Pat Moye, Executive Director of Product Innovation at Deluxe, unveils the surprising reality of accounts receivable departments still trapped in manual processes despite technological advances elsewhere in business. As he aptly points out, AR professionals often describe themselves as "cash detectives," spending valuable time hunting down payment information across disconnected systems - a critical bottleneck in business cash flow.
The heart of modern AR transformation isn't flashy AI algorithms but rather solid data foundations. Pat emphasizes that payment information typically exists across multiple fragmented systems: "The data from ACH and wire payments is with the bank. The data from lockbox payments is with a company like Deluxe. The data about open invoices is in their ERP." This fragmentation creates significant challenges when attempting to implement automation, as these disparate data sources must first be unified and standardized.
Organizations seeking to modernize their AR operations should resist the temptation to overhaul everything at once. Instead, Pat advocates for an agile approach that tackles specific pain points incrementally: "Don't think about making these big swings. Think about the incremental gains you can have by just changing some pieces of the puzzle." This methodical journey begins with consolidating basic payment data sources before expanding to incorporate unstructured data and advanced analytics - allowing teams to transform from operational cost centers into strategic business partners.
Discover how Deluxe has evolved beyond its 110-year history as "the check company" to become a comprehensive payment and data solutions provider, helping businesses eliminate AR friction points and accelerate cash flow. Whether you're just beginning your automation journey or looking to enhance existing capabilities, this episode offers practical insights for finance leaders ready to transform their receivables operations.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:01):
Welcome to this special four-part series titled
Modern Finance, sponsored byDeluxe, where we explore the
future of financial automation.
From treasury to accountspayable and receivable, we're
diving into how AI andintelligent automation are
transforming every corner offinance.
In each episode, you'll hearfrom leaders at Deluxe who are
(00:23):
driving innovation anddelivering real-world results.
Whether you're navigatingcompliance, fighting fraud or
connecting the financial dots,this series is packed with
insights you won't want to miss.
Speaker 2 (00:37):
Hello everyone and welcome to the Leaders in
Payments podcast.
I'm your host, greg Myers, andthis episode is part of our
four-part series we're doing onmodern finance being brought to
you by Deluxe.
So today we have a very specialguest, pat Moy, who is the
Executive Director of ProductInnovation at Deluxe.
So, pat, thank you so much forbeing on the show today and
welcome to the show.
Speaker 3 (00:58):
Happy to be here, Greg.
Speaker 2 (00:59):
All right.
Well, let's get started byhaving you tell our audience a
little bit about yourself, maybewhere you're born, where you
grew up, where you went toschool, just a few things like
that.
Speaker 3 (01:07):
Yeah, sure.
So I grew up in Fairfield,connecticut, so about an hour
outside New York City, went toschool at Loyola University of
Chicago and studied businessadministration there and then
bounced around a little bit in afew different industries
administration there and thenbounced around a little bit in a
few different industries.
Spent a lot of time in HRsoftware, then in payments, then
(01:30):
down in Atlanta at First Data,then in Columbus, ohio, at
Nationwide Insurance and for thepast four years been at Deluxe
In all those places helpingcompanies build new products.
Speaker 2 (01:45):
Okay, okay.
Well, for those in the audiencewho may not know who Deluxe is,
if you don't mind, tell us alittle bit about Deluxe and
maybe where they fit into thepayments ecosystem.
Speaker 3 (02:06):
Yeah, absolutely.
If you haven't heard of Deluxe,I'm sure you know a lot about
the payment channel that weinvented 110 years ago the paper
check and ever since then we'vebeen really trying to enable
businesses to pay and get paidand grow, and part of that
journey has been how do we takethis core business of checks,
how do we take this corebusiness of checks, printing
checks, processing checks andexpand into adjacent areas that
(02:31):
help businesses with the otherproblems that come in, like the
order-to-cash space, where we'regoing to be spending a lot of
time talking today.
So we've been steadilyinvesting in new technology and
new platforms that help solvesome of the problems in those
spaces and enabling us to takethose really deep, trusted
(02:52):
relationships we have withcompanies and banks into the
next generation, where we'regoing to have the ability,
regardless of what paymentchannel you're using, tools that
can help businesses run theirreceivables process more
effectively.
Speaker 2 (03:11):
Well, tell us a little bit about your role
Executive Director of ProductInnovation.
What does that mean?
What do you do on a daily basis?
Speaker 3 (03:18):
Yeah, it's one of those words that, when I
introduce myself to people, thateverybody hears and can mean a
lot of different things andinnovation at Deluxe.
What it means is really solvingproblems for our customers, and
it doesn't always have to besome flashy new use of
(03:39):
technology or some crazy newproduct.
It's really spending time doingsome really deep research with
customers to understand theirneeds and really understand
their pain points and thenworking on building solutions
that address those and reallydelight users in ways that they
(03:59):
can't really even articulate andcan't imagine.
So there's a whole world ofplaces we could go and my job is
to help focus on the placeswhere Deluxe should go and do it
from a customer lens, so thatwe are not just solving problems
we think we should solve.
We're solving problems thatactually make an impact for our
(04:21):
customers.
Speaker 2 (04:23):
Okay, great.
So let's dive into the topic athand the future of accounts
receivable automation.
So maybe tell us what does thatmean, what is accounts
receivable Level?
Set on a definition, and maybetalk about the size of companies
.
You typically work with size ofbanks and we'll build on that.
Speaker 3 (04:42):
Yeah, absolutely so.
I'll start with companies thatwe typically work with.
At Deluxe.
Our core business is mid tolarge enterprises.
