Cash flow underwriting, explainable AI, and credit risk analytics are changing how lenders approve borrowers and set loan terms. Tedd Huff, CEO of fintech advisory firm Voalyre and founder of Fintech Confidential, sits down with Jamie Twiss, CEO of Carrington Labs, and Kasey Kaplan, Chief Product and Commercial Officer, to break down how behavioral signals in bank transaction data outperform traditional credit scores.

Over 50 percent of loan applicants cannot produce a reliable credit score, leaving self-employed workers, gig earners, and younger borrowers locked out of the system. Carrington Labs uses billions of lines of transaction data to build personalized, explainable machine learning models per lender, per product, and per customer segment. The conversation covers their "control point" approach to AI, lifecycle underwriting beyond origination, open banking friction in the US, and a five-year outlook on embedded, agent-driven lending.

FIND OUT MORE

1️⃣ Map analytics to every step of your lending funnel to find exactly where borrowers drop off and why.

2️⃣ Buy best-of-breed origination and servicing tools instead of building proprietary underwriting tech in-house.

3️⃣ Start with off-the-shelf models, lend small, collect performance signal, then shift to custom models fast.

4️⃣ Offer higher loan limits to borrowers who sync more accounts through open banking.

5️⃣ Track how borrowers respond to financial scarcity and build those behavioral patterns into your credit criteria.

Guest

Jamie Twiss LinkedIn: https://www.linkedin.com/in/james-twiss/

Kasey Kaplan LinkedIn: https://www.linkedin.com/in/kaseykaplan/

Company

Carrington Labs: https://www.carringtonlabs.com/

Carrington Labs LinkedIn: https://www.linkedin.com/company/carringtonlabs/

Beforepay Group: https://www.beforepaygroup.com

Fintech Confidential

Podcast: https://fintechconfidential.com/listen

Notifications: https://fintechconfidential.com/access

LinkedIn: https://www.linkedin.com/company/fintechconfidential

X: https://x.com/FTconfidential

Instagram: https://www.instagram.com/fintechconfidential

Facebook: https://www.facebook.com/fintechconfidential

About the Guests

Jamie Twiss is CEO of Carrington Labs and Beforepay Group. He began his career at McKinsey & Company, held senior banking roles including Chief Data Officer at a major Australian bank, and now leads the development of explainable AI credit risk models for lenders globally.

Kasey Kaplan is Chief Product and Commercial Officer at Carrington Labs. With over 15 years across payments, program management, and fintech lending, he leads commercial execution across credit risk scoring, cash flow underwriting, and loan limit solutions.

About the Company

Carrington Labs is the AI and enterprise software division of ASX-listed Beforepay Group. The company builds explainable AI credit risk scoring, cash flow underwriting, and loan limit solutions for banks and non-bank lenders worldwide, having powered over 4 million loans through its sister business.

About the Host

Tedd Huff, CEO of fintech advisory firm Voalyre and founder of Fintech Confidential. With 25+ years in fintech and payments, he brings entertaining and informative conversations focused on the people, tech, and companies that change how you pay and get paid.

DD3 Media

Fintech Confidential is a production of DD3 Media, a media creation, management, and production company delivering engaging fintech content globally.

Chapters

00:00 Episode Highlights

01:04 Welcome to Fintech Confidential

01:13 DFNS: Wallets as a Service (sponsor)

02:34 Meet Carrington Labs

04:48 Casey FinTech Origin

06:05 Jamie Credit Risk Path

07:59 Mission Beyond Scores

10:16 Cashflow Underwriting

13:35 Alternative Data Behaviors

17:37 Built Inside Beforepay

21:01 AI Control Points

24:07 Deterministic Vs Inference

28:35 Keeping Bias Out

34:48 Real Client Turnaround

36:44 Funnel Friction Signals

38:25 Optimizing Drop Off

39:21 Sky Flow: Building Fast and Secure (sponsor)

40:21 Product Specific Risk Models

42:32 From Shelf To Custom

43:34 Model Retraining Workflow

47:05 Siloed Versus Consortium

48:59 Cashflow Behavior Insights

50:25 Feature Engineering Matters

51:41 Macro Shocks In Data

56:07 Lifecycle Servicing Signals

57:36 Limit Management Uplift

58:55 Open Banking Pushback

01:03:53 Crystal Ball AI Lending

01:09:11 Advice And Wrap Up

01:13:24 Hawk AI: Realtime Fraud Monitoring (sponsor)

01:14:10 Disclaimer

Nobody gets into FinTech.

Kids wanna be firefighters and astronauts not very often do kids say,

Hey, I want, I wanna get into FinTech.

The challenge I've always found was the way lending

works today has not changed very much in the past 30 years.

The traditional credit systems not rewarding you for that.

Our models have an impact on if humans get access to credit,

which could be life changing for them,

we can fix this.

Not that we got to fix this, but we can fix this.

A gallon of gas here in Las Vegas ranges anywhere from 5 29 to 6 29 a gallon.

I never thought I'd see that.

With that being said, how are you using that kind of data

and you

cannot

afford to make mistakes With lending decisions,

you could put a ton of money out there on day one.

It is gonna be risky, but you will get signal back very, very.

Fast.

That's where we, you know, are, are big believers in ai.

If you ever are running an actual individual lending

decision through a neural network, you are in a world of trouble.

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Welcome to FinTech Confidential Leaders, one-on-one series where

we sit down with FinTech leaders to understand what drives their passion

for FinTech and their leadership lessons on how to get through things.

I'm your host, Ted Huff, the CEO and founder of voler, and today we have

Jamie Twist, the CEO and Casey Kaplan.

Man, you got one long title Chief Product and Commercial Officer.

That's right.

May maybe we can make it a bit longer at some point I'll third add

a third thing on, but, but yeah, T two is good for now and, and both of

these guys are from Carrington Labs.

Guys, I really appreciate you coming in.

You flew a long ways, but you're here in Vegas with us, so I'm

super happy to have you here.

It's a pleasure to be here.

Thank you for having us.

It's great to be in the studio.

Uh, so I, one of the things that as having you come into the studio, I mean.

Jamie, your, your background is really around the data science side

of the house, which I find interesting having a data scientist in the CEO

role, and it makes me curious to how you look at things and, we'll,

we'll dive into that in a little bit.

And then Casey, I mean, you've spent, what, 15 years or so building

and participating in fintechs of all different shapes and sizes.

Absolutely.

One of the things that we were talking about is how Carrington Labs has built

this really cool products that, that leans really, really heavily into

the AI side of the house, but doesn't forget about the human piece of it.

Nobody gets into FinTech, like kids wanna be firefighters and

astronauts and doctors and.

Firemen.

I mean, we could probably list out a whole bunch of typical things, but

not very often do kids say, Hey, I want to, I wanna get into FinTech.

What was it about the space that, that got you interested?

And then we'll dive into what, what really drove Carrington Labs to, to become Sure.

I'll start.

