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NEW YORK — Varo Bank is upgrading its underwriting systems to develop deeper customer relationships and grow. The $486 million digital bank was one of the first fintechs to gain a banking license in 2020 from regulators and has tried all forms of underwriting to grow while still maintaining high credit standards, Das Mohandas, chief […]
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KeyBank is continuing its AI and gen AI development pipeline after seeing positive effects on its operations. “We have roughly about 40 proofs of concept [POCs] across KeyBank that we are evaluating right now,” Ken Gavrity, head of commercial banking, told Bank Automation News. “You are going to see it across our business as those […]
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U.S. Bank is working on AI-driven solutions for its small and medium-sized business clients to improve their operations. “By no means is it as easy as just deploying AI into the ecosystem without taking into consideration some of the challenges it comes with,” Shruti Patel, chief product officer for business banking, told Bank Automation News. […]
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When Jason Sorochinsky began transforming the Marlborough, Massachusetts-based Digital Federal Credit Union’s mortgage origination process in 2019, he knew that always offering the lowest rates wasn’t feasible. But with the help of several
“Our value proposition really came down to one sentence, which is, we want to be known for speed and service using digital tools and technology,” said Sorochinsky, who is head of mortgage lending for the $12.1 billion-asset DCU.
DCU officially launched the self-service mortgage portal in 2022 after spending a year piloting the platform to fine tune the processes. The digital lending platform, built by the New Jersey software firm Blue Sage Solutions, capitalizes on the credit union’s “consumer direct” model by allowing potential borrowers to apply for mortgages and home equity loans and refinance existing loans, without the need for a staff member.
After selecting which of the three products they want to apply for, and inputting property details like zip code, anticipated down payment and estimated purchase price, consumers can see the maximum amount they could bid on a property and choose which rates and terms best fit their needs. This phase also allows members to electronically verify their income, employment and other owned assets to support their eligibility.
During the application process, borrowers concerned about market volatility can lock in their rate using OptimalBlue’s rate lock API, for 15 to 90 days.
Next, DCU will use Blue Sage’s integration with the mortgage fintech Optimal Blue’s product and pricing engine to order credit reports, validate loan pricing, run the file through Fannie Mae and Freddie Mac and conduct other calculations. A secondary API connection with the information services firm ClosingCorp provides added support by calculating application and appraisal fees as well as generating disclosure agreements for the member to sign.
Members will receive emails or text messages prompting them to proceed to the next steps in DCU’s mortgage portal and sign the necessary forms after the initial application is submitted. Once the fees are paid, orders are put in for standard items including title insurance, appraisals and flood certificates, then a second round of confirmation documents are sent back to the applicant for signing.
After signing all the necessary forms, the file is submitted to the underwriting department for further processing — which DCU says can be done in as little as 30 minutes and without the need for a credit union representative. Two-way communication with a DCU mortgage lending officer, processor or closer via a chat function, as well as informational videos, are available to help the member address any issues.
“It doesn’t matter what the forces are, recession or high rates or low inventory, we’re able to still be successful because we’re focusing on speed and service using digital tools and technology,” Sorochinsky said. With the addition of the self-service portal, DCU was able to boost lending from roughly $1 billion in mortgage loans when talks began in 2019, to $1.6 billion in 2023.
DCU is among a host of other institutions that have added new technologies in the hopes of furthering membership growth and increasing loan volume.
The $18.5 billion-asset
While the
Debra Shultz, vice president of mortgage lending at CrossCountry Mortgage, said that
“Today, borrowers understand that real estate is a great investment [as] it gives them the freedom to create the home of their dreams, take advantage of tax advantages and build wealth over time,” Shultz said. “The opportunity to refinance their loan into a lower rate in the next 1-2 years is a reality.”
Experts with Cornerstone Advisors and Datos Insights underscored the importance of proper due diligence when vetting both third-party firms and the products they bring to the table, but equally highlighted the value of exploring new technology.
“This sounds like a no-brainer but despite having system capabilities, many underwriters still manually pull credit and calculate ratios manually,” said Eric Weikart, partner at Cornerstone Advisors. “Sometimes, this is due to system setup issues but many times it’s because they have always done it that way and they aren’t willing to change.”
Automation is an important characteristic for underwriting programs to be truly effective, but only with “comprehensive risk assessment, regulatory compliance and clear guidelines” also put in place, said Stewart Watterson, strategic advisor for Datos Insights.
As
“Compared to 20 or 30 years ago, borrowers have a much higher expectation of speed to approval and closing along with desire to have a tech enabled process supported by knowledgeable, professional loan officers and operations personnel,” said Christy Soukhamneut, chief lending officer for the $4 billion-asset University Federal Credit Union in Austin. “We are actively implementing mortgage technology that is user friendly and intuitive so that our sales teams can focus on the member and referral partner experience.”
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A day after
On March 8, the
While not able to speak to the specifics of the posting, nor about any possible actions the regulator might take, the Community Home Lenders of America “is thrilled that they’re jumping into this,” Scott Olson, its executive director, said in an interview.