From a receivable standpoint,when you think about companies
getting paid heavily via check,checks are still one of the
(05:03):
predominant channels in B2Bpayments.
So a lot of our corporatecustomers are in that mid to
large enterprise and we workwith banks of all sizes to offer
our receivable solutions totheir customers.
So that's sort of the landscapeof who we work with.
(05:26):
But at the end of the day, it'sreally solving problems for
those customers.
That are the AR teams at thesecompanies and what they're doing
is working through the processof issuing invoices and managing
payments that are coming in,matching payments to invoices
(05:50):
and remittance and fieldingquestions and making sure that
the business is getting paid andgetting paid on time.
And if we think about why that'sso important, it's the critical
component to cash flow thatenables businesses to have
working capital to invest backin their teams, in new products,
(06:12):
in helping customers.
And it's a really interestingspace because for us, what we
see is a lot of the technologyadvances that we've seen over
the years haven't really made itto this space.
There's still a lot of workthat's surprisingly very manual
(06:33):
a lot of spreadsheets, pdfs,emails, right.
There's a lot of things thatare in this space that cause a
lot of strain on these teams,and one of my favorite things
when we go and talk to accountsreceivable teams is they often
refer to themselves as cashdetectives.
So they are out doing detectivework to figure out this payment
(06:54):
they just received.
Who paid them, what did theypay them for?
And so that work is really whatstands in between businesses
getting paid and being able touse that cash to invest back in
their organization.
Speaker 2 (07:08):
Okay, great, so we've level set on the AR function.
I think we all kind of get that.
We understand the types ofcompanies and organizations you
work with.
Now let's talk a little bitabout the future and how AR can
be more automated and we oftenhear this phrase embedded
analytics.
So maybe let's unpack that alittle bit.
What is it?
What's the goal, maybe?
(07:28):
What are some of the challengesaround that?
Speaker 3 (07:31):
Yeah, I love how this is becoming more and more
important as companies talkabout the use of data and really
what it's in service of isdecision velocity.
It's about arming users with theright insight at the moment
(07:51):
it's needed and not making themwork for it.
So if we think about one of theways we sort of architect
decision-making, as I look athelping businesses, it's built
on sort of a three-stack pyramidof data at the bottom and data
then creates insights as amiddle layer which drive action
(08:15):
at the top layer.
So for me, embedded analyticsis all about action and the
challenge is, without good dataat the bottom layer, the
insights might not be good,which means the actions are
potentially not the rightactions or not specific enough
to actually make an impact onyour business.
(08:36):
So, as we think about whysomething like embedded
analytics would be important,it's because teams need to make
faster decisions.
We talk about enabling fasterpayments and we want
organizations to help them getpaid faster, but if you can't
use that to then make fasterdecisions and smarter decisions,
(09:00):
then it's not really thatbeneficial to get paid faster,
because you're still held backby the challenges that your data
is causing problems when it'snot actually useful for the
decisions that you're trying tomake.
Speaker 2 (09:15):
Yeah, I'm going to double click on something you
said.
You know that bottom layer ofdata.
Is that data that's like alwaysthere, it's there or are they
having to go out and get thedata, and that adds even more
complexity.
Speaker 3 (09:29):
That data exists, but it's in a bunch of different
systems.
The data from ACH and wirepayments is with the bank.
The data from lockbox paymentsis with a company like Deluxe.
The data about their openinvoices is in their ERP.
They've got data in a CRM.
(09:51):
They've got data in Salesforce.
They've got data everywhere.
And the challenge is all of thatdata is in different models, is
in using different fields usinga field that means one thing in
one data set and the same fieldusing a different label in a
(10:12):
different data set.
So it causes a lot of problemswhen you're trying to use that
data effectively because it'snot mapped right, and even
bringing it into a consolidatedExcel spreadsheet still requires
a lot of work to make sure thatit actually makes sense and
that the labels are right andthat you can actually then use
(10:35):
that data to be effective.
So one of the areas that Deluxehas really spent a lot of time
in is the data piece.
It's not the most glamorouspart of the problem spaces that
we're looking to solve, butwithout it everything else kind
of falls apart.
If you don't have really strongdata and really trustworthy
(10:59):
data, even the most advancedtools can't really deliver their
value.
Speaker 2 (11:05):
Yeah, that makes perfect sense and I guess that's
sort of why it's at the bottomof the pyramid.
Right, it's the foundation andgetting that foundation right.
So what are some of the metricsthe user metrics or operational
metrics that you know thebusinesses typically have and
they're trying to track, and howdoes that equate into defining,
(11:26):
maybe, their future needs andand how they help and to and I'm
sure you guys help themprioritize that AR roadmap?
Speaker 3 (11:34):
Yeah, so you're going to have your stock standard
ones like day sales, outstandingpayment velocity, unapplied
cash rate, percentage of automatch to invoices.
Those are all core insightsthat can be generated.
(11:54):
But again, as I was mentioningbefore, those are just insights.
You need to have the tools inplace to turn those insights
into action that your team cantake.
But I see a lot oforganizations struggle with even
some of those stock standardones because of the disconnected
data set.
So you have those pieces of thepuzzle that are really
(12:16):
important.
But what I've seen when I goout and talk to CFOs and talk to
leaders of receivables teamsand ask about what types of
insights are they using andshare ideas for new insights
that they could be generating,there's a whole wish list of
(12:39):
things that are so far fromtheir reality because even some
of the basic ones they'restruggling to really get their
arms around.
But once you have better datasets and cleaner data, you can
get better insights right andyou can start to see things like
what payment exception typesare driving the most manual work
(13:03):
right, what's the collectionsworkload by collector and what's
the forecast of your cashposition versus your actual cash
position.