'cause I probably have a fun story and, and Jamie's is

probably more of a technical, analytical, an answer knowing him,

that used

to be the

case

I first got into FinTech because in grad school

we used to do bar hops alone.

And one of the things that I found really annoying was, at the time Foursquare

was really popular and you could check in and become mayors of venues.

And I thought there was too much friction to take out your phone and

check in and realize that wherever you're normally, you know, going, you're,

you tend to make a purchase there.

So I said, how can I link a credit card transaction to automatically

checking in on Foursquare?

And that caused me to learn how payments and cards work.

I then went on to found a prepaid debit card program at the time, and then from

there became a program manager and that evolved into a, a FinTech ecosystem

and then got into lending and credit.

So I kind of started with the payment side and then evolved from there just

'cause I was trying to solve a problem that I had and found it fascinating how

complicated payments was at the time.

I guess still is, but once you start to connect the dots, it's really interesting

and you see all these opportunities.

So Jamie, I'm guessing that yours didn't have anything to do with

the pub and maybe a pin of beer.

I'm guessing it didn't start there.

Yeah, we, we probably should have gone in the other order

'cause mine's gonna be a lot less exciting pub oriented than Casey's.

But, uh, so I've spent my career working at the intersection of data and technology

and financial services and I've always been attracted to what are the hardest,

most wicked problems in that space and.

If you look across large banks in particular, the hardest problems

they have sit in credit risk.

How do we take a vast amount of data, which is often poorly structured

and use it to make a very difficult decision about whether we should

lend this, this person or business.

And so I built credit risk models for years for, uh,

first as a, as a consultant.

And then I went in-house at a couple of banks, and then I finished up in

my banking career as the chief data officer of a global top 50 bank.

The challenge I've always found was when you're working in that big end of town

in banking, you're spending 95% of your time doing pick and shovel work around.

Data quality and lineage and extracting information and only a tiny amount

of your time to actually using it.

And so it's been fantastically liberating to move into a, a startup environment

where you can really focus on the problem itself and how do you actually use

data to make these really interesting, difficult decisions around lending.

Nobody comes out saying, Hey, there's a standard

that's been in place for decades.

Like, it's not like, you know, you're sitting on the deck on, on a Friday

night and you're like, you know what?

I, I think what we've been doing for the last three plus decades.

Decades, I think it's time to change it.

I think it's time to fix it.

So that doesn't just happen like on a whim.

But what was it that, what was the gap, I guess, really, that you

found that said we, we can fix this.

Not that we got to fix this, but we can fix this.

Carrington Labs grew out of a lending business that was and is

very much a mission-driven business.

The goal has always been to make loans to people who aren't well treated by the

traditional financial services sector and do so in a way that's safe and affordable.

So often a small amount of money for a short period of time.

By design, that lending business ran at very thin margins.

That was always the goal.

Have something that isn't gonna extract a lot of money from people, charge them, you

know, $10 for a loan, that sort of thing.

In order to make that work, it became very quickly apparent that simply

pulling a credit score for those people wasn't going to be very useful.

Many of them were thin file or no file customers.

Mm-hmm.

They didn't have a credit score and also credit scores.

They turned out not to be very predictive.

Outside of that sort of.

Kind of middle, maybe 60% of the market, people above and below that, a credit

score doesn't really tell you very much.

And so in that business, we were looking for a way to fi figure

out someone has come in front of us, we've never met them before.

How do we understand them and work out whether we should lend to them

and how much we should lend to them?

The way lending works today off the credit file and the credit score has not

changed very much in the past 30 years.

And there have been these tremendous advances in technology and data

computing and storage speeds.

The availability of kind of the digital exhaust from your, your

financial life, machine learning and artificial intelligence.

And those have almost entirely bypassed traditional lending, very

much still stuck in the late 1980s.

And we realized by starting to stitch together other sources of data,

particularly banking transaction data, you could paint a much

richer picture of an individual.

And make a far more accurate assessment of whether you should

be lending the money or not.

And we realized that IP that we built for that was itself actually quite an

important product, both in terms of being, you know, financially viable, driving the

industry forward, but also being, I think, a much fairer and more inclusive way of

bringing people into the financial system.

So when you talk about the inclusion piece of it at Caring

Two Labs, what does that, what does that actually mean when you start

looking at the product itself?

I, I, I think keeping in mind how we got there is, is really important.

And also so at Carrington Labs, for, for those of you who don't

know, we specialize in cashflow underwriting and credit risk analytics.

And I think the term cashflow underwriting is still this elusive thing to people.

People know the term.

If you go, you know, two, three levels deep, that meaning changes to people.

And it's more than just inflows and outflows of your money.

And a lot of what we're doing is creating really advanced behavioral

features that go into machine learning models to understand credit worthiness.

And when we productize our offering, that's something we keep in mind.

We generally feel that every lender and credit provider has a unique set

of customers who's trying to do their own unique business objectives and

our products tailored to that, right?

So instead of just having one generic score, similar to like

what credit bureaus tend to offer, we have personalized scores.

And we've spent years now building the capabilities to have an automated

model built pipeline based on the unique data points that a lender or a,

a credit provider might have, so that they're able to get really predictive

analytics and probability of default percentages for each of the products

that they might have in their portfolio.

What that then comes to, as you think about it, inclusion is.

Uh, when you have data sets on customers that are maybe other than

bureaus, like the open banking data, transaction data, you can actually

shape, that's your business objectives.

Well, also helping customers in, in a compliant and, and regulated way.

And, and, and that, that's how we productize it.

So when you look at our, our product suite, it is understanding credit risk.

It's understanding how should you size loan limits.

And then it's understanding if people are gonna be able to service

that once the loan is out there.

And, and combining all those together, you find that you're often able to

approve people who would normally be declined through the traditional

system while also being able to be more profitable at the same time.

So it's create, you know, these win-win scenarios.

But that is truly what we see.

Well, I thought it was interesting.

Lexin just put out a report where they look at it and like over 50% of

applicants that come in board lending aren't able to have a reliable score.

And I think we kind of talked about this earlier as we were

prepping for today is like.

Why is it not reliable?

Is it because it was the point in time?

Is it because we don't get to see all of the things that, that

go on in their financial life?

Like all of these different pieces, the people I think that get hit the

most, it hit the hardest, I guess you would say, are, are people who are

self-employed, are a gig worker, may, may have an, a side hustle, we'll call it.

Mm-hmm.

That, that don't get treated as fairly help.

Help me understand, like how you guys are approaching at Carrington Labs.

How are you approaching these unique cases?

I'll start with the shortcomings of the credit

score approach to lending.

Uh, essentially, and everybody should look at their credit

file and see what's in there.

It's, it's a. Useful exercise, but essentially that talks about the way

that you've engaged with credit in the past, in the country you're living in

now, they don't cross borders very well.