“We’ve actually used this phrase [junk fees] ourselves a couple of years or so ago” he said in regards to click fees lenders are charged by third party vendors, which are passed on to consumers.
Others in the industry had a hard time understanding where the CFPB was coming from.
“The CFPB’s blog post is baffling and reveals little understanding of how the mortgage market works or awareness of its own regulations that provide for full fee transparency and limits on what can be charged,” Bob Broeksmit, president and CEO of the Mortgage Bankers Association, said in a lengthy statement.
“The fees mentioned are clearly disclosed to borrowers well before a home purchase on forms developed and prescribed by the Dodd-Frank Act and the CFPB itself,” he added, referring to the
The other form – the closing disclosure presented at the end of the process – must be within certain tolerances of the data provided on the loan estimate.
“In 2020, the CFPB issued a report
But in Olson’s view, “transparency is not the same as competition.”
The CHLA has been supportive of
“We think that opening up the line of sight on some of these things is reasonable where there really is not competition,” Olson said.
CHLA plans to “comment vigorously” to the CFPB, he continued, adding that it has done so regarding competition and fees charges in the not-so-distant past, particularly
As far back as 2003, if not even earlier, the government has had so-called mortgage junk fees in its crosshairs. Mel Martinez, Department of Housing and Urban Development secretary under President George W. Bush, said in a speech before the National Community Reinvestment Coalition almost exactly 11 years ago that members of Congress did not understand that reform proposal would help consumers understand the mortgage process and the costs involved so
The CFPB, in its recent post, took its own shot at the lender policy portion of title insurance, saying the borrower has no control or options.
“Instead of paying this fee themselves, lenders make borrowers pay the cost,” said the blog posting authored by Julie Margetta Morgan, associate director. “The amount that borrowers pay for lender’s title insurance is often much greater than the risk.”
The CHLA has been supportive of
“We think that opening up the line of sight on some of these things is reasonable where there really is not competition,” Olson said.
The American Land Title Association issued commentary on the CFPB blog.
“Reform of mortgage closing costs is unnecessary,” the ALTA response said. “The
Credit reports also were specifically mentioned as a problem area in the CFPB posting, claiming the business lacks competition and choice.
“The CFPB has heard
CFPB is also looking for consumer comment on
“We are paying particular attention to the recent rise in discount points,” the posting said. “A higher percentage of borrowers reported paying discount points in 2022 than any other years since this data point was first reported in 2018.”
The agency said 50.2% of home purchase borrowers paid some discount points in 2022, with the median dollar amount being $2,370, up from 32.1% and $1,225 one year earlier.
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For the first time since going public in 2022, digital bank Dave posted a profitable quarter. Dave reported fourth-quarter adjusted EBIDTA of $10 million compared to a loss of $13 million in Q4 2022, according to the bank’s earnings release today. The $391 million company’s growth is due to continued investment in advanced technology like […]
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Eight in 10 credit union execs are looking to AI to enhance their underwriting capabilities.
Credit union executives “said they would like to deploy AI within underwriting because of the impact it would have on their balance sheets as well as their members,” de Vere tells Bank Automation News on this episode of “The Buzz” podcast.
Zest AI’s underwriting technology allows financial institutions to assess loan decisions using richer data and insights through AI, de Vere said, noting that members “are more than a number.”
With the technology, FIs can lend to consumers in a smart, inclusive and efficient way, he said.
Zest AI was founded in 2009 and has bank and credit union clients including $1.2 billion Credit Union West, $1.3 billion First Service Credit Union and $4.7 billion Truliant Federal Credit Union.
Listen as de Vere tells how credit unions are improving the underwriting process with AI.
Get ready for the Bank Automation Summit U.S. 2024 in Nashville on March 18-19! Discover the latest advancements in AI and automation in banking. Register now.
The following is a transcript generated by AI technology that has been lightly edited but still contains errors.
Whitney McDonald 0:02
Hello and welcome to the buzz of bank automation news podcast. My name is Whitney McDonald and I’m the editor of bank automation News. Today is January 4 2024. Happy New Year. Joining me is Sai CEO Mike de Vere. He is here to discuss how AI is improving the decisioning and lending experience for financial institutions, as financial institutions look to serve their entire communities and lean on automation to make smart lending decisions. Prior to this day, Mike spent more than four years at Nielsen served on Google Surveys advisory board, and was the CFO at radius financial join me in welcoming Mike.
