Oct. 29, 2025

Winning the Lending Game: What Smart Credit Unions Are Doing Differently

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Credit unions are operating in a new lending environment defined by risk, regulation and rapid innovation. In this episode of Leaders in Lending, hosts Barry Roach, Drew Megrey and Lynn Sautter Beal share how forward-thinking credit unions are adapting to win.

They explore how to build confidence in AI-driven models, balance compliance with growth, and strengthen governance through smarter data. The hosts also discuss why being too conservative on credit can cost market share, how approval experience matters more than rates and why data-driven models now outperform traditional underwriting.

From model oversight to regulatory readiness, this conversation gives credit union leaders practical strategies to stay competitive, confident and compliant in today’s lending landscape.

Don’t risk falling behind. Learn how to strengthen your governance, leverage smarter data, and prepare for the future of lending today.

WEBVTT

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Credit scores and credit reports are a record of what

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has happened. They are not a record of what will happen.

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You may not know exactly what the rules are. You

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may not know exactly what the rules will be. You

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do know that you should understand how models work.

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People want to know the answer right away if they

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don't want to wait ten minutes. Some days it's in

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some done frames just to then get declined, anything just

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to get to climb.

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Approval rates matter more than interest rate in today's work.

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I think I would say facts.

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Hi, Welcome to Leers and lending. Today. We're going to

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be talking about how credit unions can manage risk through

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smarter lending decisions and how that works within the regulatory

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environment that credit unions have. Talking to Drew and Linn today,

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managing lending risk has never been more complex we have.

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If you think of the how lending regulations have evolved

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through the years. You know, TILA came out in like

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the seventies, and then you know we all the way

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to sort of Dodd Frank in twenty ten, and it

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just seems there's always more and more regulations that are

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coming up for credit unis and banks to have to

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deal with. So, how do we get regulators comfortable with

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partnering with FinTechs and those FinTechs specifically that we leveraging

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AI based underrating models thinking of the regulatory environment that

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we're dealing with today.

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Yeah, it's a good question, and I think it's a

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shifting regulatory environment on a day to day basis. I

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think all of us have certainly watched the changes with

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the various financial regulators this year than now some court

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challenges to the leadership and who's on the board and

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who's not on the board, and what the rules are.

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So I would recommend for a credit union or really

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any lending institution, like just focus on the fundamentals. You

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may not know exactly what the rules are, You may

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not know exactly what the rules will be, but you

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do know that you should understand how models work. You

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should understand who owns which part.

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Of the decision.

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You should do your due diligence on any vendors that

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you partner with in a traditional way where you're looking

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at compliance risk, financial health, reputational risk, like even though

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those things are may not be explicitly called out anymore,

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like reputational risk as an example, that doesn't mean that

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you don't want.

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To do that for your business.

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So think about from a core business standpoint, what do

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you care about? What do you think the key areas

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are that you should be able to both understand and

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document and then how can you up level your organization

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to really be confident in understanding and monitoring AI models?

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And I think that's a very big challenge. And I

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think similar to a lot of conversations we've had about

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different sized organizations, Like there's choices of partnering. There are

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many vendors, there are many consultants who.

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Help in this space today.

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So you may not be able to hire deep AI

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experts internally you don't need to, but can you find

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trusted partners that can help you do that evaluation and

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help and opportunities for your team to become to kind

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of deepen their subject matter expertise as it relates to.

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AI and.

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New and more innovative ways of evaluating risk and lending.

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Yeah, and that's a good like having a good general

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high level knowledge base of all things AI. I mean,

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it's the topic at hand most often anymore, right, but

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digging even deeper. So if you are partnered with some

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type of institution that is driving AI and some metric.

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How can you get more knowledge base into your internal

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teams on this company, does X, Y, and Z we

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kind of understand the full rails of it. Maybe we

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don't understand all of it, but we have confidence in

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going into a regulatory exam or speaking to regulators of

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why we're comfortable with this approach and having that for

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every single vendor that you have that leverages AI, so

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you have the high level knowledge base and then you

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have all the double clicks and everything else you're using

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it for.

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Right, So you don't have to be the expert thing.

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You just have to partner with experts in some respects.

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Yeah, well, I mean I think you have to have

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some level of expertise. But like if we go back

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to like thinking about how say phycostore example is created,

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could you, as a credit union executive, tell me how

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that number is calculating exactly? So I think think about

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it that way, like you know, putting an oversight of

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an AI lender, an AI vendor, or an AI model

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into your overall model risk management framework, Like how do

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you think about managing model risk?

