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

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  Speaker 1: Credit scores and credit reports are a record of what
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  Speaker 1: has happened. They are not a record of what will happen.
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  Speaker 1: You may not know exactly what the rules are. You
• 0:08
  Speaker 1: may not know exactly what the rules will be. You
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  Speaker 1: do know that you should understand how models work.
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  Speaker 2: People want to know the answer right away if they
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  Speaker 2: don't want to wait ten minutes. Some days it's in
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  Speaker 2: some done frames just to then get declined, anything just
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  Speaker 2: to get to climb.
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  Speaker 3: Approval rates matter more than interest rate in today's work.
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  Speaker 4: I think I would say facts.
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  Speaker 3: Hi, Welcome to Leers and lending. Today. We're going to
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  Speaker 3: be talking about how credit unions can manage risk through
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  Speaker 3: smarter lending decisions and how that works within the regulatory
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  Speaker 3: environment that credit unions have. Talking to Drew and Linn today,
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  Speaker 3: managing lending risk has never been more complex we have.
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  Speaker 3: If you think of the how lending regulations have evolved
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  Speaker 3: through the years. You know, TILA came out in like
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  Speaker 3: the seventies, and then you know we all the way
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  Speaker 3: to sort of Dodd Frank in twenty ten, and it
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  Speaker 3: just seems there's always more and more regulations that are
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  Speaker 3: coming up for credit unis and banks to have to
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  Speaker 3: deal with. So, how do we get regulators comfortable with
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  Speaker 3: partnering with FinTechs and those FinTechs specifically that we leveraging
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  Speaker 3: AI based underrating models thinking of the regulatory environment that
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  Speaker 3: we're dealing with today.
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  Speaker 1: Yeah, it's a good question, and I think it's a
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  Speaker 1: shifting regulatory environment on a day to day basis. I
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  Speaker 1: think all of us have certainly watched the changes with
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  Speaker 1: the various financial regulators this year than now some court
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  Speaker 1: challenges to the leadership and who's on the board and
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  Speaker 1: who's not on the board, and what the rules are.
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  Speaker 1: So I would recommend for a credit union or really
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  Speaker 1: any lending institution, like just focus on the fundamentals. You
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  Speaker 1: may not know exactly what the rules are, You may
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  Speaker 1: not know exactly what the rules will be, but you
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  Speaker 1: do know that you should understand how models work. You
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  Speaker 1: should understand who owns which part.
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  Speaker 4: Of the decision.
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  Speaker 1: You should do your due diligence on any vendors that
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  Speaker 1: you partner with in a traditional way where you're looking
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  Speaker 1: at compliance risk, financial health, reputational risk, like even though
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  Speaker 1: those things are may not be explicitly called out anymore,
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  Speaker 1: like reputational risk as an example, that doesn't mean that
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  Speaker 1: you don't want.
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  Speaker 4: To do that for your business.
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  Speaker 1: So think about from a core business standpoint, what do
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  Speaker 1: you care about? What do you think the key areas
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  Speaker 1: are that you should be able to both understand and
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  Speaker 1: document and then how can you up level your organization
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  Speaker 1: to really be confident in understanding and monitoring AI models?
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  Speaker 1: And I think that's a very big challenge. And I
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  Speaker 1: think similar to a lot of conversations we've had about
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  Speaker 1: different sized organizations, Like there's choices of partnering. There are
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  Speaker 1: many vendors, there are many consultants who.
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  Speaker 4: Help in this space today.
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  Speaker 1: So you may not be able to hire deep AI
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  Speaker 1: experts internally you don't need to, but can you find
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  Speaker 1: trusted partners that can help you do that evaluation and
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  Speaker 1: help and opportunities for your team to become to kind
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  Speaker 1: of deepen their subject matter expertise as it relates to.
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  Speaker 4: AI and.
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  Speaker 1: New and more innovative ways of evaluating risk and lending.
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  Speaker 2: Yeah, and that's a good like having a good general
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  Speaker 2: high level knowledge base of all things AI. I mean,
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  Speaker 2: it's the topic at hand most often anymore, right, but
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  Speaker 2: digging even deeper. So if you are partnered with some
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  Speaker 2: type of institution that is driving AI and some metric.
