we’re solving problems, it’s not only around the development of the product but about the company at large. What market should we go into? What new products should we make? How should we sell it? When we’re tackling those questions, we also use cross-functional teams and we have these collaborative sessions where we do pathfinding.

Before when Tom hires, he says that he needs pathfinders, problem solvers and people who do execution. However, the good news is that pathfinding is a good collaborative activity for all kinds of people to be on because you’re trying to make sure you understand the broadness of all the issues affecting the problem you’re going to solve before you hone in on what you think the solution is.

Pathfinding is a good collaborative activity for all kinds of people to be on because you're trying to make sure you understand the broadness of all the issues affecting the problem you're going to solve before you hone in on what you think the… Share on X

If you’re too quick to try and jump to the solution, you could miss important information about what path you’re going down. We do a lot of pathfinding as a group, which I love because it means I get to have in-depth conversations with our underwriters, testers, engineers and sales and client success teams. I get to know all these groups and their point of view, experience and knowledge. They educate me.

It’s a collaborative process where everyone ends up educating each other. We figure out what the best path to go down is, get into execution mode at that point and get it done. I find it to be exciting and interesting. It gives everyone a voice too. It’s like the democratization of ideas. If all the ideas in our company had to come from the top down, we would be nowhere. They’ve got to come from everyone in the company.

It is easier said than done.

Sara, I want to copy that. Can I use the democratization of ideas? I like that.

Absolutely. I’m pretty sure I probably heard it somewhere else but please do.

I feel like listening to you that your father could be my brother from another mother because the whole concept he lays out on how he deals with people and the questions he asked them are some of the things I’m putting in a book I’m doing. It’s amazing to hear confirmation of an approach I’ve taken on something successful in my many years in management. To be acknowledged that I’m doing the right thing by somebody in the technology field makes me more excited.

I’m glad to hear it.

Seeing how this ties together, one of the things that I look at is the complexities of human beings. I love looking at personality assessments, the various kinds that are there, how they affect the decisions we make and where should we be putting people. There are a couple of tests that I can’t wait to talk to you and Tom about when we’re sitting together again. I want to turn on the mic and record it again like we did when Tom and I were together.

It’s like taking the six working geniuses. If you understand that, Google that. Take a look at Patrick Lencioni’s The 6 Types of Working Genius. Overlay that on top of what Dr. Berkman did with the Berkman assessment. He measured where we go under stress and how our behavior and our decisions go and change when we’re in stress mode.

There are four basic modes. One is where our interests and preferences are, what our usual style is, our needs and then where we go under stress. All of those are like a Rubik’s cube and it makes for the complexity within a human. That’s what goes in and much of which you are starting to codify in a way the complexities of human nature and the predictability of a simple thing like, “Are they going to stay in the home? Are they going to make these payments,” and do so in a manner that’s going to be a good investment for someone down the road.

You have done more work so far in humanizing the technology process and probably most companies have done it in their whole body. You’ve done it with your little finger and that’s amazing.

Thank you, Marc. That means a lot to us because we are trying to be partners, not vendors.

It comes through when I start talking with you, Tom, Booker and the whole organization. I can’t wait to know more. Through your marketing with Sharon and the whole group, this is something special that we’re witnessing here in the industry. How have you begun to navigate? Can you give us insights when you’re navigating through some of the complexities of the mortgage industry? How have you done that through technology?

We use something called Expert System. It’s a form of artificial intelligence where you are programming into the machine the critical thinking of an expert. This is different from purely a rules engine. The challenge with rules engines is that once you take a branch of a certain path, you’re on it. That’s your branch. You can’t then switch over to another one. It’s very linear. You have to know the full length of the path before you build it so you have a limited number of outcomes.

What we’ve been able to do is layer critical thinking in the machine so that it can evaluate a picture holistically. It can see if there’s something unusual on a bank statement over here in terms of a large deposit or something moving over here, then does this other piece of information on the application make sense anymore? It doesn’t have to think through the problem linearly. That’s one of the things we’ve been able to do.

The other thing is that the machine is constantly deepening its problem-solving capabilities. We have a thing called a pivot point, which is how we measure the thinking ability of CogniTech. We describe a pivot point as whenever the machine encounters an anomaly, it has to resolve it. To date, it’s over 60,000 pivots are in the system. It grows at about 1,000 a month.

Every time it does that, what it means is it can handle a more complex scenario or do even more cross-checking of, “Something over here might be related to something over here. I want to do the doesn’t make sense check.” There’s underwriting to the guidelines, which is very important but the other thing that human underwriters do that is hard for folks to quantify is they do a lot of what we call beyond the guidelines work. It’s these checks around, “Do certain things make sense? Does it make sense that you live over 1,000 miles from your employer if I don’t know if you’re a remote worker?” The human underwriter catches that, most machine solutions do not but Candor does. That’s been our approach.