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How do you think about evaluating.

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Model risk that does not mean that you need to

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be able to build, build, and recreate the entire thing,

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but you have to be able to articulate and understand

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it and are doing enough kind of due diligence on

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your partner to feel confident in their capabilities and their depth.

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Yeah, and if you get the further digged in details,

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you're able to create risk assessments if XYZ and happen.

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If XYZ happened, we mitigate this way, and that just

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gives more comfort to regulators on why you're able to

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adopt those types of rights of partnerships.

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So shifting gears a little bit. How do you believe

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the credit unions are using data today to sort of

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balance unsecured versus secured lending?

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I would say from an organic perspective, if it's an

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existing member base that's coming through to get unsecured. The

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biggest thing from a data perspective today is the credit

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unions have a large source of data for that consumer.

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They have their in and outs via AH, they know

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their cash flows, they understand their spending habits. Just because

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DTI percent is X does not mean that they're an

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actual DTI percent of X, right, So they're able to

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leverage that from a decisioning basis. Then you think about

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all of that data and different rails that you can

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put it in. You're getting an output of pre approval

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or denial at a snap of a finger. So I

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think that's one real good aspect of comparing underwriting today

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compared to the prior.

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Yeah, I'm true.

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If you have their main deposit account, their main transaction account,

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you can learn a lot about a person's behaviors or

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next like does it you know, are they buying it?

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Did they recently get married? Did they maybe open a

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joint account?

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Like can you did they move? And can you you know?

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Will they need potentially additional types of of lending to

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support that. So I think using that, using that data

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that they've had and have rich sources of, and using

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it to figure out how they're lending or engaging with

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those people.

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And even if they're not a depository or using a

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depository product, you can paint a picture from a credit

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report and you don't need an underwriter to sit there

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and look at the fifty different tradelight lines that were

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opened over the past twenty years to paint that picture.

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You can tell that that borrower has never missed a payment,

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or maybe they'll miss a payment at month on book

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thirty six, but things start to level back out. You

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can kind of get sense of that. And what's nice

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now is with the use of AI and with automated fashions,

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you don't have to sit and read it. You can

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get an output again going back to kind of like

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the cash flows of their depository product to be able

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to paint that picture again on a snap up, because.

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It's just data.

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It's just there.

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It's just a bunch of data. And it's how you

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actually ingest that data into some sort of an AI

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system that will take data and turn into really live information.

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That's yes, were for you. You know, it's really good at

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this are the credit card companies right, absolutely, because and

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we're talking on secure lending, right, so they have a

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lens into purchasing behavior. They know that you booked a

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flight to Columbus, Ohio, and so they would expect, well,

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there should be a hotel charge in Columbus, Ohio, and

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restaurant charges and so on, and so instead of now

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I'm getting declined because I'm no longer and you know

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close to home. I'm two thousand miles from home. Credit

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card companies have sort of figured this out. I mean,

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is this something that is there an opportunity here for creditings?

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Many creditings don't own their credit card balances anymore, but

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they do have partnerships where there should be some sort

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of sharing of that data.

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Sure, and at about years ago, when you, particularly if

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you traveled internationally, you had to notify your credit cards.

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Sometimes you still do.

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You had to go in and say here's where I'm

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going on these dates, or you risk your credit card

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not working when you got on vacation. And now that

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it happens organically because they're using the data to make

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those make those assumptions.

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Yeah, I do think that's interesting.

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I think particularly with me, and I don't want to

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don't want to segue too far into the open banking

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kind of quagmire that's happening today because there's a lot

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of competing conversations around it. But I think that's a

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good question of like who owns the data? So they

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may be partnering with a credit card vendor to extend

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that product to their members, but that is still their member.

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They still hold the economic risk. And so because they

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hold that economic risk, what level of data do they

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have access to and how can they use that, particularly

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to make new offers. And I think that's I think

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is a lot of this open banking. Like the legal

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challenges play out too, I think we'll see more clarity

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und what they can do in the future.

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Do you think overall that data driven models outperform traditional linderwriting.