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  Speaker 2: How can you get more knowledge base into your internal
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  Speaker 2: teams on this company, does X, Y, and Z we
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  Speaker 2: kind of understand the full rails of it. Maybe we
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  Speaker 2: don't understand all of it, but we have confidence in
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  Speaker 2: going into a regulatory exam or speaking to regulators of
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  Speaker 2: why we're comfortable with this approach and having that for
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  Speaker 2: every single vendor that you have that leverages AI, so
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  Speaker 2: you have the high level knowledge base and then you
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  Speaker 2: have all the double clicks and everything else you're using
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  Speaker 2: it for.
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  Speaker 3: Right, So you don't have to be the expert thing.
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  Speaker 3: You just have to partner with experts in some respects.
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  Speaker 1: Yeah, well, I mean I think you have to have
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  Speaker 1: some level of expertise. But like if we go back
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  Speaker 1: to like thinking about how say phycostore example is created,
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  Speaker 1: could you, as a credit union executive, tell me how
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  Speaker 1: that number is calculating exactly? So I think think about
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  Speaker 1: it that way, like you know, putting an oversight of
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  Speaker 1: an AI lender, an AI vendor, or an AI model
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  Speaker 1: into your overall model risk management framework, Like how do
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  Speaker 1: you think about managing model risk?
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  Speaker 4: How do you think about evaluating.
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  Speaker 1: Model risk that does not mean that you need to
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  Speaker 1: be able to build, build, and recreate the entire thing,
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  Speaker 1: but you have to be able to articulate and understand
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  Speaker 1: it and are doing enough kind of due diligence on
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  Speaker 1: your partner to feel confident in their capabilities and their depth.
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  Speaker 2: Yeah, and if you get the further digged in details,
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  Speaker 2: you're able to create risk assessments if XYZ and happen.
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  Speaker 2: If XYZ happened, we mitigate this way, and that just
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  Speaker 2: gives more comfort to regulators on why you're able to
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  Speaker 2: adopt those types of rights of partnerships.
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  Speaker 3: So shifting gears a little bit. How do you believe
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  Speaker 3: the credit unions are using data today to sort of
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  Speaker 3: balance unsecured versus secured lending?
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  Speaker 2: I would say from an organic perspective, if it's an
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  Speaker 2: existing member base that's coming through to get unsecured. The
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  Speaker 2: biggest thing from a data perspective today is the credit
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  Speaker 2: unions have a large source of data for that consumer.
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  Speaker 2: They have their in and outs via AH, they know
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  Speaker 2: their cash flows, they understand their spending habits. Just because
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  Speaker 2: DTI percent is X does not mean that they're an
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  Speaker 2: actual DTI percent of X, right, So they're able to
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  Speaker 2: leverage that from a decisioning basis. Then you think about
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  Speaker 2: all of that data and different rails that you can
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  Speaker 2: put it in. You're getting an output of pre approval
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  Speaker 2: or denial at a snap of a finger. So I
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  Speaker 2: think that's one real good aspect of comparing underwriting today
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  Speaker 2: compared to the prior.
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  Speaker 4: Yeah, I'm true.
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  Speaker 1: If you have their main deposit account, their main transaction account,
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  Speaker 1: you can learn a lot about a person's behaviors or
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  Speaker 1: next like does it you know, are they buying it?
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  Speaker 1: Did they recently get married? Did they maybe open a
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  Speaker 1: joint account?
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  Speaker 4: Like can you did they move? And can you you know?
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  Speaker 1: Will they need potentially additional types of of lending to
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  Speaker 1: support that. So I think using that, using that data
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  Speaker 1: that they've had and have rich sources of, and using
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  Speaker 1: it to figure out how they're lending or engaging with
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  Speaker 1: those people.
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  Speaker 2: And even if they're not a depository or using a
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  Speaker 2: depository product, you can paint a picture from a credit
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  Speaker 2: report and you don't need an underwriter to sit there
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  Speaker 2: and look at the fifty different tradelight lines that were
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  Speaker 2: opened over the past twenty years to paint that picture.
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  Speaker 2: You can tell that that borrower has never missed a payment,
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  Speaker 2: or maybe they'll miss a payment at month on book
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  Speaker 2: thirty six, but things start to level back out. You
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  Speaker 2: can kind of get sense of that. And what's nice
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  Speaker 2: now is with the use of AI and with automated fashions,
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  Speaker 2: you don't have to sit and read it. You can
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  Speaker 2: get an output again going back to kind of like
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  Speaker 2: the cash flows of their depository product to be able
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  Speaker 2: to paint that picture again on a snap up, because.
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  Speaker 3: It's just data.
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  Speaker 2: It's just there.