You have broken the code on something that most technology companies haven’t. You have not left out the human perception of that and human concept as you built your technology. The very idea that you’ve got so many pivot points has been handled inside an analytical system. If you take a mere fraction of those and think if you were depending on humans to do it, where you’d be now? It’d be mighty tough and a very complicated decision.

What you’ve done is something that is going to bring value not only to what you’re doing but multitudes of other possibilities inside the space. I also see some servicing relative intuitive things that would be very great for you to get involved within your business. I’ve been fascinated to listen to this. You’re so on point with so many things that are going to make a difference in our industry. With the kind of pedigree you got, I’m excited about what you’re going to go through in the future because you’ve taken in the human aspect and that’s not usually done this way.

Which opens up the question, what is the vision of Candor? This is the part in which it starts getting exciting. Your background, Digital Risk, gives us some insights into it. Explain that and how you’re carrying that into Candor.

Having been at Digital Risk, I got to learn a lot about how loans perform when they have defects. First of all, we had hundreds of thousands of loans to use for analysis. We ended up doing a lot of predictive analytics. I helped Tom build what some people may have heard of called Veritas, which was a decision platform that could predict the behavior or the performance of a loan down to a very high level of accuracy and with a lot of nuances. First of all, “Will it perform or not,” but then if it doesn’t perform, “What does the solution look like?” Resolving a defaulting loan requires very different solutions depending on the situation and your objectives.

There are all different kinds of data science I can talk about. I can talk about something called optimization as a different objective function. There’s something called segmentation analysis. We find it very useful for understanding different groups of humans and their behavior but that’s the kind of stuff I got to play with there. The beauty about being at Candor is we have machines performing underwrites so we have even more granular data available about the underwriting.

We call what we collect off the loan during the underwriting of the loan DNA. It was Watson and Crick who discovered DNA, the double helix. They stole that discovery. That’s what’s commonly referred to. When the double helix and DNA were discovered, we could finally start analyzing what made humans and it was always there. It’s just that nobody knew about it. You can test your genes for all kinds of stuff and make decisions about what you should do based on that information.

This data has always existed. It was just never captured about the loan. The underwriter would know it. The underwriter would have a very detailed idea of the different income, employment sources and asset breakdown but as soon as he was finished working on the file, all of his notes and all of his work and due diligence, where did it go? Maybe it’s on his laptop somewhere, a home computer or a scratch pad but it’s not in the loan file and the tape.

You’re getting to the real value because you talked about system learning. I would like to expand on this. Where does this take us? Are we going to get so smart that we’re not going to need underwriters? I don’t think that’s the case.

I don’t think you do but you’ll be able to optimize the process. It’s a broad term but what optimization will let you do is take a particular processor activity and pick an objective function, which is, “What is the thing I’m trying to improve?” You tell the machine, “This is the thing I’m trying to improve.” These are all the inputs to the process that you can massage to make that thing better but here are all the rules and the boundaries about how far you can push these other factors.

You’re telling the machine these are the levers you can pull. This is how far you can pull them and maybe which ones you can even use together because you can’t do all of them but you’ve got to make this number over here as best as it can be. That number could be the cost. You could be trying to minimize the cost of making the loan as much as possible and here are all the different things you can do to the loan to make that happen.

Otherwise, you can say, “I’m trying to minimize the speed and make it as short as possible.” You could be saying, “I’m trying to minimize the likelihood of the performance of the loan and the borrower’s ability to stay in it.” That’s a very different objective function. The outputs based on those different functions will be very different in terms of what you do to a loan.

When you have all this data, you eventually could have a world where as soon as somebody starts filling out their loan application and providing information about themselves and information about what it is they’re trying to accomplish. You take all that information. You take information about what the lender needs to accomplish and you select some objective functions. It comes back and tells you, “This is the best kind of loan to make with these features and parameters to meet this borrower’s goals or the lender’s goals.” That’s what you can do with optimization.

 

 

Let me ask a question. I don’t know the details of your system but when the system underwrites a loan, can it complete a loan 100% on underwriting? If it finds exceptions, what is the agreeability quotient? Meaning when an underwriter reviews those exceptions that they agree with what the system came up with or they override what was said and improve the system. Those are things that you, being a potential user should be thinking about.

The platform is able to do the credit file analysis portion of the underwrite and any kind of wage earner. About 80% of self-employed, there are a few things it doesn’t yet read. For example, it can’t read a divorce decree just yet. There are little pockets of things where the system will simply say, “This is everything that I have done and substantiated. I can see that there’s this other piece here that I cannot do. I’m issuing a condition for the human to finish the work here.” That’s what it’ll do.

To your question about what if they don’t agree with what Candor Technology says, that’s always a lender’s choice. We like to say that we aren’t the ones making the lending decision. We’re telling you if it qualifies and what’s possible. It is entirely possible that someone would look at our output and say, “I don’t agree with this condition. I’m very comfortable with handling it the way I want to handle it and that is their right to do that.”