And so if you have had a credit card for a while and you've maybe taken

out a loan and paid it back, you'll have a credit file and a credit

score, and it'll probably be pretty good and you'll be eligible for a

loan using that traditional approach.

But as you rightly note, that leaves out a lot of people.

It leaves out people who are new to the workforce, new to the

formal financial system, new to the country, people who have come up in

a different socioeconomic pattern.

That means they haven't had that kind of engagement with

traditional banking and lending.

And so a huge portion of America and a huge portion of people around the world

are essentially by design cut out of the formal financial system that way.

In a way that's unfair to them, but also isn't good for the lenders

'cause they miss out on business.

It isn't good for the country because people who could responsibly use more

capital to drive their lives forward aren't, aren't getting access to it.

We will look at different sources of alternative data and your

bank transaction data will be usually a very big part of that.

And almost everybody has or can have a bank account and have transactions that

are clearly listed in that bank account.

And using that, we can draw a very detailed picture of how

you live your financial life.

Certainly there's some basic things like how much money do you make?

How much money do you spend?

Do you have savings?

Understand your p and l and your balance sheet, so to speak.

And that'll give us a good sense of whether you can service a debt right now.

We'll also look at things that are much more behavioral in nature.

So in the past, if you've had.

Uh, a, a, a cash crunch.

Let's say there was a month when it was difficult to pay your bills.

How far in advance of that cash crunch did you see that and adjust your spending?

Did you pull down your non-discretionary spend?

Some people see those cash crunches 21 days in advance.

Some people it's seven.

Some day people, it's three days in advance.

Well, we can see that in the transaction data, and that is enormously predictive

of your ability to repay a loan.

We also see it around a lot of behavioral factors.

So in the past when you've had an obligation, have you done everything you

can and deployed assets and moved money around to try to meet that obligation,

to pay that bill to repay that loan, let say a direct debit, hit an empty account.

That's also enormously predictive of your attitudes towards money and

your willingness to repay a loan.

And we find that that approach, first of all.

You can bring basically everybody into the lending system as long

as they have that transaction account and some history in it.

But it's also a much more fair way of engaging with somebody.

'cause you're actually getting to what drives their credit worthiness.

You're getting to their financial situation, you're getting to their

character, and it's a much more reasonable assessment that has much less to do

with the kind of family they grew up in or when they moved to a given country.

If you look at the last 30 years, you have millennials and

and younger who tend to be taking out less credit, which means their

credit files might not be as robust.

And part of that is because they've seen their parents go into debt.

Right.

And that's been traumatizing for many of them.

So as you, you know, have.

Less credit cards or less forms of credit because you know, you might

have a job, you might be responsible and you don't need credit as much.

You're just paying it on a debit card through your salary.

The, the traditional credit system is not rewarding you for that.

Right.

So some of these newer approaches to cashflow underwriting and, and looking at

that behavioral information that Jamie has touched on, it becomes really important

and it turns out it's really predictive.

One of the things that, as we look at the way that Carrington Labs became its own

piece, I mean, it wasn't like you had an idea and needed to test it and figure it

out and try and find data from God who knows where to, to get it into action.

Building it inside of before pay gave you millions and millions

of data points to be driven from.

How, how do you feel that has benefited?

You and being able to address the the market needs.

So we built the Carrington Labs capability inside a lending

business, and that's a fantastic place to start when you're building an

analytics capability for credit risk.

There are a number of advantages that we got by starting that way.

The first one is tremendous access to data.

So when we built our first external models, we had literally billions of

lines of transaction data on which to train those models and understand

how to build features and, and work through all of the pieces there.

The second advantage we had was the ability to get very rapid feedback.

So as we build models, we deploy new features, we try new techniques, we

can put those live in production in our sister lending business and very

quickly get a sense of do they add value and how much value do they add?

And the third one, which is tremendously powerful, and this is

very compelling for clients, is.

Alignment.

So we are 100% aligned with our clients in ensuring that the Carrington

Lab's approach and those models are fit for purpose and do deliver a

better lending commercial outcome.

Our models have an impact on if humans get access to credit, which

could be life changing for them, right?

So, so this is serious stuff that we really need to think about.

We're not just, you know, a few people in a garage who have a cool

idea and we're just throwing some tech out there to see if people

use it and, and hope for the best.

Uh, which, which ha unfortunately ha you know, it, it like for certain types of

product, great way to, you know, rapidly prototype and see what's happening.

But when you're getting to regulated products that, that have a true impact

on humans, you need to get it right.

You wanna support the borrower, but you also want the institution who's

giving that loan out to make sure that they're gonna get the money back.

So we, we think about that a lot and we, we tests on our, our own lending business.

We really think through the implications of how we're building these models.

Part of the Genesis story is, is, you know, the, the lending business

started, we thought there were better solutions than bureau credit scores.

We got into cashflow underwriting and, and built these sophisticated models before we

knew it was called cashflow underwriting.

And we've iterated from there.

And I think part of the genesis story is, is, you know, I think

JMU was in, in U Europe or the UK having a meeting with banks.

I, I was in, in the US doing similar things, just talking to some of

the connections we had and we were explaining what we were doing before

we officially launched Carrington Labs.

And people were saying, wow, that sounds great.

Like, can you roll that out?

So we realized there was market opportunity and demand from other

lenders to use this type of solution.

We realized we kind of cracked it on the lending business

that we were involved with.

There was this synergetic opportunity where it all came together well.

And something I hear a lot working with fintechs and financial

institutions is that they can't afford.

AI tools to be a black box.

They can't do it for compliance.

They can't do it for payments, they can't do it for lending.

They do can't do it for customer acquisition.

And frankly, I mean, we all know the, the regulators have, have stated

very clearly, no AI whitewash or no AI washing if, if you're using

it, it is gotta be explainable.

All these different pieces come in and I think it's been really

interesting that you guys launched an MCP server focused approach to this

that was purpose-built specifically to identify the credit risk models.

We talked a little bit about how the perspective of the agentic

workflows are changing over time.

What, what would you tell leaders that are in any sector of.

Regulated financial and financial technology, what would you say to them?

How to look at the AI architecture and what, what is it?

What is changing for them?

So I'd say a couple of things to financial sector leaders.

The first one is, as you look to deploy ai, be very thoughtful about the use

case and whether that use case is fault tolerant or fault intolerant.

So there are some areas such as generating marketing copy where you don't wanna

make a mistake, but actually depending on what you know, the nature of what you're

putting out there, it might be okay if there's a quality, if there's a quality

issue, or if you know it doesn't say quite what you thought it might say, then

there are many areas, probably more areas that are fault intolerant, and clearly

lending is one of those you cannot afford to make mistakes with lending decisions.

You cannot afford to have, I think the, the law is appropriately very

clear and very strict on that.

And so if you're working in a fault intolerant area of your business, when you

deploy ai, you always want to make sure you're embedding it in a workflow that

puts a tight layer of control around that.

And so the way that we think about that is we use a tremendous amount of AI in

the upstream model creation process, but then as those models come together,

we have what we call a control point.