Mike de Vere 0:38
Well, thanks for having me, Whitney, super excited to be here. So Mike, de Vere CEO of zest AI, I have been, you know, perfecting the translation of data into insights over the last few decades. I’m here at CES AI, we’re our technology deal delivers and automates underwriting with more accurate and inclusive lending insights. And so just to unpack that, there’s a lot of discussion around automation, certainly with this economy around efficiency. So definitely topical, but foundational to automate your underwriting is you have to have better insights going into the system. And that’s where this more accurate approach to assessing credit comes in. That’s applying better math called AI. And so you can’t just have this more accurate inclusive lending insight. Because it also you have to make sure that you’re serving all your members and all your customer customers. And that’s where inclusivity comes in. And so we have been solving that problem for the last roughly a decade and a half. And excited to share more about the adventure that we’ve been on. It’s just
Whitney McDonald 1:42
great. Well, we are definitely excited to hear more. So thanks again for being here. Let’s start here with kind of a market update tell us about the current lending market. And then we can kind of get into how credit unions can really navigate this space as we close out 2023 and get into 2024.
Mike de Vere 2:01
Well, if I think about the last 100 or so conversations I’ve had with credit union executives a consistent theme surrounds were lent out, you know, in this economy with rising interest rates demand going down because of these rising interest rates. And so many credit unions find themselves in a position where they have very little to support their communities. And what they’re faced with is because of the tools that exist today, there, they’re inaccurate. Their face was really only lending to a small segment of the population, you’re a tear paper. And so, you know, from an economy perspective, certainly there’s a lot of focus in on lending. Really what people are asking us for help with is around decreasing charge offs, improving yield, being able to serve your entire community, not just those at the top socio economic bracket.
Whitney McDonald 2:57
Now, when it comes to being able to accomplish exactly what you were just saying, let’s kind of get into how technology fits into this. And more specifically, we can’t really have conversations right now with talking through AI. So how can credit unions really optimize look to technology, technology, optimize automation, improve underwriting using AI right now?
Mike de Vere 3:20
Well, I think that there’s three pillars that that we work with credit unions on smart, inclusive and efficient. And so smart is, as it says, which is, the current credit system is failing America, whether you’re talking about a good a good economy or a struggling economy, it’s failing America, because it’s only serving parts of it, if you’d segments of the population are left out whether they be thin file, there’s significant segments of the population that are where there’s bias and discrimination in the end. And so, this idea of smart means, we’re appending to the current credit system, which uses roughly 20 variables to assess if we should give a person alone, the current industry scores that are out there, and it tries to boil an individual down to a number. But what we know is that members and customers are more than a number. And so you’d have to open up the aperture and consume more information. And that’s where AI comes in and enables a credit union or a bank of any size to accurately and smartly assess if they should issue that loan. The second pillar that was around inclusion, that’s really where purpose comes in. Because it’s one thing to be more accurate and drive your balance sheet but it’s the second is fulfilling your mission and being able to serve your entire community that you’re within. And that’s why being purposeful about the models that you built to ensure that they’re inclusive and then finally, around automation. Listen, there is such a huge business case right now, for this third pillar on efficiency, where you’re taking this more accurate inclusive Linda inside, but now you’re looking at the the human policies that get overlaid on top and the manual review that gets overlaid on top. So let me give you an example. The average credit union automates their decisions roughly 20% of the time. Now, the challenge with that wouldn’t be is that the average credit union number one, eight out of 10, roughly one a decision in less than a second. And so four out of five are getting kicked out for manual review. You’re really dissatisfying, your customer, that’s a problem. And so really being thoughtful not only about the technology, but around your policies and overlays, is really important. And so what we find is that the normal credit union might have 20 policy overlays, on top of this industry score, which you know, for me is really like duct tape and spit and chewing gum and in dirt, but you’re just trying to put on top of this failing industry score. Well, when you use AI that’s more accurate and more inclusive, you actually have to address those policies, what you find is that up to roughly 20 to 25, probably 10 of them, you don’t even need, because the signals that you’re trying to measure are already within the model itself. So you can dump those out, that manual step is gone. The second bucket is around, well, there’s a lot of policies that frankly, have no signal whatsoever. You know, it’s I love hearing, we’ve had that in place for the last 50 years, the old clo Chief Lending Officer has had that in place. And I frankly, don’t know why it’s there. And so we kick those out. And then there’s this this last bucket around really optimizing policies, so you end up with four or five. And the net result, if you do that implement AI driven underwriting is you should be able to audit a decision 80 to 90%, for those loan applications that come across your desk, which is what customers want. And from an efficiency perspective, dear gosh, probably our poster child in efficiency was able to eliminate two thirds of the resources for underwriting through automation. That’s a heck of an ROI.
Whitney McDonald 7:08
Yeah, I’d say that’s huge. And throughout the year, it’s been a consistent theme across the industry where we’re focused on efficiency, we’re pulling back on costs, where can we automate? Where can we invest in technology? So that leads me to the next question, I know you talked about the three pillars where technology can fit in kind of throughout the institution? How do we really approach this technology strategy? If you’re a credit union? How do you how do you prioritize those must haves? Where do you start, we
Mike de Vere 7:36
did a study of credit union executives and eight out of 10, asked for, and they said that they’d like to deploy AI with an underwriting because the impact that it could have on their balance sheet as well as their members. That to me, is a good starting point. And why do I say that? Because if you think about what a credit union or bank does, at its very core, it’s lending money. And so that foundation, if you get that, right, that cascades out to all of the other technology, things you may want to do as a business. But you got to get that right first. Imagine if you’re overlaying technology on a broken system, it’s a wasted effort, you have to start with a smarter brain at the core of the credit union or bank.