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  Speaker 3: It's just a bunch of data. And it's how you
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  Speaker 3: actually ingest that data into some sort of an AI
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  Speaker 3: system that will take data and turn into really live information.
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  Speaker 3: That's yes, were for you. You know, it's really good at
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  Speaker 3: this are the credit card companies right, absolutely, because and
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  Speaker 3: we're talking on secure lending, right, so they have a
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  Speaker 3: lens into purchasing behavior. They know that you booked a
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  Speaker 3: flight to Columbus, Ohio, and so they would expect, well,
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  Speaker 3: there should be a hotel charge in Columbus, Ohio, and
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  Speaker 3: restaurant charges and so on, and so instead of now
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  Speaker 3: I'm getting declined because I'm no longer and you know
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  Speaker 3: close to home. I'm two thousand miles from home. Credit
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  Speaker 3: card companies have sort of figured this out. I mean,
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  Speaker 3: is this something that is there an opportunity here for creditings?
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  Speaker 3: Many creditings don't own their credit card balances anymore, but
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  Speaker 3: they do have partnerships where there should be some sort
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  Speaker 3: of sharing of that data.
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  Speaker 1: Sure, and at about years ago, when you, particularly if
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  Speaker 1: you traveled internationally, you had to notify your credit cards.
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  Speaker 4: Sometimes you still do.
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  Speaker 1: You had to go in and say here's where I'm
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  Speaker 1: going on these dates, or you risk your credit card
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  Speaker 1: not working when you got on vacation. And now that
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  Speaker 1: it happens organically because they're using the data to make
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  Speaker 1: those make those assumptions.
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  Speaker 4: Yeah, I do think that's interesting.
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  Speaker 1: I think particularly with me, and I don't want to
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  Speaker 1: don't want to segue too far into the open banking
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  Speaker 1: kind of quagmire that's happening today because there's a lot
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  Speaker 1: of competing conversations around it. But I think that's a
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  Speaker 1: good question of like who owns the data? So they
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  Speaker 1: may be partnering with a credit card vendor to extend
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  Speaker 1: that product to their members, but that is still their member.
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  Speaker 1: They still hold the economic risk. And so because they
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  Speaker 1: hold that economic risk, what level of data do they
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  Speaker 1: have access to and how can they use that, particularly
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  Speaker 1: to make new offers. And I think that's I think
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  Speaker 1: is a lot of this open banking. Like the legal
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  Speaker 1: challenges play out too, I think we'll see more clarity
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  Speaker 1: und what they can do in the future.
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  Speaker 3: Do you think overall that data driven models outperform traditional linderwriting.
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  Speaker 2: Yes, being biased since we come from upstart, of course
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  Speaker 2: we use data driven models, but we're able to see
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  Speaker 2: and it's not just upstarted across the board is you
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  Speaker 2: have borrowers that under traditional underwriting guidelines probably wouldn't qualify
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  Speaker 2: for a credit union type of credit, right, whether that
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  Speaker 2: be a secured versus an unsecured type of asset. Whereas
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  Speaker 2: these types of models are able to separate risk much
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  Speaker 2: better than an underwriter sitting in front of a computer
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  Speaker 2: or some automated prequal type of logic that, Okay, maybe
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  Speaker 2: this six forty FICO borrower we would decline, but they're
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  Speaker 2: really transacting more of like a prime type of borrower.
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  Speaker 2: But they're too new into the system take a chance
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  Speaker 2: on them, right, So I would be very biased in
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  Speaker 2: saying that, yes, they're better separators of risk.
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  Speaker 1: Yeah, And you think about and We've talked about this
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  Speaker 1: a lot at at our company. Of course, where credit
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  Speaker 1: scores and credit report sports are a record of what
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  Speaker 1: has happened, they are not a record.
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  Speaker 4: Of what will happen.
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  Speaker 1: And while you know, we'll put the disclaimer like historical
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  Speaker 1: results are in an.
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  Speaker 4: Indicator of a future, there certainly.
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  Speaker 1: Is that idea that that you know, if you've behaved
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  Speaker 1: a certain way, particularly if you behave really risky you
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  Speaker 1: could expect risky behavior. But like you're to the point
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  Speaker 1: you used, what if it's a person who is, you know,
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  Speaker 1: a new citizen in the US or a recent college
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  Speaker 1: student who maybe didn't have the benefit of their parents
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  Speaker 1: helping them build that credit profile and that credit score
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  Speaker 1: growing up, but they have a a good job and
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  Speaker 1: they're making money, and they are are most likely going
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  Speaker 1: to pay in the future. How do you use that
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  Speaker 1: data to lend to those borrowers where they may in
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  Speaker 1: traditional kind of rate rate based models may may not
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  Speaker 1: be lent to.