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Yes, being biased since we come from upstart, of course

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we use data driven models, but we're able to see

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and it's not just upstarted across the board is you

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have borrowers that under traditional underwriting guidelines probably wouldn't qualify

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for a credit union type of credit, right, whether that

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be a secured versus an unsecured type of asset. Whereas

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these types of models are able to separate risk much

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better than an underwriter sitting in front of a computer

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or some automated prequal type of logic that, Okay, maybe

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this six forty FICO borrower we would decline, but they're

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really transacting more of like a prime type of borrower.

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But they're too new into the system take a chance

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on them, right, So I would be very biased in

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saying that, yes, they're better separators of risk.

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Yeah, And you think about and We've talked about this

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a lot at at our company. Of course, where credit

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scores and credit report sports are a record of what

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has happened, they are not a record.

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Of what will happen.

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And while you know, we'll put the disclaimer like historical

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results are in an.

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Indicator of a future, there certainly.

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Is that idea that that you know, if you've behaved

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a certain way, particularly if you behave really risky you

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could expect risky behavior. But like you're to the point

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you used, what if it's a person who is, you know,

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a new citizen in the US or a recent college

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student who maybe didn't have the benefit of their parents

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helping them build that credit profile and that credit score

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growing up, but they have a a good job and

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they're making money, and they are are most likely going

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to pay in the future. How do you use that

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data to lend to those borrowers where they may in

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traditional kind of rate rate based models may may not

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be lent to.

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Another example of that, too, is going back to my

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previous kind of comment, is the credit report has this

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this whole slew of everything that this individual has transacted on.

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Maybe there was a life changing event that took them

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into that six point forty. But they've always been a

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super prime type of consumer. But something happened along along

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the way, and to your point, forward thinking, this borrower

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is probably going to transact that same way. But death, divorce,

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there's a bunch of different things that could bring you

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into a lower fight Coban, Yeah.

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And I think that trending data of like which direction

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are you trending?

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That can definitely be seen in the.

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Credit report data. Are your balances increasing, are they decreasing?

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Are the number of tradelines you have increasing decreasing? Is

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your mix improving of the types of tradelines? I think

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all of those things, not just like the snapshot of

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what your credit report looks like today, but what does

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it look like today compared to maybe two years ago.

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You know, maybe you lost your job and during COVID

242
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or you you know, had a period of time you

243
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were underemployed. Does that mean you won't pay your bills

244
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next year? I think that's a very different.

245
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And another benefit of that is a standard underwriting models

246
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they cannot train underwriter sees what they're seeing, whereas models

247
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that are leveraging decisioning processes are able to train on

248
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subsets of data over and over and over again based

249
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on this credit report kind of mimics that credit report

250
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or this cash flow mix of that cash flow, So

251
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the output of risk and the output of approval odds

252
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is going to be much better. Yeah.

253
00:12:22.919 --> 00:12:25.279
I'm experienced enough in my career that I was making

254
00:12:25.320 --> 00:12:28.559
lending decisions as a lending officer. Now it was assisted

255
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by what was the information that was in our system

256
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and assisted by credit report, but inherently I probably had

257
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some bias as I was making decisions, right, And if

258
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you think of an AI underwriting model, it's intentionally supposed

259
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to reduce some of that bias programmatically within the algorithms, correct.

260
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Yeah, I mean you would you assuming that you're testing

261
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for that bias. And I think that's an important thing.

262
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And you know, we talked a little bit earlier about

263
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getting comfortable with ai A lending models and fareness is

264
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a key component, and that's those are great questions to

265
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ask your partner, your vendor. How are they testing for it?

266
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How are they validating that? Is there any independent testing

267
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of of fair lending within their model? You know, what

268
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sort of demographic data do they have access to?

269
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If so?

270
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And how are they using it, and then as they're

271
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making changes to that model, they're adding in new data,

272
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new data sources. How are what does their model change

273
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management process look like? Their fair lending testing component look like?

274
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To get comfortable that they are choosing.

275
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A model that you know, are there least.

276
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Discriminatory alternatives that they could pursue that have the same output?

277
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And I think knowing enough to ask those sorts of

278
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questions are are the key parts even if you can't

279
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kind of rebuild the model yourself from.

280
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Scratch, and in most oftentimes it's more approval odds with

281
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less bias.

282
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Great. How do you think credit unions can prepare for

283
00:13:57.240 --> 00:14:01.799
their regulatory examination and all make the specific to those

284
00:14:01.799 --> 00:14:05.159
that are maybe partnering with FinTechs who may be using

285
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AI or machine learning for the render any models.

286
00:14:09.039 --> 00:14:12.759
Yeah, I can start night. I honestly would be interested.