Whitney McDonald 8:20
Now, when it comes to innovation within ZX sai we can kind of get into your technology a bit here. What really are your credit union clients asking for I know that you just mentioned the survey that they’re asking for more AI within the decisioning. What is really driving that innovation within this AI, maybe a few things that you’ve you’ve got in the works or some products that you’ve got working on.
Mike de Vere 8:41
So we actually started solving the most difficult problem, which is how to safely and soundly underwrite a loan. So that’s the core. But now you can move up the customer journey and talk about pre screening or pre approvals, you could actually go down the customer journey and say, Now once I have an individual loan, well, now let me look at the health of the portfolio itself. And understand things like credit migration, you know, 18% of your portfolio was a paper, it’s migrating now it’s 22%. So you’re now skewing more towards higher paper. Within the analytics, you’re able to look at numbers that may be in distress, that are moving from an ATR all the way down to a C tier, and there’s an opportunity to engage them before they end up in collections. And so, from a technology perspective, when you’re asking that question of assessing credit, that’s where our technology really shines. And so pre screen pre approval, we look at the underwriting question itself, as well as portfolio management. Now, I would be remiss if I didn’t talk about some of the significant innovations that we’ve had around fraud and detecting fraud. And so it always starts first with us understand that every customer has their own unique set of issues and so one fraud solution doesn’t fit all. And so for example, our partner So Equifax have a phenomenal fraud solution. But sometimes that might not be the right fit. And it might be that you could use AI. So zeste uses AI to detect fraud, and identify early default and things of that nature. And so it really depends on the individual credit union and their needs and the type of fraud that they’re experiencing. And so I think if I were to say a very, a very consistent theme across each of our offerings, is that we tailor them specifically thoughtfully to that credit union or bank understanding that one size doesn’t fit all.
Whitney McDonald 10:36
Now, speaking of that, one size doesn’t fit all approach. I’m gonna go off script a little bit here. But when it does, when you do get approached by a credit union, or a credit union is interested in Sai, what are those conversations usually looks like? What are they asking for? What are what are you really solving for? I mean, other than the obvious, but what are those questions kind of look like when you’re in those early stages?
Mike de Vere 10:58
Sure. So it depends on what’s going on with the economy. So today, it’s really leading with things like my charge offs are starting to drift up, can you help. And what we know at best is that we can reduce charge offs, roughly 32%, everyone’s across, if you look at the NCAA findings, they’re all going up across the board. And so imagine if you could bend that curve down. The second area is around yield. And so most credit unions are focusing in right now only on their a paper, but there’s almost no yield there. And so what better way to generate capital than having increasing your yield. And then there’s the topic of inclusion, I want to make sure that I’m assuming serving all of my members and 10s of millions of Americans are left out of the current credit system, because of the bias that’s associated within the system. And so there’s a significant opportunity there. And then finally, it’s really around efficiency is that weren’t tough economic times right now, where we’re going to invest is where it makes us stronger and smarter with our lending. And so it really comes down to efficiency.
Whitney McDonald 12:08
And I’m guessing those those topics that you just disclosed that were that were the questions that come about is that kind of helping set up your your plan or your roadmap for 2024, and what your focus is.
Mike de Vere 12:21
So our current product offering actually addresses that. So where we’re expanding in 24, is, first off looking at additional consumer verticals, additional, commercial, vertical, so we’re addressing different types of loans. We’re also going within the customer journey and automating various steps in the process. And so imagine if you’re a large credit union on the West Coast, and you have this great technology company called SSDI, that you work with, and it automates the credit decision in less than a second. But then the underwriter has to manually turn around and do a fraud check. And it takes five minutes while automation falls apart. And so we’ve launched a product called zest connect, where we work with credit unions, and their ability to not only from an underwriting perspective, but identify those other manual steps in the process that can be automated, whether it be through native integration, robotic process automation, what have you, we’re really trying to streamline that customer journey.
Whitney McDonald 13:24
Yeah, that definitely makes sense. And thanks for kind of giving us a look ahead into the next year. Now, as we, as we kind of wrap things up a little bit. What would one piece of advice be for credit unions that are implementing technology that are looking to automate these processes? I know that you just kind of gave that that great example of automate the whole process don’t get stuck after the first piece of the automation puzzle. But what would you give? What advice would you give when implementing this technology kind of getting into the next year? I mean, cost, of course, is one, one area that has to be considered but but what’s the what’s one piece that you would give to a credit union that’s looking into these automation and AI technologies?