So these are things that teamsreally want to be able to have
to make those types of decisionsand drive action.
But without the full set ofdata being organized in a way
(13:28):
that can generate those types ofinsights, you're sort of left
struggling to figure out that onyour own, that on your own, and
that's why, when it comes tohow to prioritize, where to go
from here, it's really aboutlooking where you have
(13:49):
bottlenecks today to start.
It's about prioritization basedon where your needs are that
you can start to chip away at,because it's not something that
you can implement, a system, andthen all of a sudden all of
your problems are solved.
You have to look at the spaceswhere you need help and look at
the foundational spaces to beable to build from a core and
(14:15):
have those more advanced thingscome later a core and have those
more advanced things come later.
If you're asking to start withan analysis of your cash
position but you don't reallyhave a good way of assessing
your day sales outstanding, youranalysis on your cash position
isn't going to be as goodbecause your foundational data
(14:36):
is still missing some pieces.
So that's where I would saystart where there's bottlenecks.
Start where there's thefoundational layer of the core
elements to help you make thosedecisions and drive those
actions.
Once you have those, then youcan see what the next set of
(14:56):
actions that need to be betterinformed are.
What are the next things thatyou can start to prioritize from
there?
So, in a world where I, as aproduct leader, am constantly
thinking about prioritization,it's about taking things one
step at a time and making surethat you're not over committing
(15:19):
and setting yourself up for amassive project that isn't going
to yield results for years.
You want something that's goingto yield results in weeks and
months.
Right, and those are the things.
How do you take thoseright-sized approaches?
So those are just somequestions to ask yourself as
(15:39):
you're trying to prioritize.
What are those right-sizedspaces for us to start?
Speaker 2 (15:45):
And Deluxe often emphasizes that this automation
for I mean really anything butcertainly for accounts
receivable, this automation isreally a journey, right.
And so how do you develop orwork with your customers to
develop that kind ofimplementation approach?
You know what does that looklike.
(16:06):
And then from a timingperspective, I mean you
mentioned obviously there can beprojects that take years and
there can be projects that takedays, but sort of what do you
lay out as typical timelineswhen you talk to customers and
what's the implementation looklike?
Speaker 3 (16:21):
Yeah, the reason why we say that it's a journey.
Everybody wants the destinationright, everybody wants the end
of the road and the straightline forward, but that's the
hardest thing to get to if youdon't take the time to deal with
(16:43):
some of the mess and chunk outthe work in a way that allows
you to take steps in thatdirection.
And also in this space, there'sa lot of risk in change.
So, if you think about where westarted this conversation,
receivables is the lifeblood ofthe organization.
Right, and they have thereceivable teams have processes
(17:06):
in place.
They have mechanisms to helpthem turn a payment into applied
cash.
So we are injecting into anexisting ecosystem of work.
And coming in and saying we'regoing to tear down everything
and start over and have a brandnew process in place isn't going
(17:28):
to work for any team and wedon't want it.
That's not what we want,because that is a major
disruption to the ability forthat team to be effective.
So where we look to start as tono surprises is on the data.
So identify what your datasources are and automate the
(17:55):
ingestion into a data platformthat can allow for that data to
then be used for research,reporting, insight generation
and structuring that remittancedata in line with the payment
data, being able to match it toopen invoices.
(18:16):
Those types of things allow youto then take those next steps.
And taking those next stepsthen looks like how do we get
some of the more unstructureddata sets into the mix?
If you have an email inbox thatyou have for customers who are
(18:37):
paying invoices digitally andthey send emails saying, hey, I
paid this invoice, that's notreally a structured set of data.
So don't start with some of themore complex data sets.
Start with the ones that havesome structure that's just a
little disconnected and thenmove into those unstructured
data sets that can then powerthe core platform to be more
(19:00):
effective, because now you havean additional layer into what
you're using for matching, forresearch, for reporting and then
from there, now you have all ofthe pieces of the puzzle.
You have your payments, you haveyour remittance, you have your
invoices, you have your customerinformation.
(19:21):
Now you can start to then applythat in those layers of
intelligence that the CFOs andthe leaders of these finance
teams really are craving.
But if you start with thosetrying to solve for your
intelligence layer withoutsolving for your data layer,
(19:43):
you're going to have a bigproblem when that data may or
may not be trustworthy.
You want to be able to trustthe data, so that's where we
spend the majority of our timeis making sure that we have the
highest level of data integritywith what we bring into our
(20:04):
products, our tools, so that wecan start to unlock the value of
automation and intelligencewith that data set, assuming a
client has kind of mastered thefoundational and has the data,
maybe they're looking atactivating some of these more
advanced things or evenintegrating other technology,
(20:25):
but, as everyone knows, they'renot the ones doing the
development work right?
Speaker 2 (20:29):
There's no development team in AR
automation that I know of, sohow do they do that?
How do your clients kind oftake advantage of those things
without overloading their IT anddev teams?
Speaker 3 (20:41):
Yeah, this is one of my favorite parts of where we've
been focused is let us handlethe complexity for you.
That's part of the mantra ofour team, because a lot of what
(21:02):
we've seen over the years and,candidly, what Deluxe had done
before our pivot to a paymentsand data company is that if you
want to bring this data into oursystem, you have to follow this
format, which means developmentwork on your end to build a
file that fits that format thatthen can be imported into our
(21:23):
system.
We've made fundamental changesto how we think about that, and
a big part of our platform isrooted in just give us the data
as it exists today.
You have data you can exportfrom a system.
Don't worry about formatting it.
We will take care of that.
(21:44):
Let us handle that complexityfor you so you don't have to
spend those resources to enablea product to work.