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  Speaker 2: Another example of that, too, is going back to my
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  Speaker 2: previous kind of comment, is the credit report has this
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  Speaker 2: this whole slew of everything that this individual has transacted on.
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  Speaker 2: Maybe there was a life changing event that took them
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  Speaker 2: into that six point forty. But they've always been a
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  Speaker 2: super prime type of consumer. But something happened along along
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  Speaker 2: the way, and to your point, forward thinking, this borrower
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  Speaker 2: is probably going to transact that same way. But death, divorce,
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  Speaker 2: there's a bunch of different things that could bring you
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  Speaker 2: into a lower fight Coban, Yeah.
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  Speaker 1: And I think that trending data of like which direction
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  Speaker 1: are you trending?
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  Speaker 4: That can definitely be seen in the.
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  Speaker 1: Credit report data. Are your balances increasing, are they decreasing?
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  Speaker 1: Are the number of tradelines you have increasing decreasing? Is
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  Speaker 1: your mix improving of the types of tradelines? I think
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  Speaker 1: all of those things, not just like the snapshot of
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  Speaker 1: what your credit report looks like today, but what does
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  Speaker 1: it look like today compared to maybe two years ago.
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  Speaker 1: You know, maybe you lost your job and during COVID
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  Speaker 1: or you you know, had a period of time you
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  Speaker 1: were underemployed. Does that mean you won't pay your bills
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  Speaker 1: next year? I think that's a very different.
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  Speaker 2: And another benefit of that is a standard underwriting models
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  Speaker 2: they cannot train underwriter sees what they're seeing, whereas models
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  Speaker 2: that are leveraging decisioning processes are able to train on
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  Speaker 2: subsets of data over and over and over again based
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  Speaker 2: on this credit report kind of mimics that credit report
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  Speaker 2: or this cash flow mix of that cash flow, So
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  Speaker 2: the output of risk and the output of approval odds
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  Speaker 2: is going to be much better. Yeah.
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  Speaker 3: I'm experienced enough in my career that I was making
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  Speaker 3: lending decisions as a lending officer. Now it was assisted
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  Speaker 3: by what was the information that was in our system
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  Speaker 3: and assisted by credit report, but inherently I probably had
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  Speaker 3: some bias as I was making decisions, right, And if
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  Speaker 3: you think of an AI underwriting model, it's intentionally supposed
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  Speaker 3: to reduce some of that bias programmatically within the algorithms, correct.
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  Speaker 1: Yeah, I mean you would you assuming that you're testing
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  Speaker 1: for that bias. And I think that's an important thing.
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  Speaker 1: And you know, we talked a little bit earlier about
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  Speaker 1: getting comfortable with ai A lending models and fareness is
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  Speaker 1: a key component, and that's those are great questions to
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  Speaker 1: ask your partner, your vendor. How are they testing for it?
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  Speaker 1: How are they validating that? Is there any independent testing
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  Speaker 1: of of fair lending within their model? You know, what
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  Speaker 1: sort of demographic data do they have access to?
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  Speaker 4: If so?
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  Speaker 1: And how are they using it, and then as they're
• 13:22
  Speaker 1: making changes to that model, they're adding in new data,
• 13:24
  Speaker 1: new data sources. How are what does their model change
• 13:27
  Speaker 1: management process look like? Their fair lending testing component look like?
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  Speaker 1: To get comfortable that they are choosing.
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  Speaker 4: A model that you know, are there least.
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  Speaker 1: Discriminatory alternatives that they could pursue that have the same output?
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  Speaker 1: And I think knowing enough to ask those sorts of
• 13:44
  Speaker 1: questions are are the key parts even if you can't
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  Speaker 1: kind of rebuild the model yourself from.
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  Speaker 2: Scratch, and in most oftentimes it's more approval odds with
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  Speaker 2: less bias.
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  Speaker 3: Great. How do you think credit unions can prepare for
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  Speaker 3: their regulatory examination and all make the specific to those
• 14:01
  Speaker 3: that are maybe partnering with FinTechs who may be using
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  Speaker 3: AI or machine learning for the render any models.
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  Speaker 4: Yeah, I can start night. I honestly would be interested.