Mike de Vere 14:08
Well, so for me, it’s always is the juice worth the squeeze? So there are many executives I run across that have just fallen in love with the technology. I get it. We’re all emotional buyers. But there could be this rational component. And if you have a technology provider, like SAS AI, whose suggestion you can have a 10 times return on your investment within the first year. That’s going to be a pretty smart bet. And so I would encourage people when assessing what technology to prioritize is to ask yourself, the question is the juice worth the squeeze? The second piece is really the people component is that I see whether I was at sastra. And in my past life technology initiatives will fall apart because they forget change management in the human component, that this is a big change you’ll have if you’re talking about underwriting And you’ll have people who’ve been underwriting the same way for three, four decades. And so their willingness to change is not quite there. And so it’s really going to be important for an organization when implementing technology that they understand the role of change management. But they also understand there’s a human impact. And so there needs to be that software approach going forward.
Whitney McDonald 15:25
Now, lastly, as we look into the new year, What trends are you following for 2024?
Mike de Vere 15:31
If I look at 2024, and ahead, I think, you know, one of the big trends that I want to call out is certainly technologies is going to play a big role, and day to day business, but technology and the intersection between that and purpose is going to become even more important as we look ahead. And so purpose is being mindful about when I implement a technology, what outcome am I expecting? And so when I build an AI underwriting model, what outcome Am I looking for? Am I looking for better economics? Well, that’s that certainly is purposeful and how you build it. But there could also be a secondary thing on we also have a mission to serve our community. And so certainly with a credit union, that is core to who they are. And so the question is, are you being purposeful about how you’re building the model to make sure that men and women get a fair shot. Different ethnic groups get a fair shot. And so you’ve got to be thoughtful about how you build the model. It is not just something that happens. It’s having technology and IP, around D biasing the model, and so that you’re able to fulfill your mission. In really lean
Whitney McDonald 16:46
You’ve been listening to The Buzz, a Bank Automation News podcast. Please follow us on LinkedIn. And as a reminder, you can rate this podcast on your platform of choice. Thank you for your time, and be sure to visit us at Bank automation news.com For more automation news,
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The Federal Housing Finance Agency moved forward on some of its proposed tweaks to rules related to the financial soundness of Fannie Mae and Freddie Mac with what it characterized as “minor modifications.”
Certain changes to guarantees on uniform mortgage-backed securities, apartment loan exposures on government-subsidized buildings and interest-only securities are part of the finalized Enterprise Regulatory Capital Framework the agency promised to deliver this year.
But not all proposed updates to the framework advanced. Notably, it withdrew one related to use of credit scores and reporting at the enterprises the FHFA oversees.
“FHFA currently is not adopting the proposed modification to the procedure for selecting a representative credit score for a single-family mortgage exposure when multiple credit scores have been submitted for at least one borrower,” the agency said.
The omission was prompted by mixed feedback about a plan to give mortgage companies the option to use two rather than the three credit reports when submitting loans to Fannie Mae and Freddie Mac.
In the proposal, the industry would have transitioned from using either the median of three scores from as many reports, or the lower of the number from two, to an average of either.
“FHFA proposed this modification to prevent a downward shift in representative credit scores under the current methodology once the enterprises require a minimum of two, rather than three, credit reports,” the agency explained.
While that aspect of the proposal had supporters who’ve studied it and determined it wouldn’t result in a material change for borrowers, others have raised questions about whether it could have some negative unintended consequences.
“In consideration of the delayed implementation date for the bimerge requirement and the ongoing public engagement related to credit scores, FHFA has determined to not adopt the proposed change to the calculation of representative credit scores at this time,” the agency said.
“FHFA may, in the future, finalize this aspect of the proposed rule,” it added.
The agency did move forward with part of the proposal that updates the score assumption to 680 for single-family mortgage exposures originated without a traditional debt-payment history.
Industry experts contacted at deadline were still reviewing the final rule’s nuances.
But one expressed hope advancing the overall capital framework would help the enterprises move toward a point where profits wouldn’t have to be swept to the Treasury, as they have been since conservatorship
“As one of the leading advocates in ending the profit sweep, CHLA commends FHFA for completing this rule making,” said Scott Olson, executive director of the Community Home Lenders of America, in an emailed statement.
“However, it’s important to remember that the main purpose of building capital is to enable Fannie and Freddie to aggressively fulfill their role of providing mortgage credit access to homeowners,” he added.
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Financial institutions are investing in AI and, as they do, they must consider application, talent and regulation.
Card issuing fintech Mission Lane has created an internal framework to help implement new technologies, including AI, head of engineering and technology Mike Lempner tells Bank Automation News on this episode of “The Buzz” podcast.
Mission Lane has a four-step framework when approaching new technology, he said:
Listen as Lempner discusses AI uses at the fintech, monitoring risk and maintaining compliance when implementing new technology throughout a financial institution.
The following is a transcript generated by AI technology that has been lightly edited but still contains errors.
Whitney McDonald 0:02
Hello and welcome to The Buzz, a bank automation news podcast. My name is Whitney McDonald and I’m the editor of bank automation News. Today is November 7 2023. Joining me is Mike Lempner. He is head of engineering and technology at FinTech mission lane. He’s here to discuss how to use the right type of AI and underwriting and identifying innovation and use cases for AI, all while approaching the technology with compliance at the forefront. He worked as a consultant before moving into the FinTech world and has been with Mission lane for about five years.