We want to take that on becausethat's really where some of the
biggest hurdles fororganizations to adopting these
tools lie is in gettingresources, and it's why, as I
(22:07):
mentioned before, the accountsreceivables teams haven't really
fell to the advancements intechnology that other areas in
the organization have is becausethey don't get as much time and
attention from internalresources, technology resources
as other parts of the businessthat are more revenue generating
(22:29):
.
And I think that that's reallyone of the big opportunities for
leaders in this space to reallythink about is how do you
reframe your receivables teamfrom a service provider to the
organization to a strategicadvisor to the organization, and
(22:52):
that then allows you to havemore a seat at the table for
resources and things like that.
So we want to take on thatcomplexity and enable you to
have those types ofconversations that really pivot.
You know where you'repositioned in the organization
(23:13):
and do it in a way that youdon't have to.
You know spin up a technologyproject and you know beg, borrow
, steal for resources to be ableto do it.
Speaker 2 (23:25):
Sure, makes a lot of sense.
So what do you see as, beyondobviously trying to get more IT
resources or developmentresources, what do you see as
other internal barriers thatkeeps AR from really scaling
their automation?
Speaker 3 (23:45):
I think there's a couple things I'll point out,
one being just not knowing whereto start.
It's a big hairy mess when yougo through and try to unravel
all of the different pieces ofthe puzzle here, especially if
you think about a mid-sizedbusiness who has gone through an
(24:09):
acquisition of another business.
Now you're dealing with twoERPs, or maybe even more, and
two banks and other paymentchannels, and now you're trying
to even that consolidationeffort is now multiplied beyond
just trying to handle it for oneorganization, and so you run
into this scenario where youdon't even know where to start
(24:33):
and everything feels like amountain to climb, and so I
think that that tends to be oneof the barriers there, and a lot
of that can be rooted in somemore legacy mindsets.
If I think about I'll use atechnology and product analogy
here where traditional productdevelopment was more of a
(24:59):
waterfall approach, where youbuild out all the requirements
and then everything's defined,and then you give it to a
technology team to build, andthen they build the whole thing
and then it's ready for acustomer Versus an agile
approach, where you define whatis the way you can deliver value
(25:19):
and solve a customer need, butnot everything, and build
towards everything over time,incrementally releasing value.
I think a lot of parts ofdifferent businesses still kind
of operate in a more waterfallmindset where, okay, well, if
we're going to do this, it hasto be everything, and I would
(25:39):
challenge teams to really changethat right.
Change that mindset so you'renot thinking that it has to be
this massive project in orderfor us to get funding, in order
for us to get time of day fromdifferent teams.
Don't think about making thesebig swings.
Think about the incrementalgains you can have by just
(26:03):
changing some pieces of thepuzzle.
Just take your lockbox and ACHand wire data and just have it
all in one place.
That doesn't seem like a lot,but I'll tell you that's not
easy.
For companies to just then haveone portal for being able to
research and have access to allthe data in one place can start
(26:27):
to then change some of the otherbehaviors.
And now all of a sudden you'relike, okay, now we can do this,
now we can do this.
So back to your question onprioritization.
When you prioritize a bigtransformation and then try and
do it in one big swing, it'sgoing to take forever and it's
not going to yield the resultsyou want it to.
So transform in the mostimportant areas first and take
(26:53):
those smaller steps.
Think in a more agile wayinstead of a waterfall way, to
get you to the outcomes you wantto achieve.
Speaker 2 (27:02):
All right.
Well, it's been a greatdiscussion so far.
I mean, you've shared a ton ofincredibly valuable insights and
wisdom, obviously with yourexperience.
But if you could try to boilall of that down to maybe one
piece of advice for the leadersin this AR space, what would
that be?
Speaker 3 (27:20):
Yeah.
So I think my advice would bedon't fall for the flashiness of
AI, automation, all of thesenew tools that are out there for
teams to be able to takeadvantage of.
(27:42):
Make sure you understand thedata that's underpinning it,
because it's easy to fall intothat trap.
And one of the things you askedme about what my role is and
what innovation means.
At Deluxe, one of the thingsthat we see a lot of is
(28:04):
innovation theater in themarketplace, where it's using
flashy words, buzzwords, ai, allof these things that generate a
lot of buzz in the market, butvalue-wise, I think there's
still a lot to be understoodabout what the true value of
(28:27):
those tools are if they're notrun on a data platform where
data integrity is prioritynumber one.
So that would be.
My biggest piece of advice is,as you're looking to transform,
as you're looking to takeadvantage of all of the things
that exist today and newtechnologies, don't fall for
(28:51):
what seems like the flashy thingto do without truly
understanding how it works andwhat data powers it, because you
can end up in a situation whereyou think you have automation
but your team's actually doingthe work.
Their day-to-day doesn'tactually get any better, gotcha.
Speaker 2 (29:10):
Well, before we go, I'm just going to open the floor
and see if there's anythingelse you'd like to add to the
conversation.
Anything maybe you feel like wemissed or you wanted to double
click on, so just going to openthe floor for that.
Speaker 3 (29:20):
Yeah, no, I appreciate the time.
I love this conversation and Ihope for folks that know the
Deluxe name and know us as thecheck company, you can start to
see that we've come quite a longway from the core product that
we've had for the past 110 years.
(29:43):
It's really been a veryspecific focus of ours.
It's allowed us to enter intothese new conversations, but our
core goal is still the sameHelping businesses get paid
faster and be smarter about thedecisions they make and remove
(30:08):
friction from the processes thatthey're operating on a
day-to-day basis.