And that's where a human reviews, the model prototype that's come out of ai.

And checks every feature that's in there and signs off that this is an appropriate

way to be making credit decisions.

And from that control point forwards, everything is deterministic,

just repeatable, statistical, explainable, same input, same outputs.

And we can tell you exactly why someone did or did not get approved.

Casey, like one of the things that I, I find slightly hilarious is

that a lot of companies are putting.ai on the end of their, their URL.

They're saying, Hey, we have this new AI tool without really explaining it.

And really in a lot of cases it, it feels, and correct me if I'm wrong,

but it, it feels like it's just an automation layer that's sitting on top

of it, like the next level of robotic process automation, maybe using a little

bit of extra data, but not a whole lot.

You guys have approached this from more of a. Correct me if I'm wrong, more

of a, like a deterministic type layer.

Yeah.

Why does the distinction matter when we start talking about

deterministic versus inference?

Yeah.

It matters a lot as, as, as it turns out.

So if you take lending for example, a huge amount of effort

goes into origination, right?

Should you approve someone for a loan?

And, and there's some good companies out there that are automating

workflows and, and they're partially using AI to do some of those things.

I think there's the broader question of do you need AI to really automate

some of those things or could you use traditional workflows to do that?

But that aside with lending, you know, basis points matter.

Right?

There's the regulatory piece where it has to be explainable, right?

You need to make sure all the features, you can understand the features in the

model, how it's impacting that, and you can explain that to regulators.

That's also important for you to improve your credit criteria, right?

If people are defaulting, you wanna be able to update the criteria that you're

approving people on when it's more of a probabilistic solution where you're

utilizing AI more to make those decisions.

You can't explain it.

And, and we have helped lenders where they're quite literally calling

the open AI API with borrower data and effectively asking cache, pt,

should we lend to this person?

And their feedback was, oh, we, we get different answers

at different times, right?

We spend a lot of time thinking about where does it make sense to actually

use AI because you get benefit from it versus using machine learning or, or other

approaches which, which are deterministic.

When we look at some of our.

AI forward solutions.

So, so we do have an MCP, which can communicate the results of our models.

I, I think that's a good example, right?

So if someone is using an ag agentic workflow, and it is early days for

that, still to get the contextualized information back is quite helpful.

But also if you have a, a credit team or a support team who's trying to understand

why was the MCB can be quite helpful there because they can effectively ask questions

to the MCP server around the specific borrower without having to read through,

you know, hundreds of lines of schema and, and, and seeing all the features.

Uh, I think that is evolving very quickly and the use cases

are evolving really quickly.

But I, I think right now in the regulated environment, there, there

is that distinction where when things can be probabilistic and when it is.

Sometimes Okay to make mistakes are when you need to get it right.

And I'd also say it depends on country as well.

So we support lenders globally.

In Australia, for example, you have to explain why you approve someone

for a loan in the United States.

You have to explain why you decline someone for a loan with

adverse action reasons, right?

So in our solutions, we have a, in our credit risk model solution, we have

a solution where in our response we will give you score rationale where we

have promoters and detractors, right?

So kind of you're, you're covered on, on, on both ends and, and both

the jurisdictions and those map to the features that are in the model

that the lenders approve themselves before it goes into production.

So we think that transparency with, with lenders and what's going to

their risk model is really important.

But while also keeping the explainability, you know, it, it's

interesting as you, you talk about that, it makes me curious because ev

the many layers of what we call ai.

You mentioned machine learning.

Some people will call that ai.

Ai,

yeah.

Some people will, will look at the inference of a particular

information as AI based off of data.

Then you've, you've got the large language models and you've got all

these different pieces into it.

Help me, help me and everybody understand like how, how it's different between

just, you'd mentioned throwing it into an open AI API maybe with a little

bit of context, maybe with a series of prompts, maybe, maybe, heck we could,

even now with, with Claude Finance being launched here in the last six months

or so, how, how are we making sure that we can maintain the explainability?

Maintain the fairness and maintain the,

I guess I wouldn't say maintain, but ensure that the bias doesn't creep in.

'cause every time we look at something, when I'm using it,

it wants to tell me the answer.

I want to hear, not the answer that I, I need to hear.

How are you at Carrington labs?

Like balancing between, I want to hear their this versus you need to hear this.

We could probably tag team this one a, a little bit here

from different perspectives.

I, I think that's where we have the concept of a control point.

So, so downstream of the control point solution, we believe everything

has to be explainable and that's where we rely on machine learning

and deterministic solutions.

Upstream of the control point.

That's where we, you know, big believers in ai.

I'd say specifically gen AI to get the creativity when you're using Gen AI

to try to get more deterministic like features Still not guaranteed though.

There are a number of strategies that are emerging and the solutions are getting

better and better and better every day.

There's the concept of guardrails that you can put in, you can com

play with like the, the temperature of what the LLMs are doing to try

to get more consistent outputs, but it's, it's still not guaranteed.

So there's risk from that regulatory perspective when we

look downstream past the control point where it has to be explained.

But that's where we think, you know, machine learning ultimately produces

what regulators want right now and that that's been tested and, and it's

come inplace so that com combination right now is, is really effective.

So if you are using AI internally to build your tooling, that is one thing, right?

And there's a number of strategies and there's a concept of skills

are emerging and utilizing various markdown files to provide extra

context to the agents as they're building is, is commonplace at, at.

The time of this recording, maybe by the time it rolls out what the

world will, will, will change.

But I think when you are in production in a regulated environment and you

are not able to make mistakes, so there's massive regulatory risk

for, I guess, making a decision that would adversely impact someone.

You, you can't explain it.

That's what you need to be mindful of when either building it yourself or,

or working with third party providers.

As Casey was talking about that all that comes in my mind is, like you

mentioned, things are changing so fast.

Mm-hmm.

And, you know, it feels like every day that, you know,

open AI model ahead on this.

And then the anthropic model's ahead on this and then the, the Google Gemini

is head on this and you got gr like all these models are like jockeying for

being the best on that two part question.

One, how in the heck do you keep up with it?

And two.

How does that benefit us to remove the bias that we talked about?

To take a step back, there are two broad categories of what you might

call ai, and they're very, very different.

And I think a lot of people blur them together in a way that makes

it hard to resolve these questions.

When most people think of ai, they think of generative AI and related

techniques that are based on what we call neural networks, which are essentially

mathematical recreations of the brain.

Enormously powerful, enormously flexible, absolutely black boxes.

No one has a good way of understanding what's happening inside.

Randomness is a core element of how they work.

They don't work without some random elements to them, and so you will

get different things every time.

And so most people, when they think of ai, they think of those

neural network based technologies.

Then separately we have a whole bunch of things like machine learning

and other statistical techniques, which are just more statistical and

mathematical, and those are generally explainable and they're reproducible.

You can put the same things in and generally get the the same answers out.