287
00:14:13.759 --> 00:14:16.440
Drew in your perspective too as a farmer credit union

288
00:14:16.480 --> 00:14:19.000
CEO and very that you know the way you thought

289
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about this as a CEO.

290
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Of a credit union. But I think ultimately is have

291
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a plan.

292
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Right, like know how you want to do diligence, how

293
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you want to think about what your use cases like,

294
00:14:30.879 --> 00:14:32.679
what what do I want to solve? What am I

295
00:14:32.799 --> 00:14:37.320
looking for this vendor to help me do? Is it

296
00:14:37.360 --> 00:14:39.480
an end to end of all one product? Is it

297
00:14:39.559 --> 00:14:44.200
multiple products? Is it a very limited scope? And uh?

298
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And doing an RFP and asking those questions and making

299
00:14:47.039 --> 00:14:50.879
sure that you're documenting that that diligence process and doing

300
00:14:50.879 --> 00:14:53.279
a real risk based assessment of the value, not just

301
00:14:53.360 --> 00:14:56.279
get Really it's really easy to get excited about AI

302
00:14:56.440 --> 00:14:58.720
and it's shiny, and you know, there's so many things

303
00:14:58.720 --> 00:15:00.799
that you can you can solve, and everyone's getting a

304
00:15:00.840 --> 00:15:05.840
mandate top down, but doing your diligence, building that partnership,

305
00:15:06.559 --> 00:15:09.120
finding a partner that you really can build a business

306
00:15:09.200 --> 00:15:13.200
relationship with, not just a transactional vendor relationship. I think

307
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that to me is the first thing is document that.

308
00:15:15.720 --> 00:15:19.120
So when your field examiner comes in, they say, we

309
00:15:19.679 --> 00:15:21.360
know you have a new partnership with X, y Z.

310
00:15:21.720 --> 00:15:25.639
Tell us how it's going, and you can lay out

311
00:15:25.720 --> 00:15:27.200
your work. You can kind of show your work. These

312
00:15:27.200 --> 00:15:29.559
are the things that we did to get comfortable working

313
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with this vendor and have a plan for oversight.

314
00:15:32.519 --> 00:15:34.519
What does that look like annually? What does that look

315
00:15:34.559 --> 00:15:37.679
like periodically? And it should be risk based, how much.

316
00:15:37.840 --> 00:15:40.600
You know, if you're doing a very small bit of business,

317
00:15:40.679 --> 00:15:42.799
it's you know, a couple percent of your balance sheet

318
00:15:42.919 --> 00:15:45.639
with a particular vendor, you don't need to build a

319
00:15:45.679 --> 00:15:47.960
whole organization around it. If it's something that's going to

320
00:15:47.960 --> 00:15:51.840
be twenty five percent, you need a much more robust oversight.

321
00:15:51.960 --> 00:15:56.120
So I think your oversight should be scale with both

322
00:15:56.120 --> 00:15:58.320
the size of the of the fintech vendor you're working with,

323
00:15:58.399 --> 00:16:01.600
but also with the size of your organization and the

324
00:16:01.639 --> 00:16:05.440
importance of that product placement within your organization.

325
00:16:06.039 --> 00:16:08.279
I'm just going to be transparent. So a majority of

326
00:16:08.279 --> 00:16:11.159
the credit unions a little over ninety percent or Campbe's

327
00:16:11.200 --> 00:16:13.360
one or Campbell's two rating, that's that's a great rating.

328
00:16:13.399 --> 00:16:15.960
If you're three to four are about to become insolvent,

329
00:16:16.440 --> 00:16:18.240
you probably have other things to be talking to your

330
00:16:18.279 --> 00:16:22.639
field executions exactly. You shouldn't even be having those conversations.

331
00:16:23.039 --> 00:16:26.720
And everything that you've built from a diligence perspective preparing

332
00:16:26.799 --> 00:16:29.480
for an exam is already doing very well, or else

333
00:16:29.519 --> 00:16:31.559
you wouldn't be a Campbell's one or a Cameos two.

334
00:16:32.000 --> 00:16:35.039
Your lending strategy is built, your deposit strategy is built.