So it's been an awesome journeyto be a part of over the past
four years here and excited toreally see the next things that
we're bringing to market.
Speaker 2 (30:27):
All right.
Well, pat, thank you so muchfor being on the show today.
I know your time's veryvaluable.
So, Pat, thank you so much forbeing on the show today.
I know your time is veryvaluable.
So again, thank you so much forbeing on the show.
Speaker 3 (30:34):
Thanks for having me.
Speaker 2 (30:35):
And to all you listeners out there.
I thank you for your time aswell, and until the next story.
Speaker 1 (30:40):
Thank you for listening to today's episode.
If you'd like more informationon the transformative potential
of AI and automation in modernfinance, please visit
wwwdeluxecom.
Slash receivables hyphenmanagement.
Slash cash hyphen application.
Welcome to this special four-part series titled
Modern Finance, sponsored byDeluxe, where we explore the
future of financial automation.
From treasury to accountspayable and receivable, we're
diving into how AI andintelligent automation are
transforming every corner offinance.
In each episode, you'll hearfrom leaders at Deluxe who are
(00:23):
driving innovation anddelivering real-world results.
Whether you're navigatingcompliance, fighting fraud or
connecting the financial dots,this series is packed with
insights you won't want to miss.
Speaker 2 (00:37):
Hello everyone and welcome to the Leaders in
Payments podcast.
I'm your host, greg Myers, andthis episode is part of our
four-part series we're doing onmodern finance being brought to
you by Deluxe.
So today we have a very specialguest, pat Moy, who is the
Executive Director of ProductInnovation at Deluxe.
So, pat, thank you so much forbeing on the show today and
welcome to the show.
Speaker 3 (00:58):
Happy to be here, Greg.
Speaker 2 (00:59):
All right.
Well, let's get started byhaving you tell our audience a
little bit about yourself, maybewhere you're born, where you
grew up, where you went toschool, just a few things like
that.
Speaker 3 (01:07):
Yeah, sure.
So I grew up in Fairfield,connecticut, so about an hour
outside New York City, went toschool at Loyola University of
Chicago and studied businessadministration there and then
bounced around a little bit in afew different industries
administration there and thenbounced around a little bit in a
few different industries.
Spent a lot of time in HRsoftware, then in payments, then
(01:30):
down in Atlanta at First Data,then in Columbus, ohio, at
Nationwide Insurance and for thepast four years been at Deluxe
In all those places helpingcompanies build new products.
Speaker 2 (01:45):
Okay, okay.
Well, for those in the audiencewho may not know who Deluxe is,
if you don't mind, tell us alittle bit about Deluxe and
maybe where they fit into thepayments ecosystem.
Speaker 3 (02:06):
Yeah, absolutely.
If you haven't heard of Deluxe,I'm sure you know a lot about
the payment channel that weinvented 110 years ago the paper
check and ever since then we'vebeen really trying to enable
businesses to pay and get paidand grow, and part of that
journey has been how do we takethis core business of checks,
how do we take this corebusiness of checks, printing
checks, processing checks andexpand into adjacent areas that
(02:31):
help businesses with the otherproblems that come in, like the
order-to-cash space, where we'regoing to be spending a lot of
time talking today.
So we've been steadilyinvesting in new technology and
new platforms that help solvesome of the problems in those
spaces and enabling us to takethose really deep, trusted
(02:52):
relationships we have withcompanies and banks into the
next generation, where we'regoing to have the ability,
regardless of what paymentchannel you're using, tools that
can help businesses run theirreceivables process more
effectively.
Speaker 2 (03:11):
Well, tell us a little bit about your role
Executive Director of ProductInnovation.
What does that mean?
What do you do on a daily basis?
Speaker 3 (03:18):
Yeah, it's one of those words that, when I
introduce myself to people, thateverybody hears and can mean a
lot of different things andinnovation at Deluxe.
What it means is really solvingproblems for our customers, and
it doesn't always have to besome flashy new use of
(03:39):
technology or some crazy newproduct.
It's really spending time doingsome really deep research with
customers to understand theirneeds and really understand
their pain points and thenworking on building solutions
that address those and reallydelight users in ways that they
(03:59):
can't really even articulate andcan't imagine.
So there's a whole world ofplaces we could go and my job is
to help focus on the placeswhere Deluxe should go and do it
from a customer lens, so thatwe are not just solving problems
we think we should solve.
We're solving problems thatactually make an impact for our
(04:21):
customers.
Speaker 2 (04:23):
Okay, great.
So let's dive into the topic athand the future of accounts
receivable automation.
So maybe tell us what does thatmean, what is accounts
receivable Level?
Set on a definition, and maybetalk about the size of companies
.
You typically work with size ofbanks and we'll build on that.
Speaker 3 (04:42):
Yeah, absolutely so.
I'll start with companies thatwe typically work with.
At Deluxe.
Our core business is mid tolarge enterprises.
From a receivable standpoint,when you think about companies
getting paid heavily via check,checks are still one of the
(05:03):
predominant channels in B2Bpayments.
So a lot of our corporatecustomers are in that mid to
large enterprise and we workwith banks of all sizes to offer
our receivable solutions totheir customers.
So that's sort of the landscapeof who we work with.
(05:26):
But at the end of the day, it'sreally solving problems for
those customers.
That are the AR teams at thesecompanies and what they're doing
is working through the processof issuing invoices and managing
payments that are coming in,matching payments to invoices
(05:50):
and remittance and fieldingquestions and making sure that
the business is getting paid andgetting paid on time.
And if we think about why that'sso important, it's the critical
component to cash flow thatenables businesses to have
working capital to invest backin their teams, in new products,
(06:12):
in helping customers.