When we think about lending and credit there, there's tremendous power in

being able to use generative AI and other neural net based technologies

and approaches, and using that to give yourself insight into data, in order to

set up ways of building other kinds of models and all sorts of explanatory power.

But if you ever are running an actual individual lending decision through

a neural network, you are in a world of trouble because you will not know

what answer it comes to, and you may get a different answer every time.

So those are tremendously powerful tools, but when it comes to actually making

a decision about an individual loan.

You should always be in that more deterministic and that explainable space.

Machine learning and, and, and, and related techniques.

And so to answer your question, when we think about the relentless march

and progress around these models, we always think about how can we make our

explainable, predictable, deterministic machine better using whether it's Claude

or Gemini or the the latest open AI models and so on, but not hand them the keys

and let them make decisions on their own.

And I think despite the rapid rate of progress, I think the world is

very far away from a large language model that actually has that level

of transparency and predictability.

You know, watching you guys, we started talking late last year

about what Carrington Labs was doing and the approach that you were taking.

I mean, you guys have been crazy busy.

With partnerships and announcements and, and all these different things, and I

wanna get a little bit, let's, let's get into some of the specific pieces, right?

Because these are real products.

You do have real partners and you are delivering real results.

Walk me through like a real life scenario where, let's just say a FinTech

company is declining 40 plus percent of their applicants, and they're starting

to see their margins just get, just demolished by, by all these declines.

They've got their, their cost of acquisition has gone through the roof

because they're having all these declines.

But what does, what does it look like for someone that comes to Harrington

Labs and says, this is my problem.

How do I, how do I fix this?

We'd normally start by understanding what they're trying to achieve, right?

Because they might have a problem, but it ultimately comes down to how does

that compare against their objectives?

The correct economic answer is, is how do we maximize our margin, is usually the the

right thing they should try to be doing.

But not everyone has that mindset.

So we sometimes see startups who are, let's go with that though.

Let, let, let's go down that path.

Let's, we, we're trying to maximize our, all right, this is the fun one, right?

We're trying to maximize margins, so

contribution margin and to dollars.

Yeah.

Yeah.

Right.

Yeah.

How do we get the most dollars, right?

At the, the margin level, and there's a few pieces to that I

wanna get to lending and credit.

So one is, are who are we approving?

The second is of the people we are approving, how much money are

we giving them, and how is that priced and what is the duration?

And then once it is originated, how is that loan performing or

that type of credit performing.

And those are the three areas that we, we tend to look at.

And, and you can use that same concept for Prequalifications as well.

Well, you had mentioned earlier today that motivation.

Yep.

How do you determine like whether or not your lending process has too

much friction, whether or not the motivation is there to the customer?

Like how is Carrington Labs helping identify maybe some excessive, I'll call

it excessive friction in the process?

So we love looking at lending funnels.

This is like, it's probably a passion area of mine and breaking down the

data, but, but it is a ton of fun.

There are different types of elasticities, right?

So pricing elasticity is, is clearly one of the things you need to consider if

you're, you're pricing too high, people might create an account, but they're

not gonna necessarily take the loan even if they're approved, which means

the marketing dollars you're spending are not gonna translate into revenue.

Then there are other things in the actual funnel itself.

So if you are, you know, doing, in some cases there are borrowers who are

really sensitive to doing a pull from a bureau 'cause they're worried that

that's gonna impact their credit score.

There could be other borrowers who, who are maybe a bit hesitant to use

scraping and open banking, right?

To pull in the transaction data.

It really just depends.

So depending on how motivated a borrower is to take out the loan has

a big impact on throughput, right?

If people want the loan, they're much more likely to go through the process.

Now as a lender, what I would encourage everyone to be doing

is have your analytics in place.

Look at every single step in your process.

See why there is drop off and do research, right?

There's quantitative and qualitative things you can be doing to understand that

at, at each step of the way, introduce a, a financial health tool, right?

That might be one way to get people to aggregate more of their bank account data.

Let customers know that maybe if they've been approved for a smaller amount

using some of the traditional approval processes and they're interested in

more, sync, more of their accounts, and use more of a, a cashflow underwriting

approach to, to better understand their serviceability and, and, and maximize

some of the limits they, they can have.

But, but breaking it down each step of the way I think is really key.

But it does come back to the lender's objectives, right?

It's, it's not just about saying yes to more people if those people are

high risk and not gonna translate to margin and, and dollars on the p

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I believe this is a completely different risk profile than somebody who's looking

to, to get a loan, to buy a new TV or to get braces for their kids or some,

something more lifestyle oriented.

How, how do your models adjust for these types of variances?

Because the, the standard loan seems relatively straightforward.

It, and I'm, I'm probably oversimplifying, but it feels

like it's pretty straightforward.

You know, you got these things.

You pull this data, you look over here, you figure out

what your risk tolerance is.

But when you have that many things in a closed ecosystem, I can only imagine

the way, as a data scientist would look at it, changes the perspective.

That's that's absolutely correct.

And we find that the same person can have, or same business, can have a very

different risk profile for different products and in different circumstances.

And I think one of the big shortcomings of the traditional ways of lending is that

people tend to be treated as constants, even in these different contexts.

So the way that we think about that is that if we're working with a

lender who has an existing business and has an existing set of data.

We will retrain a custom model just for them on that data.

So the risk factors that get selected for inclusion in the model, the way that

flows through to the, to the, to the elasticity matrices, all of those reflect

the actual experience of that particular product for that particular lender,

for that particular customer segment.

And where lenders don't necessarily have that data, we work with them to

start them down a path which will enable them to get that kind of capability

fairly quickly and dial up the portion of their lending decisions that are

based on cash flow underwriting.

And you can very quickly see that performance improvement even in

a situation where you're starting with no data from the beginning.

So, so in that scenario, we will have off the shelf models

that someone can utilize from day one if they have no data.

And then once they put loans out there and there, there's a couple

strategy they could use for, for how aggressively they want to grow.

We'll get that signal back and once we have that signal on performance, we

very quickly start putting them on a custom model, which might have some of

the standard off the shelf features and weights along with the custom signal we're

seeing from their specific portfolio.

And over time, that will go more and more and more towards their specific portfolio.

And within that, you could put a ton of money out there on day one.

It is gonna be risky, but you will get signal back very, very fast.

I think the more conservative and, and and safe thing to be doing is,

is, is ramp up over time, right?

So, so only put out a, a small number of loans, get the signal back, refine,

improve, and iterate really quickly.

It's kind of like your traditional like, like product mindset.

On, on, on how would you go things unless you're.

Or maybe if you're VC backed and blitzscaling and, and you want to take

all the risks, take that first approach, but otherwise go, go with the latter.

One of the things that you've, you've mentioned a couple times is data learning.

Adjust the model data, learn it, adjust the model.

We've mentioned that a few times.

What a lot of people that I work with are concerned about is,

Hey, I, that means that I have to understand how to adjust the models.

I have to understand how this decision impacts that decision.