335
00:16:35.360 --> 00:16:37.399
Everything on the back end that you're monitoring all of

336
00:16:37.399 --> 00:16:39.360
that with to make sure that it is in line

337
00:16:39.399 --> 00:16:42.600
with regulatory guidance is probably there in some aspect, and

338
00:16:42.639 --> 00:16:45.559
you probably have the risk assessments in place to be

339
00:16:45.600 --> 00:16:48.159
able to rinse and repeat here right now. If you're

340
00:16:48.159 --> 00:16:51.879
starting to bring in AI types of partnerships or usage

341
00:16:51.879 --> 00:16:53.919
of AI, whether it be in your branch or in

342
00:16:53.919 --> 00:16:56.480
your call centers or things of that nature, you need

343
00:16:56.519 --> 00:16:59.039
to do the build of what you've already built for

344
00:16:59.159 --> 00:17:02.559
lending and for does it build risk assessments, become knowledgeable

345
00:17:02.559 --> 00:17:05.559
that I was talking about earlier, and or be very

346
00:17:05.599 --> 00:17:09.359
confident in talking about it and why it's beneficial for

347
00:17:09.400 --> 00:17:12.519
your institution, because if you know, I mean, we've all

348
00:17:12.559 --> 00:17:17.000
gone through exams with maybe not the NCUA, but if

349
00:17:17.039 --> 00:17:18.799
you don't know what you're talking about, there's a red

350
00:17:18.799 --> 00:17:21.079
flag there and they're going to double click and double

351
00:17:21.079 --> 00:17:22.960
click and double click. And since you are already so

352
00:17:23.240 --> 00:17:26.119
ingrained on your your lending and your deposit strategy and

353
00:17:26.160 --> 00:17:28.200
all the risk that is associated with that, they're going

354
00:17:28.279 --> 00:17:30.079
to start double clicking into this use of AI. And

355
00:17:30.079 --> 00:17:33.160
if you're not confident, not confident in your delivery of

356
00:17:33.200 --> 00:17:35.759
your knowledge base, then it's going to create issue. So

357
00:17:35.839 --> 00:17:40.279
you have to build the knowledge base, build procedures, policies,

358
00:17:40.559 --> 00:17:42.559
and then monitor on the back end and then recurring

359
00:17:42.720 --> 00:17:43.680
what reoccurring basis.

360
00:17:44.079 --> 00:17:48.680
Confidence in your governance is a very underrated character.

361
00:17:48.880 --> 00:17:49.279
Yes, yep.

362
00:17:49.400 --> 00:17:51.480
And knowing that it's your governance, I think that's an

363
00:17:51.480 --> 00:17:53.839
important thing. Someone else that you as the credit union

364
00:17:53.880 --> 00:17:56.720
to ultimately have that responsibility. You are the frontline to

365
00:17:56.759 --> 00:17:59.200
your regulators. You're the front line to your examiner and

366
00:17:59.279 --> 00:18:01.400
talk to them in a else if you are thinking

367
00:18:01.400 --> 00:18:04.319
about a partnership in a certain area, they're there to

368
00:18:04.680 --> 00:18:06.880
they will work with you, so you have you know,

369
00:18:06.920 --> 00:18:09.680
I know there's been a lot of retirements and turnovers,

370
00:18:09.680 --> 00:18:12.720
so I think that those like maybe kind of extended

371
00:18:12.759 --> 00:18:15.799
relationships with those specific examiners have been broken in a

372
00:18:15.799 --> 00:18:19.720
lot of cases. But you definitely reach out to them

373
00:18:19.720 --> 00:18:23.000
in advance of a partnership and talk to them about

374
00:18:23.079 --> 00:18:26.359
what you're doing. And that definitely a surprise when they

375
00:18:26.400 --> 00:18:27.960
come in the door is not a good idea.

376
00:18:28.000 --> 00:18:31.240
One hundred percent agree, And don't be afraid to challenge

377
00:18:31.279 --> 00:18:34.920
your examiner with within respect, with right reason, within reason,

378
00:18:35.160 --> 00:18:38.200
but just because the examiner thinks it should be this

379
00:18:38.279 --> 00:18:40.359
way doesn't mean that it has to be that way.

380
00:18:40.640 --> 00:18:43.000
If you're able to give them a good reasoning as

381
00:18:43.000 --> 00:18:45.039
to why you think that this is the way that

382
00:18:45.400 --> 00:18:49.119
we're doing it is sufficient enough, maybe that will create

383
00:18:49.160 --> 00:18:52.519
an aha moment and then other exams. Okay, I remember

384
00:18:52.559 --> 00:18:55.519
talking to x y Z from Credit Union one, two three.