And it's a really interestingspace because for us, what we
see is a lot of the technologyadvances that we've seen over
the years haven't really made itto this space.
There's still a lot of workthat's surprisingly very manual
(06:33):
a lot of spreadsheets, pdfs,emails, right.
There's a lot of things thatare in this space that cause a
lot of strain on these teams,and one of my favorite things
when we go and talk to accountsreceivable teams is they often
refer to themselves as cashdetectives.
So they are out doing detectivework to figure out this payment
(06:54):
they just received.
Who paid them, what did theypay them for?
And so that work is really whatstands in between businesses
getting paid and being able touse that cash to invest back in
their organization.
Speaker 2 (07:08):
Okay, great, so we've level set on the AR function.
I think we all kind of get that.
We understand the types ofcompanies and organizations you
work with.
Now let's talk a little bitabout the future and how AR can
be more automated and we oftenhear this phrase embedded
analytics.
So maybe let's unpack that alittle bit.
What is it?
What's the goal, maybe?
(07:28):
What are some of the challengesaround that?
Speaker 3 (07:31):
Yeah, I love how this is becoming more and more
important as companies talkabout the use of data and really
what it's in service of isdecision velocity.
It's about arming users with theright insight at the moment
(07:51):
it's needed and not making themwork for it.
So if we think about one of theways we sort of architect
decision-making, as I look athelping businesses, it's built
on sort of a three-stack pyramidof data at the bottom and data
then creates insights as amiddle layer which drive action
(08:15):
at the top layer.
So for me, embedded analyticsis all about action and the
challenge is, without good dataat the bottom layer, the
insights might not be good,which means the actions are
potentially not the rightactions or not specific enough
to actually make an impact onyour business.
(08:36):
So, as we think about whysomething like embedded
analytics would be important,it's because teams need to make
faster decisions.
We talk about enabling fasterpayments and we want
organizations to help them getpaid faster, but if you can't
use that to then make fasterdecisions and smarter decisions,
(09:00):
then it's not really thatbeneficial to get paid faster,
because you're still held backby the challenges that your data
is causing problems when it'snot actually useful for the
decisions that you're trying tomake.
Speaker 2 (09:15):
Yeah, I'm going to double click on something you
said.
You know that bottom layer ofdata.
Is that data that's like alwaysthere, it's there or are they
having to go out and get thedata, and that adds even more
complexity.
Speaker 3 (09:29):
That data exists, but it's in a bunch of different
systems.
The data from ACH and wirepayments is with the bank.
The data from lockbox paymentsis with a company like Deluxe.
The data about their openinvoices is in their ERP.
They've got data in a CRM.
(09:51):
They've got data in Salesforce.
They've got data everywhere.
And the challenge is all of thatdata is in different models, is
in using different fields usinga field that means one thing in
one data set and the same fieldusing a different label in a
(10:12):
different data set.
So it causes a lot of problemswhen you're trying to use that
data effectively because it'snot mapped right, and even
bringing it into a consolidatedExcel spreadsheet still requires
a lot of work to make sure thatit actually makes sense and
that the labels are right andthat you can actually then use
(10:35):
that data to be effective.
So one of the areas that Deluxehas really spent a lot of time
in is the data piece.
It's not the most glamorouspart of the problem spaces that
we're looking to solve, butwithout it everything else kind
of falls apart.
If you don't have really strongdata and really trustworthy
(10:59):
data, even the most advancedtools can't really deliver their
value.
Speaker 2 (11:05):
Yeah, that makes perfect sense and I guess that's
sort of why it's at the bottomof the pyramid.
Right, it's the foundation andgetting that foundation right.
So what are some of the metricsthe user metrics or operational
metrics that you know thebusinesses typically have and
they're trying to track, and howdoes that equate into defining,
(11:26):
maybe, their future needs andand how they help and to and I'm
sure you guys help themprioritize that AR roadmap?
Speaker 3 (11:34):
Yeah, so you're going to have your stock standard
ones like day sales, outstandingpayment velocity, unapplied
cash rate, percentage of automatch to invoices.
Those are all core insightsthat can be generated.
(11:54):
But again, as I was mentioningbefore, those are just insights.
You need to have the tools inplace to turn those insights
into action that your team cantake.
But I see a lot oforganizations struggle with even
some of those stock standardones because of the disconnected
data set.
So you have those pieces of thepuzzle that are really
(12:16):
important.
But what I've seen when I goout and talk to CFOs and talk to
leaders of receivables teamsand ask about what types of
insights are they using andshare ideas for new insights
that they could be generating,there's a whole wish list of
(12:39):
things that are so far fromtheir reality because even some
of the basic ones they'restruggling to really get their
arms around.
But once you have better datasets and cleaner data, you can
get better insights right andyou can start to see things like
what payment exception typesare driving the most manual work
(13:03):
right, what's the collectionsworkload by collector and what's
the forecast of your cashposition versus your actual cash
position.
So these are things that teamsreally want to be able to have
to make those types of decisionsand drive action.
But without the full set ofdata being organized in a way
(13:28):
that can generate those types ofinsights, you're sort of left
struggling to figure out that onyour own, that on your own, and
that's why, when it comes tohow to prioritize, where to go
from here, it's really aboutlooking where you have
(13:49):
bottlenecks today to start.
It's about prioritization basedon where your needs are that
you can start to chip away at,because it's not something that
you can implement, a system, andthen all of a sudden all of
your problems are solved.
You have to look at the spaceswhere you need help and look at
the foundational spaces to beable to build from a core and
(14:15):
have those more advanced thingscome later a core and have those
more advanced things come later.