How, how are you guys at Carrington Labs looking at, I hate to call it

this, but like the models learning from itself and learning from the behaviors

that happen based on the decisions that have been made, based on the

factors that come into play, how much.

Real self-learning actually can happen.

So there's quite a lot of learning that happens as

the model gets more data over time.

And if a lender is already lender with some scale using traditional

mechanisms, we can very quickly use that data to put a model in place.

And then that model will almost always be immediately

additive to what they're doing.

But then as more data flows through, as we get a wider range of limits, this

often leads to a much wider range of limits and higher average balances.

We will retrain that model on a regular basis and it will get sharper and sharper.

Uh, generally we, what we, the way we work with clients is we

will host the model for them.

We see their data coming through it because we're, we're doing the scoring.

And we will just retrain that model in the background.

Uh, and they'll just see that performance improvement.

Um, we do ask them to be across and understand the individual factors

just so they feel comfortable with what's going into those decisions.

But it's not something where they themselves need to be deep

data scientists necessarily.

I'd also just say one thing.

We, we never, you know, cross-contaminate data.

So a client's data is a client's data and we don't share that

with, with, with someone else.

So the models are purely for the specific client.

But when you look at lending it, it, it's really interesting because it

hasn't, I'd say, innovated at the pace of, of other industries and, and

I always like to point to e-commerce 'cause everyone gets e-commerce right?

You know, so e-commerce, you have the trend of, you know, omnichannel

and personalization lending, we don't really have that, right?

So when you're looking at risk, traditionally what you're doing is,

is looking at a bureau score and then you're using these generalized

risk bands and that's how you're evaluating if someone should get credit.

I think that's fairly antiquated, right?

And with the new way of building things, and one of the things we offer

at Carrington Labs is you can get the probability of default percentage.

You can actually send the unique risk of each borrower, which

allows you to be super sharp.

And if you understand that, you can then map that to the underlying

economics that each borrower will deliver in the lifetime value.

So you have a really good sense of the overall p and l performance.

And that's just, I think people get that concept at a high level,

but it's not there in practice yet.

So as the data is available, as we can retrain models really quick, as the

models can be personalized, each product that a lender has, you start to see

this and it's a really exciting time.

So Jamie.

My brain immediately goes over to the data side of the, the data science side of it.

It's like, isn't there benefit in, in having consortium data versus making

the models siloed from each other?

Like, isn't is the, does the benefit, does outweigh the risk when you do silo them?

Or help, help me understand why, why it's siloed and not consortium level.

So the reason that we keep each lender's data fully separate is for

the interests and benefit of that lender.

A lot of the, the, the banks and non-bank lenders we work with, they really want

their data to be fully isolated and no one else gets the benefit of that.

And we completely understand that.

Now, we do offer, if someone wants to be part of what we call the, the Signal

Sharing Consortium, they can do that.

We, we will still never share the actual data, but we will potentially share

things like signal weights across.

We generally find lenders really prefer to keep their own data to themselves.

And we also find that most models actually work very well with

just that one lender's data.

It's more tailored and specific to them.

And if that lender has even a little bit of scale, that's usually enough to give

them a pretty sharp, fine tuned model.

I think that's interesting because I, if I were lending money, I

would wanna know whether or not Casey paid back his loan or not before I decided

to do it, versus looking at a credit report that shows that it paid it back.

But did he pay on the due date?

Did he pay, like the date that he got, the, the statement invoice?

Like you would think that they would want to share that kind of data to understand

the propensity of, of repayment.

Is, is that not important?

Well, this is one of the real.

One of the really remarkable things about using that cash flow data, that

bank transaction data, is that if Casey's coming and applying for a loan,

sorry, Casey, we're not mean that

Yeah, you're sitting

I, I, I, yeah.

No, I'm very credit worthy.

Everyone should know that.

Uh,

if a customer comes and applies for a loan and consents

to having their data shared, we can see their financial activity

at a high level of detail.

So we can see things like, you know, we see a paycheck coming in every month,

but then this month it disappeared and then it came back the next month.

So what was happening there?

Or we can see a regular credit card payment going out, but then we see that

coming down, we see interest charges starting to creep in, or we can see

payments that used to be completely on time, starting to drag later and

later, whether that's a loan payment or a utility bill, or something else.

You can, even with the right, with the right logic, look at

informal lending between friends.

If your cousin sends you money, is that a loan or a gift?

And did you send it back and did you send it back after you'd already

spent a bunch of your next paycheck or did you send it back first?

Sting?

All of these things are tremendously powerful and predictive.

And with the right logic layer and the right model features, you can

extract these tremendous insights into somebody's financial behaviors,

into their character, their attitudes toward debt from that transaction data.

Not, not to cut you off Ted, but, but, but, but I, I just

wanna drill into that one 'cause it's really important, right.

So I, I think the.

A common assumption is if you have more data, you can build like a regression

model and see what's significant across a, a large number of people.

But that, but that's an sense of generalizing things.

And when you get to feature generation, it is so important, the quality of

the features that you can generate.

So we see it all the time.

People will brag about having thousands of features in their model, but those

features are like, literally, it's like grocery spend in seven days,

14 days, 30 days, 45 days, 60 days.

And it's just like a bunch of the same thing over and over and over again.

And they're using this brute force approach.

Doing it is not wrong, right?

Like, like having one of those features is good and, and figuring out if

the seven day grocery spend is more predictive than the, the 90 day gross

like sprint is, is probably worth doing.

But having that logic layer, and one of the things that we, we,

we use quite a bit of AI to do fairly advanced feature generation.

The predictive quality of, of the feature is so important.

That's where we get to these behavioral attributes that,

that, that power these features.

And, and we see quite a bit of model uplift from e even a handful

of these advanced features can have like a significant impact

on, on the genie of, of a model.

Yeah.

It, if I had David Glaser, the CEO of Dal on, and, you know, we were talking

about a, a number of d different things and the, the precursors to the economy

and the types of things that they're able to see as a payments provider that

is mostly intra bank payments providers.

And seeing how just before the president got the inauguration, how.

Basically all spending stopped and then it started back up and didn't take fully off.

And then seeing how the, the differences in the inflation

and the cost and all that.

At Carrington Labs, you guys are able to look at things like the cost of fuel.

I mean, as we're recording this right now, the cost of a gallon

of gas here in Las Vegas ranges anywhere from 5 29 to 6 29 a gallon.

I never thought I'd see that, but we have that going on and I'm imagining that

that starts to impact a lot of the other pieces that you all are seeing as well.

I bring that up because I think one of the big pieces that we don't, the traditional

FinTech side of the house, doesn't really think about the economic impacts.

To these types of models.

With that being said, how are you using that kind of data, and if so, how?

Yeah, so we use data.

We do get a lot of insight overall into what's happening in the economy and how

are people performing from the point of view of making lending decisions.

What's most interesting to us are two things.

First of all, to what extent do changes in the broader economy

affect this individual person?