Mike Lempner 0:32
I’m Mike Lempner, I’m the head of our engineering and technology at mission lane. Been in the role where I’ve been leading our technology group and engineers to help build different technology solutions to support our customers and enable the growth of mission lane. I’ve been in that role for about five years prior to that mission Lane was actually spun off from another fin tech startup, and I was with them for about a year as an employee prior to that as a consultant. And prior to that time, I spent about 28 years in consulting consulting for a variety of different fortune 500 companies, startups, but mostly all in the financial services space.
Whitney McDonald 1:09
And maybe you could walk us through mission Lane give us a little background on what you guys do. Sure,
Mike Lempner 1:16
Mission lane is a FinTech that provides credit products to customers who are typically denied access to different financial services, largely in part due to their minimal credit history, as well as poor credit history in the past. For the most part, our core product that we offer right now is we have a credit card product that we offer to different customers.
Whitney McDonald 1:39
Well, thank you again for being here. And of course, with everything going on in the industry. Right now, we’re going to be talking about a topic that you just can’t seem to get away from, which is AI and more specifically ai ai regulation. Let’s let’s kind of set the scene here. First of all, I’d like to pass it over to you, Mike to first kind of set the scene on where AI regulation stands today and why this is an important conversation for us to have today.
Mike Lempner 2:08
Yeah, sounds good. As you mentioned, Whitney AI has been really all the the conversation for about the past year, since Chechi. Beatty, and others kind of came out with their capabilities. And I think as a result, regulators are looking at that and trying to figure out how do we catch up with that? How do we feel good about what what it does? What it provides? How does it change anything that we do currently today? And I think for the most part, you regulations are really stand the test of time, regardless of technology and data. But I think there’s always kind of the lens, okay, where we are today with technology, has anything changed where we are in terms of data sources, and what we’re using to kind of make decisions from a financial services standpoint is that also creating any kind of concerns and you’ve got different regulators who look at it, you’ve got some regulators who are looking at it from a consumer protection standpoint, others who are looking at it from the soundness of the banking industry, others who are looking at it from an antitrust standpoint, privacy is another, you know, big aspect of it and as well as Homeland Security. So there’s there’s different regulators looking at it in different ways and trying to understand and and try to stay as much ahead of it as they possibly can. And so I think a lot of times that they’re looking at things and trying to kind of look at the existing regulations, and understand are there adjustments that need to be made an example of that CFPB, I think recently provided some some comments and feedback related to adverse action notices, and how those are basically being generated in the light of artificial intelligence, as well as like new modeling capabilities, and including, like new data capabilities. So I think there’s there’s some specific things in many ways it doesn’t change the core regulatory need. But I do expect there’s going to be some fine tuning or adjustments that get me to the regulations to kind of put in place more more protections.
Whitney McDonald 4:10
Now, for this next question, you did give the example of looking at existing regulation, keeping all the different regulatory bodies in mind what already exists in the space? How else might financial institutions prepare for new AI regulation? What could that preparation look like? And what are you really hearing from your partners on that front?
Mike Lempner 4:33
Yeah, I think it’s, it’s not just specific to AI regulations. It’s really all regulations, and just kind of looking at the landscape of what’s happening. You know, where we are. I think the one thing that we know for sure is regulation changes will always happen and the they’re just a part of doing business and financial services. And so that need is not going away. So There are different privacy laws that are being put into place some, in some cases by different states. There’s other things, you know, as I mentioned with AI are emerging and growth, how do regulators feel comfortable with that as well? So I think in terms of preparing, just like you would with any regulatory activities going on, it’s important to have the right people within the organization involved in that in for us, that’s typically our legal team or risk team who are working both internally as well as getting external counsel, who will help us understand like, what are some of the current regulatory ideas that are out there being considered? How might that impact us as a business and we’re staying on top of it. And then as things materialize over time, we work to better understand that regulation, and then what it means for us, and then what do we need to do to be able to support it. So I think that’s a biggest part of it is getting the right people in the organization to stay on top of it know what’s currently happening, what might be happening in the future, leveraging external resources, as I mentioned, is they may have expertise in this area, and just staying on top of it so that you’re not surprised and then really kind of reacting to the situation.