385
00:18:55.960 --> 00:18:57.640
They had a really good perspective. I'm going to go

386
00:18:57.640 --> 00:18:59.319
with that type of approach next time I'm in the

387
00:18:59.319 --> 00:19:00.519
field right and be.

388
00:19:00.559 --> 00:19:04.519
Open to the regulator or the examiners opinion on games right.

389
00:19:04.680 --> 00:19:06.480
You may not agree with it, but at least be

390
00:19:06.559 --> 00:19:08.880
open and try and see it from their perspective. I've

391
00:19:08.920 --> 00:19:11.559
been on the other side with institutions where we just

392
00:19:11.640 --> 00:19:13.279
dug our heels in and said no, no, no, the way

393
00:19:13.279 --> 00:19:14.920
you're saying it is, that's not the way. This is

394
00:19:14.960 --> 00:19:17.400
the way that never turns out very well. It certainly

395
00:19:17.400 --> 00:19:20.680
didn't for us, and as it was a good learning.

396
00:19:20.359 --> 00:19:22.319
Moment that could put you into Camels three or four.

397
00:19:22.359 --> 00:19:26.640
So then well, you know, hopefully not. But yes, I mean,

398
00:19:26.680 --> 00:19:30.720
I guess that's that's under the management waiting. Yes, absolutely, yeah,

399
00:19:30.759 --> 00:19:31.279
for sure.

400
00:19:31.119 --> 00:19:32.880
Some additional drs that you don't want.

401
00:19:33.880 --> 00:19:36.279
If today's episode gave you some new ideas around lending

402
00:19:36.359 --> 00:19:38.200
and risk, please take a second to rate us on

403
00:19:38.240 --> 00:19:41.000
Spotify or Apple or wherever you get your podcasts. It

404
00:19:41.039 --> 00:19:43.599
will help more creditings find our show. Okay, it's time

405
00:19:43.599 --> 00:19:45.480
for fact and fiction. We're going to break down your

406
00:19:45.480 --> 00:19:48.200
opinions about managing risk and deciding whether we're on board

407
00:19:48.319 --> 00:19:51.640
or not. You ready ready? Being conservative on credit risk

408
00:19:51.799 --> 00:19:54.680
is costing credit unions market share factor fiction.

409
00:19:54.599 --> 00:19:59.119
Fact with all capitals. You think about balance sheets with

410
00:19:59.119 --> 00:20:02.720
with credit union that have tightened and maybe they started

411
00:20:02.759 --> 00:20:08.440
their tightening journey post the GFC. Maybe some started tightening

412
00:20:08.559 --> 00:20:11.160
during COVID and kind of have kept that trend right,

413
00:20:11.200 --> 00:20:14.519
It just depends on where in the timeframe they decided

414
00:20:14.519 --> 00:20:16.640
to tighten, and as a result they are now not

415
00:20:16.680 --> 00:20:19.559
seeing much of a return and their margins are very

416
00:20:19.680 --> 00:20:22.319
very thin. So you think about large and even mid

417
00:20:22.359 --> 00:20:25.240
sized credit unions that took on this slew of lending

418
00:20:25.400 --> 00:20:29.759
during COVID at very reduced rates, they're starting to see

419
00:20:29.759 --> 00:20:32.599
some even pressures on margin as it relates to kind

420
00:20:32.640 --> 00:20:36.480
of the inverse now where they're paying high on apy

421
00:20:36.680 --> 00:20:40.240
for certificates of deposit and savings accounts. That their cost

422
00:20:40.240 --> 00:20:42.480
of funds has arisen, and their cost of funds is

423
00:20:42.480 --> 00:20:45.559
still super high, and those margins because they've tightened their

424
00:20:45.599 --> 00:20:47.960
credits so much, it doesn't allow for them to get

425
00:20:48.000 --> 00:20:50.799
those aprs that they would have originated for maybe a

426
00:20:50.839 --> 00:20:54.079
six hundred type of Fyco borrow or so on and

427
00:20:54.079 --> 00:20:57.200
so forth. And then even the risk segment, maybe they're

428
00:20:57.240 --> 00:21:01.200
more focused on collateralized types of loans now compared to

429
00:21:01.279 --> 00:21:04.079
either card or unsecured or even if they're seeing a

430
00:21:04.119 --> 00:21:08.720
growth in unsecured lending, terms and tightening probably are a

431
00:21:08.759 --> 00:21:10.440
little bit different than they were five ten years.