And so we'll look at that by looking at their own financial history, and

you see some people where their work really ebbs and flows for the economy.

You know, a lot of construction workers, for example, will get very,

very busy and then they'll be less busy at quieter times, and you can

see over a period of time, to what extent does this person have individual

economic exposure to the economy.

Then the second big thing that we look at there is how do people

respond to those moments of.

Abundance or, or scarcity.

So you talked about people, you know, filling up, filling

up their cars with gas.

One of the things we look at is do p, does somebody shift down to cheaper

providers, whether it's of gas or groceries or anything else when times

get tight, some people you'll see them stop going to Safeway and start

going to Costco if their balance is running low and some people won't.

That kind of behavioral response to your own financial situation is an

enormously important part of somebody's credit worthiness, but also actually

gives you a lot of insight into kind of broader economics as well.

As you're talking about that in my mind, I was thinking Whole

Foods to like the grocery outlet, but that's a big jump, not a small

one like you were talking about, but it, but also gets me thinking

about, you know, a lot of times when.

Not in lending, but in a lot of places in FinTech, the underwriting

process tends to be a point in time.

Yeah.

Have, have you started to see a shift of point in time underwriting to more

of a ongoing recursive underwriting process to decide I need to tighten my

internal policy, but for this individual based on the model, you know, and,

and then even, I'm going on a tangent here, but like in my mind it's like,

alright, so we see that this person owns a vehicle that has a very large engine

that gets extremely low gas mileage.

We're seeing them continue to, to use these purchases at these

pumps, but the income hasn't increased to go along with it.

Maybe we make an adjustment.

Is are you seeing that shift?

Is it going that detailed?

It, so it's a really interesting space, and when we started, our first

product was a credit risk model.

So it was looking at scoring a time of origination.

And the data's temporal in nature, like you pointed out.

As we've evolved, we, we really now track the full borrower lifecycle.

So we now also think a lot about the servicing element of that, right?

Because once, once you write a loan, the, the money's out there.

So then it's about what can you do to make sure that you can recover those funds,

detect early distress in a portfolio, and we have a cashflow servicing

capability that looks at that, right?

So if the, the open banking data is being persisted, if the connections

persisted, we can see like, are are these changes happening?

Right?

If fuel goes up, is there less discretionary income?

And does that mean that someone may not be able to pay the loan

or whatever that might look like?

So we do think that lenders should be thinking full

lifecycle, not just origination.

And by having the capability to rapidly retrain models based on new signal

that's coming in, whether that is at origination or on the servicing end as

well, offers a, a huge uplift, right?

But, but it's, it's early days for that, right?

There's not many people who are doing, I'd say, advanced

things on that servicing piece.

Right now, what what tends to happen is, you know, you'll, you'll get a monthly

report on how the portfolio is performing, and, and that's what most people do.

I think being proactive based on what the data's telling you in your real

time offers a lot of opportunity.

And, and I think just to jump in on that, there are a number

of products where you can actually adjust your exposure after origination.

So credit cards being an obvious one.

Yeah.

Where somebody may have a, a certain limit, they may not be using all of it.

And we can go into a lender with an existing book of business, look at

the, say they have a hundred thousand credit card holders, and say, okay,

of these a hundred thousand, these 5,000 are, we see some stress emerging.

So if you're in a position to pull back some unused limit,

that's probably worth considering.

And then conversely, what we much more often see is, well, these 30,000, you've

had a very conservative setting on them.

They are doing quite well, and they can actually, if it's appropriate, and

lines up with regulatory objectives and your funding position and so

on, you could actually consider meaningful increases to them as well.

Yes.

So one of the things we keep in mind is how do we drive

more dollars to your margin?

And I, I don't know.

That's true for all the technology providers out there, but ultimately

that's what a lender's trying to do.

And like Jamie said, we see so much uplift by doing the limit in line management and

it's something that offers a lot of value.

People know they should be doing it, but they just don't

necessarily have the solutions in place right now to be doing it.

Well, hopefully you guys don't mind, but I wanna do a little bit of a side quest

because you mentioned something that, that is near and dear to my heart and

it's something that I do a lot of work around and, and I speak about a lot.

But you mentioned open banking and in the US open banking has started to feel not

so open with, with financial institutions like JP Morgan and the most prominently

known deciding they're gonna start to charge to get access to this data.

How is that impacting the way that your customers are leveraging

data, the way your customers are?

Accessing the data.

I might let Jamie start with that one.

So, Ja Ja.

Jamie, I'd say is, is our, our expert on this.

So he helping write some of the Australia standards.

So what do you think, Jamie?

Yeah, so I'd say a couple of things.

First of all, I think any bank that wants to put up barriers to data

sharing from its customers, I think is showing a lack of confidence.

'cause what they're doing is they're saying, we think we need an asymmetric

playing field in order to compete with attackers and other lenders.

And so I'm surprised that JP Morgan, which I think is a very capable bank, feels

that it can't fight on a level playing surface for a customer's lending business.

Now you said that I also think that ultimately this is data that the

customers have created that they own.

And I think.

The bank, just from a, from a, a sense of fairness, should enable the customer

to take that and share that with other providers, uh, as they think best.

Our experience has been when barriers to data sharing go up, it's not that

data sharing goes down, is that data sharing moves into the back alleys and

it gets done through people scanning and emailing bank statements to somebody.

So it's done in a much less secure and much less coordinated way.

So I think it's a step backwards for the financial sector as a whole, if

we raised barriers to sharing data.

It's so interesting that you mention the email piece

of it because a previous guest, a company called Lno, they're, they're

helping accounts, payables, accounts receivables, all these different things.

And we were jokingly saying that, you know, APIs are really

cool and everything, but.

A lot of people live in email and business is done mostly in email.

So is email like, like a regression away from APIs to get the data in

a more free and unrestricted way.

So that's really interesting that you bring it up from that perspective.

Casey, you, you're chomping it a bit to say something.

No, there's different sides to this, right?

So I think it's very easy to say let's charge large financial institutions

who are very profitable or, or we should make, they should not charge

users to access the data, right?

But when you look at open finance more broadly, and if you say

smaller organizations also have to share the data, which they should.

But the regulation puts so many restrictions on how that data could be

shared and the standards, there's a huge cost for those smaller organizations.

So I think you just need to find the balance, right?

It, it might have a significant p and l impact to build out the infrastructure

based on what the regulation says to allow customers to access the data.

I absolutely think customers should be able to access the data, but we just

need to be mindful of, of what happens.

And I, I think some of the open banking standards that have been proposed in

the US are a, a bit more open than what's happening in, say, Australia, for

example, but in Australia there's a lot of restrictions even around derived data.

So if you are using the open banking data and generate insights

off that, you're quite restricted on, on where that data can go and,

and how you can use it as well.

So I think we just need to think through end to end both sides and,

and, and get to the right outcome.