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  Speaker 1: Drew in your perspective too as a farmer credit union
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  Speaker 1: CEO and very that you know the way you thought
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  Speaker 1: about this as a CEO.
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  Speaker 4: Of a credit union. But I think ultimately is have
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  Speaker 4: a plan.
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  Speaker 1: Right, like know how you want to do diligence, how
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  Speaker 1: you want to think about what your use cases like,
• 14:30
  Speaker 1: what what do I want to solve? What am I
• 14:32
  Speaker 1: looking for this vendor to help me do? Is it
• 14:37
  Speaker 1: an end to end of all one product? Is it
• 14:39
  Speaker 1: multiple products? Is it a very limited scope? And uh?
• 14:44
  Speaker 1: And doing an RFP and asking those questions and making
• 14:47
  Speaker 1: sure that you're documenting that that diligence process and doing
• 14:50
  Speaker 1: a real risk based assessment of the value, not just
• 14:53
  Speaker 1: get Really it's really easy to get excited about AI
• 14:56
  Speaker 1: and it's shiny, and you know, there's so many things
• 14:58
  Speaker 1: that you can you can solve, and everyone's getting a
• 15:00
  Speaker 1: mandate top down, but doing your diligence, building that partnership,
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  Speaker 1: finding a partner that you really can build a business
• 15:09
  Speaker 1: relationship with, not just a transactional vendor relationship. I think
• 15:13
  Speaker 1: that to me is the first thing is document that.
• 15:15
  Speaker 1: So when your field examiner comes in, they say, we
• 15:19
  Speaker 1: know you have a new partnership with X, y Z.
• 15:21
  Speaker 1: Tell us how it's going, and you can lay out
• 15:25
  Speaker 1: your work. You can kind of show your work. These
• 15:27
  Speaker 1: are the things that we did to get comfortable working
• 15:29
  Speaker 1: with this vendor and have a plan for oversight.
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  Speaker 4: What does that look like annually? What does that look
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  Speaker 4: like periodically? And it should be risk based, how much.
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  Speaker 1: You know, if you're doing a very small bit of business,
• 15:40
  Speaker 1: it's you know, a couple percent of your balance sheet
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  Speaker 1: with a particular vendor, you don't need to build a
• 15:45
  Speaker 1: whole organization around it. If it's something that's going to
• 15:47
  Speaker 1: be twenty five percent, you need a much more robust oversight.
• 15:51
  Speaker 1: So I think your oversight should be scale with both
• 15:56
  Speaker 1: the size of the of the fintech vendor you're working with,
• 15:58
  Speaker 1: but also with the size of your organization and the
• 16:01
  Speaker 1: importance of that product placement within your organization.
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  Speaker 2: I'm just going to be transparent. So a majority of
• 16:08
  Speaker 2: the credit unions a little over ninety percent or Campbe's
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  Speaker 2: one or Campbell's two rating, that's that's a great rating.
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  Speaker 2: If you're three to four are about to become insolvent,
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  Speaker 2: you probably have other things to be talking to your
• 16:18
  Speaker 2: field executions exactly. You shouldn't even be having those conversations.
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  Speaker 2: And everything that you've built from a diligence perspective preparing
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  Speaker 2: for an exam is already doing very well, or else
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  Speaker 2: you wouldn't be a Campbell's one or a Cameos two.
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  Speaker 2: Your lending strategy is built, your deposit strategy is built.
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  Speaker 2: Everything on the back end that you're monitoring all of
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  Speaker 2: that with to make sure that it is in line
• 16:39
  Speaker 2: with regulatory guidance is probably there in some aspect, and
• 16:42
  Speaker 2: you probably have the risk assessments in place to be
• 16:45
  Speaker 2: able to rinse and repeat here right now. If you're
• 16:48
  Speaker 2: starting to bring in AI types of partnerships or usage
• 16:51
  Speaker 2: of AI, whether it be in your branch or in
• 16:53
  Speaker 2: your call centers or things of that nature, you need
• 16:56
  Speaker 2: to do the build of what you've already built for
• 16:59
  Speaker 2: lending and for does it build risk assessments, become knowledgeable
• 17:02
  Speaker 2: that I was talking about earlier, and or be very
• 17:05
  Speaker 2: confident in talking about it and why it's beneficial for
• 17:09
  Speaker 2: your institution, because if you know, I mean, we've all
• 17:12
  Speaker 2: gone through exams with maybe not the NCUA, but if
• 17:17
  Speaker 2: you don't know what you're talking about, there's a red
• 17:18
  Speaker 2: flag there and they're going to double click and double
• 17:21
  Speaker 2: click and double click. And since you are already so
• 17:23
  Speaker 2: ingrained on your your lending and your deposit strategy and
• 17:26
  Speaker 2: all the risk that is associated with that, they're going
• 17:28
  Speaker 2: to start double clicking into this use of AI. And
• 17:30
  Speaker 2: if you're not confident, not confident in your delivery of
• 17:33
  Speaker 2: your knowledge base, then it's going to create issue. So
• 17:35
  Speaker 2: you have to build the knowledge base, build procedures, policies,
• 17:40
  Speaker 2: and then monitor on the back end and then recurring
• 17:42
  Speaker 2: what reoccurring basis.