 

The impact that has is when Candor says that it warranties the work. If it does, we can’t warranty something we didn’t do. If they decide not to follow our recommendation and choose their own, which happens and there are plenty of reasonable scenarios when that happens, then that’s something we wouldn’t be able to warranty. Other than that, it shouldn’t disrupt the loan-making process.

I certainly understand that. It sounds like the way to handle it.

It seems to be amenable to our clients. We’re trying to support them but we’re also trying not to block them.

There are so many things that we talked about when we were at your open house at the National Conference. We got some insights into where you’re going but it’s even gone further in that or our pre-call, I got more insights into where you’re going. Give us some vision of what can we anticipate Candor doing for us and our industry.

The good news is that our ability to perform an underwrite with the machine is highly portable. There’s no reason that it has to stay in the fulfillment space. We’re doing a POC to bring it into the POS. We’ll be having some official announcements about that before the end of 2022 and then hopefully some performance information about how it’s going in the early parts of 2023 Q1. We have also been using this capability in the post-close and pre-fund space.

We have several beta testers but we are also talking to a lot of other folks that work in that space about how this could provide the same kind of lift that it has in the fulfillment space. We have a couple of other things in the works from our data and analytics division, which is my division in terms of some ability to report on the trends that we have seen on the loans going through our system. We’re going to have some interesting data to report, a look at the past year and some predictive models that are going to be coming very soon.

The predictive models are getting exciting as you start looking forward to the value of securitization and where things could go with how you pull certain loans together. The implications of where they could go get exciting.

The great thing is there’s predictive analytics that could be used at the front end of the process and the back end throughout.

We’ve always said when we were originations and most originators say, “If you’re going to give a no, give it soon.” Don’t have a lingering drug out no because that’s what frustrates everyone. Did not have the facts so you could make a decision earlier on? The sooner we could deliver a no, it is going to create a positive user experience by and large.

It’s to the extent that you can integrate this into a good point POS even or at least the LOS section where you’re going through the data and collecting the data. To be able to make that decision sooner is some of the benefits of this. I see Candor being an end-to-end solution. Let’s talk about loan servicing.

Marc Helm is an expert in the servicing space so we pigeonhole him with this show because he’s so well-known in that space. Some of the implications for loan servicing is a loan going into delinquency. You could predict that to a certain degree but then the solution of how to get that loan back on track has been left to the specialty servicers. You’re saying that you have the ability to go in, if I understand the applications, of being able to solve that. Is that correct?

One of our goals is to solve that. We’ve solved it before. If you want to look up a research paper that Tom Showalter and I published in 2011 in the Journal of Structured Finance, it’s called What Do I Do With This Loan? It will explain to you the technology needed to solve this problem. The great thing is that the technology’s public. The algorithms necessary to make this haven’t changed in 100 years but the data inputs have gotten better with Candor. We’re going to be coming after this problem again.

 

Marc, that gets exciting for you. One of the things that are near and dear to your heart is loan servicing.

That’s for sure, David. Having been an underwriter for a portion of my career, I would have to tell you that one of the things that generally speaking underwriters and people that do problem-solving lack self-confidence. To have some modeling and predictiveness that can be done to assist them is going to be great.

Also, it gives them a solution that they can apply or not. A technical person and a real person agree on the same things or work out a different system to make things work. That’s exciting to me and it should be exciting for everybody in every aspect of the industry that this type of technology can be used.

How can people learn more? What’s the best place to go? Obviously, your website but where else can they go? Whom should they contact?

Visiting our website, CandorTechnology.com is a place to go.

Also, I want to give a shout-out to Earl Booker whom we had on the show the last time. You have such an amazing team there. It’s an honor to have you as a sponsor of the show but more importantly, all that you’re doing for the industry and where this could go is starting to get exciting. I am thrilled to have you here. Thank you, Sara. I appreciate you being here.

Thanks so much for having me again, David.

Marc, thanks for joining.

Thank you, Sara.

Thanks, Marc. It’s good to meet you.

It’s good to meet you too.

 

Important Links

• Candor
• CogniTech
• The 6 Types of Working Genius
• Earl Booker – Past Episode
• https://www.LinkedIn.com/in/Sara-Knochel-7360a84/

 

About Sara Knochel

Sara Knochel has spent the last twenty years delivering data and analytics solutions that transform clients’ business models. She received a Computer Science degree from the University of California, Irvine, and began her data science career analyzing consumer credit. She then graduated to residential mortgage while working at Loan Performance, CoreLogic, and Digital Risk. Sara pursued her MBA at Emory University in Atlanta, GA, after which she launched her consulting career, building a data and analytics practice serving multiple industries in the Atlanta market, including Candor Technology. In 2022 Sara joined Candor full-time as the CEO of their new data and analytics business where she is leveraging Candor’s unique and proprietary data assets to deliver transformational products to the mortgage industry.