432
00:21:10.559 --> 00:21:12.359
Yeah, do you think? And I would kind of add,

433
00:21:12.359 --> 00:21:12.720
I think the.

434
00:21:14.440 --> 00:21:18.480
Risk aversion is good to a point, but it's the

435
00:21:18.759 --> 00:21:22.279
risk reward trade off, Like you know, you could you

436
00:21:22.319 --> 00:21:24.799
take a little bit more risk even and even accept

437
00:21:24.799 --> 00:21:28.720
slightly higher losses versus like zero losses, but make a

438
00:21:28.759 --> 00:21:30.720
lot more money at the end of the day and

439
00:21:30.759 --> 00:21:34.000
grow your member base and figuring out that balance is key.

440
00:21:34.119 --> 00:21:36.160
Yeah, and you have to be able to if you

441
00:21:36.279 --> 00:21:39.440
diversify in that sense and take on I don't know,

442
00:21:39.799 --> 00:21:43.240
fifty basis points of more risk across any type of

443
00:21:43.279 --> 00:21:45.880
basset that's going to have an increase to my margin

444
00:21:46.000 --> 00:21:48.400
of why and if it makes sense when you're you know,

445
00:21:48.480 --> 00:21:52.319
validating the CECIL methodology calculation and correlation to interest income,

446
00:21:52.759 --> 00:21:54.960
then it might make sense to be more diverse and

447
00:21:55.039 --> 00:21:57.759
more risk type of loans and get your margins or

448
00:21:57.799 --> 00:22:00.480
your spreads back to something that's more desirable good.

449
00:22:01.039 --> 00:22:04.000
Uh. Data driven lending is only as strong as the

450
00:22:04.039 --> 00:22:05.079
models behind it.

451
00:22:05.200 --> 00:22:11.400
Factor fiction definitely, definitely fact. I think anybody, you know,

452
00:22:11.480 --> 00:22:12.240
it's kind of like that.

453
00:22:12.240 --> 00:22:17.119
What's the uh lies lies and statistics or it's betraying

454
00:22:17.160 --> 00:22:22.400
the quote damn lies and statistics. But but I think

455
00:22:22.440 --> 00:22:27.359
anybody can take data and can misinterpret it, misuse it, uh,

456
00:22:27.440 --> 00:22:30.440
well in in with good intention, right, It doesn't mean

457
00:22:30.440 --> 00:22:33.759
that they're doing it with some some bad intention. But

458
00:22:33.839 --> 00:22:35.720
I think how you use that data and what data

459
00:22:35.759 --> 00:22:37.680
you need? Like it doesn't you don't have to use

460
00:22:37.720 --> 00:22:40.440
every data point available. What are the things that are

461
00:22:40.480 --> 00:22:43.440
actually meaningful to use in making and how do you

462
00:22:43.559 --> 00:22:45.079
use those in making that decision?

463
00:22:45.599 --> 00:22:45.920
Uh?

464
00:22:45.960 --> 00:22:48.200
And so I think from that perspective, like you really

465
00:22:48.240 --> 00:22:51.920
need a very strong, a very strong model that can

466
00:22:52.000 --> 00:22:54.039
use that data in a powerful way because there's also

467
00:22:54.079 --> 00:22:56.839
a cost to compute, so it does cost money to

468
00:22:56.960 --> 00:23:00.200
run models, and it costs money to to hire high

469
00:23:00.240 --> 00:23:03.119
quality vendors, and I think those things are are worth it.

470
00:23:03.200 --> 00:23:09.519
But I think that the model itself is the key importance.

471
00:23:09.559 --> 00:23:12.839
And even a very strong model can make very great

472
00:23:12.880 --> 00:23:16.480
predictive results with smaller sets of data than a weaker

473
00:23:16.519 --> 00:23:18.799
model could do with even larger amounts of data.

474
00:23:18.839 --> 00:23:19.240
Good point.

475
00:23:19.359 --> 00:23:22.119
I agree. Fact, going back to a prior point, you know,

476
00:23:22.279 --> 00:23:26.680
models are trainable. People are trainable, but not with that

477
00:23:26.720 --> 00:23:30.200
many data points. And as things shift, just because we

478
00:23:30.279 --> 00:23:33.359
have fifteen hundred variables that gives you an output doesn't

479
00:23:33.400 --> 00:23:35.759
mean that six aren't important right now, and then you

480
00:23:35.759 --> 00:23:37.880
know six others will be important two years from now

481
00:23:37.920 --> 00:23:40.160
as things evolve. So one hundred percent fact.