• 17:44
  Speaker 3: Confidence in your governance is a very underrated character.
• 17:48
  Speaker 4: Yes, yep.
• 17:49
  Speaker 1: And knowing that it's your governance, I think that's an
• 17:51
  Speaker 1: important thing. Someone else that you as the credit union
• 17:53
  Speaker 1: to ultimately have that responsibility. You are the frontline to
• 17:56
  Speaker 1: your regulators. You're the front line to your examiner and
• 17:59
  Speaker 1: talk to them in a else if you are thinking
• 18:01
  Speaker 1: about a partnership in a certain area, they're there to
• 18:04
  Speaker 1: they will work with you, so you have you know,
• 18:06
  Speaker 1: I know there's been a lot of retirements and turnovers,
• 18:09
  Speaker 1: so I think that those like maybe kind of extended
• 18:12
  Speaker 1: relationships with those specific examiners have been broken in a
• 18:15
  Speaker 1: lot of cases. But you definitely reach out to them
• 18:19
  Speaker 1: in advance of a partnership and talk to them about
• 18:23
  Speaker 1: what you're doing. And that definitely a surprise when they
• 18:26
  Speaker 1: come in the door is not a good idea.
• 18:28
  Speaker 2: One hundred percent agree, And don't be afraid to challenge
• 18:31
  Speaker 2: your examiner with within respect, with right reason, within reason,
• 18:35
  Speaker 2: but just because the examiner thinks it should be this
• 18:38
  Speaker 2: way doesn't mean that it has to be that way.
• 18:40
  Speaker 2: If you're able to give them a good reasoning as
• 18:43
  Speaker 2: to why you think that this is the way that
• 18:45
  Speaker 2: we're doing it is sufficient enough, maybe that will create
• 18:49
  Speaker 2: an aha moment and then other exams. Okay, I remember
• 18:52
  Speaker 2: talking to x y Z from Credit Union one, two three.
• 18:55
  Speaker 2: They had a really good perspective. I'm going to go
• 18:57
  Speaker 2: with that type of approach next time I'm in the
• 18:59
  Speaker 2: field right and be.
• 19:00
  Speaker 3: Open to the regulator or the examiners opinion on games right.
• 19:04
  Speaker 3: You may not agree with it, but at least be
• 19:06
  Speaker 3: open and try and see it from their perspective. I've
• 19:08
  Speaker 3: been on the other side with institutions where we just
• 19:11
  Speaker 3: dug our heels in and said no, no, no, the way
• 19:13
  Speaker 3: you're saying it is, that's not the way. This is
• 19:14
  Speaker 3: the way that never turns out very well. It certainly
• 19:17
  Speaker 3: didn't for us, and as it was a good learning.
• 19:20
  Speaker 2: Moment that could put you into Camels three or four.
• 19:22
  Speaker 3: So then well, you know, hopefully not. But yes, I mean,
• 19:26
  Speaker 3: I guess that's that's under the management waiting. Yes, absolutely, yeah,
• 19:30
  Speaker 3: for sure.
• 19:31
  Speaker 2: Some additional drs that you don't want.
• 19:33
  Speaker 3: If today's episode gave you some new ideas around lending
• 19:36
  Speaker 3: and risk, please take a second to rate us on
• 19:38
  Speaker 3: Spotify or Apple or wherever you get your podcasts. It
• 19:41
  Speaker 3: will help more creditings find our show. Okay, it's time
• 19:43
  Speaker 3: for fact and fiction. We're going to break down your
• 19:45
  Speaker 3: opinions about managing risk and deciding whether we're on board
• 19:48
  Speaker 3: or not. You ready ready? Being conservative on credit risk
• 19:51
  Speaker 3: is costing credit unions market share factor fiction.