If you're asking to start withan analysis of your cash
position but you don't reallyhave a good way of assessing
your day sales outstanding, youranalysis on your cash position
isn't going to be as goodbecause your foundational data
(14:36):
is still missing some pieces.
So that's where I would saystart where there's bottlenecks.
Start where there's thefoundational layer of the core
elements to help you make thosedecisions and drive those
actions.
Once you have those, then youcan see what the next set of
(14:56):
actions that need to be betterinformed are.
What are the next things thatyou can start to prioritize from
there?
So, in a world where I, as aproduct leader, am constantly
thinking about prioritization,it's about taking things one
step at a time and making surethat you're not over committing
(15:19):
and setting yourself up for amassive project that isn't going
to yield results for years.
You want something that's goingto yield results in weeks and
months.
Right, and those are the things.
How do you take thoseright-sized approaches?
So those are just somequestions to ask yourself as
(15:39):
you're trying to prioritize.
What are those right-sizedspaces for us to start?
Speaker 2 (15:45):
And Deluxe often emphasizes that this automation
for I mean really anything butcertainly for accounts
receivable, this automation isreally a journey, right.
And so how do you develop orwork with your customers to
develop that kind ofimplementation approach?
You know what does that looklike.
(16:06):
And then from a timingperspective, I mean you
mentioned obviously there can beprojects that take years and
there can be projects that takedays, but sort of what do you
lay out as typical timelineswhen you talk to customers and
what's the implementation looklike?
Speaker 3 (16:21):
Yeah, the reason why we say that it's a journey.
Everybody wants the destinationright, everybody wants the end
of the road and the straightline forward, but that's the
hardest thing to get to if youdon't take the time to deal with
(16:43):
some of the mess and chunk outthe work in a way that allows
you to take steps in thatdirection.
And also in this space, there'sa lot of risk in change.
So, if you think about where westarted this conversation,
receivables is the lifeblood ofthe organization.
Right, and they have thereceivable teams have processes
(17:06):
in place.
They have mechanisms to helpthem turn a payment into applied
cash.
So we are injecting into anexisting ecosystem of work.
And coming in and saying we'regoing to tear down everything
and start over and have a brandnew process in place isn't going
(17:28):
to work for any team and wedon't want it.
That's not what we want,because that is a major
disruption to the ability forthat team to be effective.
So where we look to start as tono surprises is on the data.
So identify what your datasources are and automate the
(17:55):
ingestion into a data platformthat can allow for that data to
then be used for research,reporting, insight generation
and structuring that remittancedata in line with the payment
data, being able to match it toopen invoices.
(18:16):
Those types of things allow youto then take those next steps.
And taking those next stepsthen looks like how do we get
some of the more unstructureddata sets into the mix?
If you have an email inbox thatyou have for customers who are
(18:37):
paying invoices digitally andthey send emails saying, hey, I
paid this invoice, that's notreally a structured set of data.
So don't start with some of themore complex data sets.
Start with the ones that havesome structure that's just a
little disconnected and thenmove into those unstructured
data sets that can then powerthe core platform to be more
(19:00):
effective, because now you havean additional layer into what
you're using for matching, forresearch, for reporting and then
from there, now you have all ofthe pieces of the puzzle.
You have your payments, you haveyour remittance, you have your
invoices, you have your customerinformation.
(19:21):
Now you can start to then applythat in those layers of
intelligence that the CFOs andthe leaders of these finance
teams really are craving.
But if you start with thosetrying to solve for your
intelligence layer withoutsolving for your data layer,
(19:43):
you're going to have a bigproblem when that data may or
may not be trustworthy.
You want to be able to trustthe data, so that's where we
spend the majority of our timeis making sure that we have the
highest level of data integritywith what we bring into our
(20:04):
products, our tools, so that wecan start to unlock the value of
automation and intelligencewith that data set, assuming a
client has kind of mastered thefoundational and has the data,
maybe they're looking atactivating some of these more
advanced things or evenintegrating other technology,
(20:25):
but, as everyone knows, they'renot the ones doing the
development work right?
Speaker 2 (20:29):
There's no development team in AR
automation that I know of, sohow do they do that?
How do your clients kind oftake advantage of those things
without overloading their IT anddev teams?
Speaker 3 (20:41):
Yeah, this is one of my favorite parts of where we've
been focused is let us handlethe complexity for you.
That's part of the mantra ofour team, because a lot of what
(21:02):
we've seen over the years and,candidly, what Deluxe had done
before our pivot to a paymentsand data company is that if you
want to bring this data into oursystem, you have to follow this
format, which means developmentwork on your end to build a
file that fits that format thatthen can be imported into our
(21:23):
system.
We've made fundamental changesto how we think about that, and
a big part of our platform isrooted in just give us the data
as it exists today.
You have data you can exportfrom a system.
Don't worry about formatting it.
We will take care of that.
(21:44):
Let us handle that complexityfor you so you don't have to
spend those resources to enablea product to work.
We want to take that on becausethat's really where some of the
biggest hurdles fororganizations to adopting these
tools lie is in gettingresources, and it's why, as I
(22:07):
mentioned before, the accountsreceivables teams haven't really
fell to the advancements intechnology that other areas in
the organization have is becausethey don't get as much time and
attention from internalresources, technology resources
as other parts of the businessthat are more revenue generating
(22:29):
.
And I think that that's reallyone of the big opportunities for
leaders in this space to reallythink about is how do you
reframe your receivables teamfrom a service provider to the
organization to a strategicadvisor to the organization, and
(22:52):
that then allows you to havemore a seat at the table for
resources and things like that.
So we want to take on thatcomplexity and enable you to
have those types ofconversations that really pivot.