Whitney McDonald 6:14
Now, as AI regulation does start coming down the pipeline, there’s definitely not been a a waiting period, when it comes to investing in AI implementing AI and innovating within AI. Maybe you can talk us through how you’re navigating all of those while keeping compliance in mind, ahead of further regulation that does come down. Yeah,
Mike Lempner 6:39
absolutely. The, you know, for for us in AI is is a really kind of broad kind of area. So it represents, you know, generative AI like chat GPT. It also involves machine learning and other statistical kinds of algorithms that can be applied. And we operate in a space where we’re taking on risk by giving people credit cards and credit. And so for us, there’s a core part of what we do the underwriting of credit. That is is challenging involves risk. And so for us, it’s important to have really good models that help us understand that risk and help us understand like who we want to give credit to. We’ve been ever since we got started, we’ve been using AI and machine learning quite a bit in our our models. For us, one of the important things is to really look at and where we may have many models that support our business. Some of them are credit underwriting models, some of them are fraud models, some of them may be other models, we have dozens of different models that we have is making sure that we’re applying the right AI technology to meet both the business needs, but also taking into account regulation. So as an example, for credit underwriting, it’s super important for us to be able to explain the outcomes of a given underwriting model to regulators as an example. And so if you’re using something like generative API, AI or chat GPT, where accuracy is not 100%. And there’s the concept of hallucinations. And while hallucinations might have been cool for a small group of people in the 60s, it’s not very cool when you talk about regulators and trying to explain why you made a financial decision to give somebody a credit card or not. So I think it’s really important for us to use the right type of AI and machine learning models for our credit underwriting decisions so that we do have the explainability have it. And we were very precise in terms of the outcome that we’re expecting, versus other types of models. And it could be marketing models, there could be, as I mentioned, fraud models or payments models that we may have as well that support our business. And there, we might be able to use more advanced modeling techniques to support that.
Whitney McDonald 8:57
No great examples. And I like what you said about explainability as well. I mean, that’s huge. And that comes up over and over again, when it does come to maintaining compliance while using AI. You can have it in so many different areas of an institution, but you need to explain the decisions it’s making, especially with what you’re doing with with the credit decisioning. I’m moving in to something that you had already mentioned a little bit about, but maybe we can get into this a little bit further. is prepping your team for AI investment implementation. I know that you mentioned having the right teams in place. How can financial institutions look to what you guys have done and maybe take away a best practice here? For really prepping your team? What do you need to have in place? How do you change that culture as AI as the AI ball keeps rolling?
Mike Lempner 9:52
Yeah, I think for us, it’s similar to what we do for any new or emerging technology in general. which is, you know, we’ve got a an overall kind of framework or process that we have like one is just identify the opportunity and the use cases. So we’re really understanding like, what are the business outcomes that we have? How can we apply technology like AI or additional data sources to solve for that particular business challenge or outcome. And then so that’s one is just having that inventory of where all the places that we could use it, then to like really looking at it and understanding the risks, as I mentioned, credit risk is one thing. And that we may want to have a certain approach to how we do that, versus marketing or fraud or other activities may have a slightly different risk profile. So understanding those things. And even when we talk about generative AI, for us, using it for internal use cases of engineers writing code and using it to help write the code is one area where it might be lower risk for us, or even in the operations space, where you’ve got customer service, who maybe we can automate a number of different functions. So I think understanding the use cases understanding the risks, then also having a governance model, and that is, I think, a combination of having a team of people that are cross functional to include legal risk, and and other members of the leadership team who can really look at it and say, here’s our plan. And what we would like to do with this technology, do we all feel comfortable moving forward? Do we fully understand the risk? Are we looking at it like holistically, then also, governance? Like for us, we already have model governance that we have for that really identify what are the models we have in place? What types of technology do we use? Do we feel good about that? What other kind of controls do we need to have in place. So I think having a good governance framework is another key piece of it. Investing in training is a another key thing to do. So particularly in the case of emerging generative AI capabilities, it’s fast evolving, it’s really important to kind of make sure that people just aren’t enamored by the technology, but really understanding it, understanding how it works, understanding the implications, there’s a difference to whether we’re going to use a public facing tool and provide data like Chet GPT, or whether we’re going to use internal AI platforms using our internal data, and use it, you know, for more proprietary purposes. So there’s a difference, I think, in many ways, and having people understand some of those differences and what we can do there, it’s important. I think, lastly, the other key thing from an overall approach standpoint, is to really iterate and start small, and get some of the experience on some of those low risk areas. In for us the low risk areas, like we’ve identified a number of different areas that we’ve already built out some solutions around customer service. And engineering, as I mentioned, you can use some of the tools to help write code, and it may not be the finished product, but it’s at least a first draft of code that you can, you can start with that. So you’re not basically starting with a blank sheet of paper.
Whitney McDonald 13:09
Yeah, and I mean, thank you for breaking out those those lower risk use cases that you can put in action today. I think we’ve seen a lot of examples lately of AI, that is an action that is able to be launched and used and leveraged today. Speaking of maybe more of a future look, generative AI was one thing that you had mentioned, but even beyond that, would just love to get your perspective on potential future use cases that that you’re excited about within AI, where regulation is headed. But however you want to take that future look, question of what’s coming for AI, whether in the near term, or near term or the long term? Sure.