• 19:54
  Speaker 2: Fact with all capitals. You think about balance sheets with
• 19:59
  Speaker 2: with credit union that have tightened and maybe they started
• 20:02
  Speaker 2: their tightening journey post the GFC. Maybe some started tightening
• 20:08
  Speaker 2: during COVID and kind of have kept that trend right,
• 20:11
  Speaker 2: It just depends on where in the timeframe they decided
• 20:14
  Speaker 2: to tighten, and as a result they are now not
• 20:16
  Speaker 2: seeing much of a return and their margins are very
• 20:19
  Speaker 2: very thin. So you think about large and even mid
• 20:22
  Speaker 2: sized credit unions that took on this slew of lending
• 20:25
  Speaker 2: during COVID at very reduced rates, they're starting to see
• 20:29
  Speaker 2: some even pressures on margin as it relates to kind
• 20:32
  Speaker 2: of the inverse now where they're paying high on apy
• 20:36
  Speaker 2: for certificates of deposit and savings accounts. That their cost
• 20:40
  Speaker 2: of funds has arisen, and their cost of funds is
• 20:42
  Speaker 2: still super high, and those margins because they've tightened their
• 20:45
  Speaker 2: credits so much, it doesn't allow for them to get
• 20:48
  Speaker 2: those aprs that they would have originated for maybe a
• 20:50
  Speaker 2: six hundred type of Fyco borrow or so on and
• 20:54
  Speaker 2: so forth. And then even the risk segment, maybe they're
• 20:57
  Speaker 2: more focused on collateralized types of loans now compared to
• 21:01
  Speaker 2: either card or unsecured or even if they're seeing a
• 21:04
  Speaker 2: growth in unsecured lending, terms and tightening probably are a
• 21:08
  Speaker 2: little bit different than they were five ten years.
• 21:10
  Speaker 4: Yeah, do you think? And I would kind of add,
• 21:12
  Speaker 4: I think the.
• 21:14
  Speaker 1: Risk aversion is good to a point, but it's the
• 21:18
  Speaker 1: risk reward trade off, Like you know, you could you
• 21:22
  Speaker 1: take a little bit more risk even and even accept
• 21:24
  Speaker 1: slightly higher losses versus like zero losses, but make a
• 21:28
  Speaker 1: lot more money at the end of the day and
• 21:30
  Speaker 1: grow your member base and figuring out that balance is key.
• 21:34
  Speaker 2: Yeah, and you have to be able to if you
• 21:36
  Speaker 2: diversify in that sense and take on I don't know,
• 21:39
  Speaker 2: fifty basis points of more risk across any type of
• 21:43
  Speaker 2: basset that's going to have an increase to my margin
• 21:46
  Speaker 2: of why and if it makes sense when you're you know,
• 21:48
  Speaker 2: validating the CECIL methodology calculation and correlation to interest income,
• 21:52
  Speaker 2: then it might make sense to be more diverse and
• 21:55
  Speaker 2: more risk type of loans and get your margins or
• 21:57
  Speaker 2: your spreads back to something that's more desirable good.
• 22:01
  Speaker 3: Uh. Data driven lending is only as strong as the
• 22:04
  Speaker 3: models behind it.
• 22:05
  Speaker 4: Factor fiction definitely, definitely fact. I think anybody, you know,
• 22:11
  Speaker 4: it's kind of like that.
• 22:12
  Speaker 1: What's the uh lies lies and statistics or it's betraying
• 22:17
  Speaker 1: the quote damn lies and statistics. But but I think
• 22:22
  Speaker 1: anybody can take data and can misinterpret it, misuse it, uh,
• 22:27
  Speaker 1: well in in with good intention, right, It doesn't mean
• 22:30
  Speaker 1: that they're doing it with some some bad intention. But
• 22:33
  Speaker 1: I think how you use that data and what data
• 22:35
  Speaker 1: you need? Like it doesn't you don't have to use
• 22:37
  Speaker 1: every data point available. What are the things that are
• 22:40
  Speaker 1: actually meaningful to use in making and how do you
• 22:43
  Speaker 1: use those in making that decision?
• 22:45
  Speaker 4: Uh?
• 22:45
  Speaker 1: And so I think from that perspective, like you really
• 22:48
  Speaker 1: need a very strong, a very strong model that can
• 22:52
  Speaker 1: use that data in a powerful way because there's also
• 22:54
  Speaker 1: a cost to compute, so it does cost money to
• 22:56
  Speaker 1: run models, and it costs money to to hire high
• 23:00
  Speaker 1: quality vendors, and I think those things are are worth it.