482
00:23:39.920 --> 00:23:42.640
And that's what training does. Yeah, kind of identifies. Maybe

483
00:23:42.680 --> 00:23:45.480
some variables drop off and some get better.

484
00:23:45.559 --> 00:23:45.839
Yeah.

485
00:23:45.880 --> 00:23:47.960
I mean the more time you have and the more

486
00:23:48.039 --> 00:23:50.160
data you have, I think the more precise you can

487
00:23:50.240 --> 00:23:52.839
get in terms of a predictability of your model. Ye,

488
00:23:53.000 --> 00:23:56.079
approval rates matter more than interest rate in today's market.

489
00:23:56.599 --> 00:24:01.480
I think I would say fact if we said approval

490
00:24:01.880 --> 00:24:06.480
experience versus maybe rates like not everyone, so I would

491
00:24:06.480 --> 00:24:09.240
say not everyone has to have a kind of an

492
00:24:09.240 --> 00:24:13.559
instant approval, but a quicker decision and a quicker time

493
00:24:13.720 --> 00:24:17.400
to kind of the end result. Whether Hey, tell me

494
00:24:17.440 --> 00:24:19.359
if I'm getting a loan or not, and if I'm

495
00:24:19.359 --> 00:24:21.319
getting alan, I want to get it quickly and with

496
00:24:21.359 --> 00:24:24.000
as little friction as possible. If you're not going to

497
00:24:24.119 --> 00:24:27.759
lend me money or for whether it's a personal loan,

498
00:24:27.799 --> 00:24:30.920
a helock, a car, tell me that right up front

499
00:24:31.119 --> 00:24:34.079
and tell me what I think. Yeah, I don't want

500
00:24:34.119 --> 00:24:35.599
to jump through hoops to get.

501
00:24:35.440 --> 00:24:36.559
To that final decision.

502
00:24:36.680 --> 00:24:39.640
So I think you know that's reflected in the approval rates,

503
00:24:39.640 --> 00:24:42.440
But I would almost say like instant decision rates matter

504
00:24:42.559 --> 00:24:46.559
more than the rate itself. That people will do things

505
00:24:46.559 --> 00:24:49.759
like consumer behavior. It's one of the big increases in

506
00:24:49.839 --> 00:24:53.319
drivers Originally in personal loans was things that folks may

507
00:24:53.359 --> 00:24:57.480
go to a helock or a refi for to do

508
00:24:57.519 --> 00:25:00.799
things like replace the roof, or replace a data bathroom

509
00:25:01.319 --> 00:25:02.519
or replace their HVAC.

510
00:25:03.160 --> 00:25:05.759
You can take a personal loan. Is the rate higher? Yes?

511
00:25:06.160 --> 00:25:08.319
Is the speed at which you get access to that

512
00:25:08.400 --> 00:25:11.279
money also much lower with a lot less hoops to

513
00:25:11.319 --> 00:25:11.759
jump through.

514
00:25:11.839 --> 00:25:14.880
Absolutely, I would say fact as well to your point,

515
00:25:15.359 --> 00:25:17.559
people want to know the answer right away. They don't

516
00:25:17.559 --> 00:25:21.160
want to wait ten minutes, ten days, it's in some timeframes.

517
00:25:20.640 --> 00:25:22.599
Just to then get declined anywhere, just to get declimbed.

518
00:25:22.839 --> 00:25:25.400
And then, I mean, rate does matter to a sense

519
00:25:25.440 --> 00:25:28.839
to most consumers, right, But again we've talked about this

520
00:25:28.880 --> 00:25:31.359
in prior podcasts. They care more about their payment and

521
00:25:31.559 --> 00:25:33.759
they care about they need the money right right away.

522
00:25:33.799 --> 00:25:36.240
So I would say approval odds is more important than rate,

523
00:25:36.279 --> 00:25:38.880
but make sure you're pricing right. If you're pricing correctly,

524
00:25:38.880 --> 00:25:41.319
then those approval ods are going to be even greater. Right.

525
00:25:41.839 --> 00:25:44.039
Thanks for watching this episode of Leaders in Lending. We'll

526
00:25:44.039 --> 00:25:44.720
see you next time.