Mike Lempner 13:53
Yeah, it’s I think it’s a very exciting time and insane, exciting space. And to me, it’s remarkable just the capabilities that existed a year ago where you could kind of go and and put in text or audio or video and be able to interact and and get like, you know, interesting content that could help you just more whether it was just personal searches or whatever be productive, and to now where it’s available more internally for different organizations. And even what we’ve seen internally is trying to use the technology six months ago, may have involved eight steps and a lot of what I’ll call data wrangling to kind of get the data in the right format, and to feed it in to now it’s more like there might be four steps involved in so you can very, you can much more easily integrate data and get to the outcomes and so it’s become a lot simpler to implement. And I think that’s going to be the future is that it will continue to get easier, much easier for people to apply it to their use cases and to use it for a variety of different use cases. And I think different vendors We’ll start to understand some patterns where, you know, there might be a call center use case that, you know, always occurs, you know, one example I always think of is, I can’t think of a time in the past 10 plus years where you called customer service and get transferred to an agent, where they don’t say, this call may be recorded for quality assurance purposes, with quality assurance of a phone call usually involves people manually listening to it and taking notes and kind of filling out a scorecard. Well, now with you know, AI capabilities that can all be done in a much more automated way. So there’s, there’s lots of different things that like that kind of use case, that pattern that I’m guessing there are gonna be vendors who will now put that type of solution out there and make it very easy for people to consume almost like the AWS approach, where things that AWS did are now kind of exposed as services that other companies can kind of plug into very easily. That’s an example where I think the technology is headed, and you’ll start to see some point solutions that will emerge in that space. from a regulatory standpoint, I think it’s going to be interesting, you know, similar to death and taxes, I think, you know, regulate regulation is always going to be there, particularly in financial services. And it’s to do the things that we talked about before protecting customers protecting the banking system protecting, you know, different areas that are important. So I think that’s, that’s a certainty. And for us, you know, I think it’s, there’s likely to be different, different changes that will occur as a result of the technology and the data that’s available. I don’t see it as being drastic changes to the regulations. But more looking back at some of the existing regulations and saying, given the new technology, given the new data sets that exist out there, are there things we need to change about some of those existing regulations to make sure that they’re, they’re still controlling for the right things?
Whitney McDonald 16:59
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Lenders now have the ability to reliably find, score, lend to and manage the loans of 64 million unbanked and under-banked consumers in the United States alone
Press Release
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updated: Jun 12, 2019
PALO ALTO, Calif., June 12, 2019 (Newswire.com)
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Trust Science Inc., a leading provider of AI-powered credit scoring, and Inovatec Systems Corporation, a new breed of Loan Operating System (LOS) provider, announced today they will partner to release a fully automated lending platform that enables end-to-end loan management across the entire credit spectrum.
Lenders can be up and running on a fully customized LOS and an AI-powered loan underwriting model within weeks, not months (or years).
Trust Science CEO Evan Chrapko comments, “This partnership gives lenders the ability to accurately score and lend to an additional 64 million consumers in the U.S. alone, with unprecedented accuracy and speed. The end-to-end, customizable nature of Inovatec Systems’ LOS makes it a perfect partner for Trust Science and our API-based scoring solution.”
Bryan Smith, VP sales & marketing at Inovatec, shares a similar sentiment. “With this partnership, Inovatec Systems will now be able to automate the powerful AI tools at Trust Science alongside traditional credit scoring and risk measurements. Our lenders will have instant access to the Trust Science Six°Score™ to determine creditworthiness based on alternative, uncorrelated data, generating simple and powerful results for a more complete risk assessment of the individual.” He continues, “The Trust Science tools will be integrated into our Compass Asset Finance (CAF) for credit and funding, driving more innovation and thinking differently.”
Mark Eleoff, CEO of Eden Park Inc. and a customer of Trust Science and Inovatec Systems, remarks, “Both Trust Science and Inovatec Systems have proven themselves to be innovative, value-added and very customer centric in working with us to improve our credit decisions.”
A BETA version of the integration has been underway for several months, and general release is expected in June.
About Trust Science Inc.
Trust Science provides AI-powered alternative credit scoring to lenders, helping them sift prime borrowers from wrongly scored subprime applicants. Trust Science gathers alternative unstructured data and consented mobile data using its patented (30-plus patents across six countries) data collection methods and builds custom underwriting models for short-term, installment, direct auto and indirect auto lenders. Lenders see increases in their loan origination volumes, reduction in default rates and double-digit ROI. For more information, please visit https://www.trustscience.com/.
About Inovatec Systems Corp.
Inovatec Systems Corporation provides industry-leading, cloud-based software solutions for any financial institution, any type of transaction. All solutions can be brought together in a single seamless and branded platform that can be opened to external partners and customers. Capture any marketplace – full, robust ecosystem to drive the online customer/lead to you, streamline and facilitate the processes of crediting, auditing, funding and income verification for financing applications plus full servicing and portfolio analytics in the leading-edge LMS. For more information, please visit https://www.inovatec.com/.
Press Contacts:
Bryan Smith
Inovatec Systems Corp. | VP, Sales & Marketing
bsmith@inovatec.com
(647) 269-9449
Bryan Katis
Chief Product Officer, Trust Science
bryan.katis@trustscience.com
(678) 468-7391
Source: Trust Science
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