You know where you'repositioned in the organization
(23:13):
and do it in a way that youdon't have to.
You know spin up a technologyproject and you know beg, borrow
, steal for resources to be ableto do it.
Speaker 2 (23:25):
Sure, makes a lot of sense.
So what do you see as, beyondobviously trying to get more IT
resources or developmentresources, what do you see as
other internal barriers thatkeeps AR from really scaling
their automation?
Speaker 3 (23:45):
I think there's a couple things I'll point out,
one being just not knowing whereto start.
It's a big hairy mess when yougo through and try to unravel
all of the different pieces ofthe puzzle here, especially if
you think about a mid-sizedbusiness who has gone through an
(24:09):
acquisition of another business.
Now you're dealing with twoERPs, or maybe even more, and
two banks and other paymentchannels, and now you're trying
to even that consolidationeffort is now multiplied beyond
just trying to handle it for oneorganization, and so you run
into this scenario where youdon't even know where to start
(24:33):
and everything feels like amountain to climb, and so I
think that that tends to be oneof the barriers there, and a lot
of that can be rooted in somemore legacy mindsets.
If I think about I'll use atechnology and product analogy
here where traditional productdevelopment was more of a
(24:59):
waterfall approach, where youbuild out all the requirements
and then everything's defined,and then you give it to a
technology team to build, andthen they build the whole thing
and then it's ready for acustomer Versus an agile
approach, where you define whatis the way you can deliver value
(25:19):
and solve a customer need, butnot everything, and build
towards everything over time,incrementally releasing value.
I think a lot of parts ofdifferent businesses still kind
of operate in a more waterfallmindset where, okay, well, if
we're going to do this, it hasto be everything, and I would
(25:39):
challenge teams to really changethat right.
Change that mindset so you'renot thinking that it has to be
this massive project in orderfor us to get funding, in order
for us to get time of day fromdifferent teams.
Don't think about making thesebig swings.
Think about the incrementalgains you can have by just
(26:03):
changing some pieces of thepuzzle.
Just take your lockbox and ACHand wire data and just have it
all in one place.
That doesn't seem like a lot,but I'll tell you that's not
easy.
For companies to just then haveone portal for being able to
research and have access to allthe data in one place can start
(26:27):
to then change some of the otherbehaviors.
And now all of a sudden you'relike, okay, now we can do this,
now we can do this.
So back to your question onprioritization.
When you prioritize a bigtransformation and then try and
do it in one big swing, it'sgoing to take forever and it's
not going to yield the resultsyou want it to.
So transform in the mostimportant areas first and take
(26:53):
those smaller steps.
Think in a more agile wayinstead of a waterfall way, to
get you to the outcomes you wantto achieve.
Speaker 2 (27:02):
All right.
Well, it's been a greatdiscussion so far.
I mean, you've shared a ton ofincredibly valuable insights and
wisdom, obviously with yourexperience.
But if you could try to boilall of that down to maybe one
piece of advice for the leadersin this AR space, what would
that be?
Speaker 3 (27:20):
Yeah.
So I think my advice would bedon't fall for the flashiness of
AI, automation, all of thesenew tools that are out there for
teams to be able to takeadvantage of.
(27:42):
Make sure you understand thedata that's underpinning it,
because it's easy to fall intothat trap.
And one of the things you askedme about what my role is and
what innovation means.
At Deluxe, one of the thingsthat we see a lot of is
(28:04):
innovation theater in themarketplace, where it's using
flashy words, buzzwords, ai, allof these things that generate a
lot of buzz in the market, butvalue-wise, I think there's
still a lot to be understoodabout what the true value of
(28:27):
those tools are if they're notrun on a data platform where
data integrity is prioritynumber one.
So that would be.
My biggest piece of advice is,as you're looking to transform,
as you're looking to takeadvantage of all of the things
that exist today and newtechnologies, don't fall for
(28:51):
what seems like the flashy thingto do without truly
understanding how it works andwhat data powers it, because you
can end up in a situation whereyou think you have automation
but your team's actually doingthe work.
Their day-to-day doesn'tactually get any better, gotcha.
Speaker 2 (29:10):
Well, before we go, I'm just going to open the floor
and see if there's anythingelse you'd like to add to the
conversation.
Anything maybe you feel like wemissed or you wanted to double
click on, so just going to openthe floor for that.
Speaker 3 (29:20):
Yeah, no, I appreciate the time.
I love this conversation and Ihope for folks that know the
Deluxe name and know us as thecheck company, you can start to
see that we've come quite a longway from the core product that
we've had for the past 110 years.
(29:43):
It's really been a veryspecific focus of ours.
It's allowed us to enter intothese new conversations, but our
core goal is still the sameHelping businesses get paid
faster and be smarter about thedecisions they make and remove
(30:08):
friction from the processes thatthey're operating on a
day-to-day basis.
So it's been an awesome journeyto be a part of over the past
four years here and excited toreally see the next things that
we're bringing to market.
Speaker 2 (30:27):
All right.
Well, pat, thank you so muchfor being on the show today.
I know your time's veryvaluable.
So, Pat, thank you so much forbeing on the show today.
I know your time is veryvaluable.
So again, thank you so much forbeing on the show.
Speaker 3 (30:34):
Thanks for having me.
Speaker 2 (30:35):
And to all you listeners out there.
I thank you for your time aswell, and until the next story.
Speaker 1 (30:40):
Thank you for listening to today's episode.
If you'd like more informationon the transformative potential
of AI and automation in modernfinance, please visit
wwwdeluxecom.
Slash receivables hyphenmanagement.
Slash cash hyphen application.
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