• 23:03
  Speaker 1: But I think that the model itself is the key importance.
• 23:09
  Speaker 1: And even a very strong model can make very great
• 23:12
  Speaker 1: predictive results with smaller sets of data than a weaker
• 23:16
  Speaker 1: model could do with even larger amounts of data.
• 23:18
  Speaker 3: Good point.
• 23:19
  Speaker 2: I agree. Fact, going back to a prior point, you know,
• 23:22
  Speaker 2: models are trainable. People are trainable, but not with that
• 23:26
  Speaker 2: many data points. And as things shift, just because we
• 23:30
  Speaker 2: have fifteen hundred variables that gives you an output doesn't
• 23:33
  Speaker 2: mean that six aren't important right now, and then you
• 23:35
  Speaker 2: know six others will be important two years from now
• 23:37
  Speaker 2: as things evolve. So one hundred percent fact.
• 23:39
  Speaker 3: And that's what training does. Yeah, kind of identifies. Maybe
• 23:42
  Speaker 3: some variables drop off and some get better.
• 23:45
  Speaker 1: Yeah.
• 23:45
  Speaker 3: I mean the more time you have and the more
• 23:48
  Speaker 3: data you have, I think the more precise you can
• 23:50
  Speaker 3: get in terms of a predictability of your model. Ye,
• 23:53
  Speaker 3: approval rates matter more than interest rate in today's market.
• 23:56
  Speaker 1: I think I would say fact if we said approval
• 24:01
  Speaker 1: experience versus maybe rates like not everyone, so I would
• 24:06
  Speaker 1: say not everyone has to have a kind of an
• 24:09
  Speaker 1: instant approval, but a quicker decision and a quicker time
• 24:13
  Speaker 1: to kind of the end result. Whether Hey, tell me
• 24:17
  Speaker 1: if I'm getting a loan or not, and if I'm
• 24:19
  Speaker 1: getting alan, I want to get it quickly and with
• 24:21
  Speaker 1: as little friction as possible. If you're not going to
• 24:24
  Speaker 1: lend me money or for whether it's a personal loan,
• 24:27
  Speaker 1: a helock, a car, tell me that right up front
• 24:31
  Speaker 1: and tell me what I think. Yeah, I don't want
• 24:34
  Speaker 1: to jump through hoops to get.
• 24:35
  Speaker 4: To that final decision.
• 24:36
  Speaker 1: So I think you know that's reflected in the approval rates,
• 24:39
  Speaker 1: But I would almost say like instant decision rates matter
• 24:42
  Speaker 1: more than the rate itself. That people will do things
• 24:46
  Speaker 1: like consumer behavior. It's one of the big increases in
• 24:49
  Speaker 1: drivers Originally in personal loans was things that folks may
• 24:53
  Speaker 1: go to a helock or a refi for to do
• 24:57
  Speaker 1: things like replace the roof, or replace a data bathroom
• 25:01
  Speaker 1: or replace their HVAC.
• 25:03
  Speaker 4: You can take a personal loan. Is the rate higher? Yes?
• 25:06
  Speaker 1: Is the speed at which you get access to that
• 25:08
  Speaker 1: money also much lower with a lot less hoops to
• 25:11
  Speaker 1: jump through.
• 25:11
  Speaker 2: Absolutely, I would say fact as well to your point,
• 25:15
  Speaker 2: people want to know the answer right away. They don't
• 25:17
  Speaker 2: want to wait ten minutes, ten days, it's in some timeframes.
• 25:20
  Speaker 4: Just to then get declined anywhere, just to get declimbed.
• 25:22
  Speaker 2: And then, I mean, rate does matter to a sense
• 25:25
  Speaker 2: to most consumers, right, But again we've talked about this
• 25:28
  Speaker 2: in prior podcasts. They care more about their payment and
• 25:31
  Speaker 2: they care about they need the money right right away.
• 25:33
  Speaker 2: So I would say approval odds is more important than rate,
• 25:36
  Speaker 2: but make sure you're pricing right. If you're pricing correctly,
• 25:38
  Speaker 2: then those approval ods are going to be even greater. Right.
• 25:41
  Speaker 3: Thanks for watching this episode of Leaders in Lending. We'll
• 25:44
  Speaker 3: see you next time.