It's been really interesting watching open banking in Europe and the discussion that

I've had a lot around that is more of.

That was meant to make it easier for a customer to move their data, to

simplify the process to be better served.

And, and I think in the US it has gone to the perspective of how can

we, how can we monetize on the data itself, not on the services we deliver?

And so that's the piece that gets a little bit disconcerting for me is that

as, as a business and as a consumer, I wanna freely be able to have my data

and be able to send my data where I want to do, where I wanna send it.

And Jamie, like you said, if I'm stuck in a scenario where I have to email

it so that I don't have to pay a fee for it, I'm probably going to do that.

So I'm getting into my favorite section of every show.

And so one of the things that, that we look at, lending and

AI supported lending you.

It's estimated by 2037 that it's gonna be a $20 billion space for

loan origination using AI tools.

And you look at that, what I want you guys to do, and yes, I bring

out the good old crystal ball.

An actual crystal ball.

An actual crystal ball.

That's

exciting.

So what I want you to do is I, I want you to look deep, deep,

deep inside of this crystal ball.

And Jamie, I'm gonna have you go first.

What I want you to do is I want you to look in, go out three

years, see what three years is and say, yeah, not good enough.

Go out five years and then come back and tell us what is it that you see happening

using artificial intelligence and lending.

So I think looking at five years, there'll be two big shifts in

lending driven by artificial intelligence.

The first one is.

The actual process of applying for credit, getting credit, deploying that money

will be embedded much more in a much more native way into our financial lives.

Our agents will be out there seeking credit for us, pulling it back and

deploying it in a way that's that's most efficient for us without us

having to go through applications that'll seem very archaic.

Then the second thing that will happen will be lenders themselves

will be making decisions based on a much richer and fuller data set.

That gives 'em a much deeper insight into a customer, whether it's an individual or

a business, their credit worthiness, how they will respond to different financial

situations, and they'll be able to deploy credit in a much more precise way.

And overall, the availability of credit to the right people in

businesses will be much, much higher.

And there'll be a structural and permanent boost to the economy.

Off the back of that,

Casey, what do you see?

I think when I look five years out, I see what tends to happen is regulation

lag innovation, so I think there's gonna be quite a bit of policy update that

is more supportive of AI and AI driven processes as the AI solutions also evolve.

The other more general thing I see happening is, is friction is gonna

be removed from the process, which is not too dissimilar from what Jane

was saying, and we see it across.

Every industry and every technology that's been successful.

When you look at phones, right?

We had telegraphs and landlines and car phones and feature

phones, and now smartphones.

I think lending is gonna go that direction as well, right?

So whether it's new data that's being used, new ways of modeling, new approaches

to understanding and risk and maximizing margin, new types of accessing capital,

I see all of that coming to fruition.

I don't think it's a matter of if, it's just a matter of when.

Do you think we're gonna see the number of companies offering lending

expanding, or is it just the same folks maybe with a different logo?

So we see, especially in the US, a number of the financial

institutions consolidating, and some of that is because of legacy.

Issues of only being able to have like a state license in one state at a

time from, from a very long time ago.

So I think we will see a lot of consolidation because smaller financial

institutions who are lending right now just don't have the capital to innovate.

I think once that starts to plateau, I think we can then see more innovation

happening and there'll be more disruption.

So, so a little bit of both, but we'll have to see how it plays out.

I was having this discussion with a, uh, scaled car retailer that

was trying to work out whether they should be involved in, in lending

directly or not, and we don't know the answer, but there is a lot of value in

having the organization closest to the customer and the flow of data coming

off those interactions and transactions, having that company be the lender.

'cause they have more data, better access to the customer than anyone else.

So if we can build out the tooling and the systems that enable.

Even non-financial businesses, perhaps smaller businesses to have

access to cutting edge decisioning technology, limit setting origination

systems, servicing capabilities.

I think you could see lending actually spread much more widely

across the economy, potentially in a very positive way.

So if that happens, then as, as some of the things that are specialized

now become commoditized and if you're a product person as well, like that's

where your product market fit, like really comes in from that end consumer

that you're interacting with, right?

So it's no longer about do you have the best technology?

If everyone has access to the same technology, it's what's the proposition to

the end customer and can you position that in a unique way and your specialization

that will ultimately make you win?

And that's really exciting.

Alright, well last question of today is, if I wanna know your over your

perspective, if you were to tell somebody over here that, hey, we've got one piece

of advice for you, that if you follow it, it'll change the game for you.

What would it be and why?

Jamie, we'll start with you.

So my advice to any lender is bring together the process by which you

approve or decline a loan with the process by which you set the terms of that loan,

the limit, the duration, the pricing.

Too often those things are separate, and as a result, lenders miss out on

the opportunity to optimize the value of the customer that's there before

them because they don't think deeply enough about what's the right limit,

what's the right duration, what are the right terms for these loans.

So Casey, I would love if you could take it from the approach

of the product, product market fit, commercial side of the house, what

advice would you give somebody who's looking at, do I build a technology?

Do I market a technology?

Do I become a lender?

Do I expand my lending?

Like look at it from that perspective.

How, what advice would you give them if it was the only thing

that they could use this year?

I'd say like the only thing that limits you in life is yourself.

And you need to think about what that means.

I think if you are a lender and you specialize in the relationship

you have with your customer, I would not be trying to build my

own technology in house, right?

I think there's great solutions out there, uh, from the origination, underwriting,

servicing side, and I would buy the best of breed and bring those together in

a way that, that Jamie just described.

And I think the relationship you have with your customer, those

interactions is, is the most important.

I think if you have a generic product, you're not differentiated.

So see, how can you differentiate and really understand what are

the pain points of your customers?

And that will require some testing, like chances are you

will create some products that.

Customers might not use or, or don't work out the way that you want.

And I wouldn't take that as a defeatist approach, that you

shouldn't try anything else.

I think it just means you need to take the learnings and try again.

With that said, I'd be mindful of, of spreading yourself

too thin, too fast, right?

Like, find your niche, get that flywheel going, and, and get that

working, and then think about iterating over and over and over again.

I think a lot of fintechs fail because they, they do too much at once, and

then you probably have the opposite end of the spectrum where there's

a bit of a innovator's dilemma for more of the established businesses.

They're not willing to try new things and, and they ultimately get outpaced by

some of the emerging companies out there.

Well, guys, I really appreciate you taking the time out today to sit down with me.

The, the one thing there, there are three things really that, that

I, that I took away from today.

One is that it's not a simple yes no scenario.

The thing, the second thing that I, I found interesting is that.

Just because you're using the data you have access to from within your silo,

doesn't mean that you're restricted, that you can leverage the data that

you have to still make great decisions.

And then last but not least, is that we are still in the early stages of this

technology and what it can and can't do.

Well side, thank you so much for joining us today.

It's been really good to have you.

If you got value from this conversation today, be sure to head over to

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on in all the areas of FinTech.

And as always, keep moving forward

as we wrap up today's episode.

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