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Courtesy of FIS's Keith Cowart & Crowley Maritime Corporation's Lisa Whitehead, below is a transcript of the webinar session on 'Solving Cash Flow with AI' to Build a Thriving Enterprise.
Solving Cash Flow with AI
The pandemic has prompted companies to refocus their efforts on improving cash flow and reducing collections risk. Short-term survival and long-term viability are challenges many companies did not see coming.
Hosted by FIS in partnership with Business Transformation & Operational Excellence (BTOES), the industry leading organization for continuous improvement, this webinar will explore how you can harness artificial intelligence (AI) and process automation to achieve these goals.
During the webinar, we will take a detailed look at how AI and machine learning can help you predict future risk, automatically prioritize accounts and support the transition to a zero-touch environment.
We will also explore the value of timely management information when it comes to making operational adjustments – particularly when your workforce is operating remotely.
Following the webinar, you will learn how to:
So with no further ado, I'd like to introduce you to our speakers today.
These are Lisa Whitehead who is the Director. Credit and Collections for Crowley Maritime and Keith Coutts who's the Senior Product Manager. Receivables, corporate Liquidity and Fires.
And with that, ladies and gentlemen, I would like to hand you over to Keith and Lisa and wish you all the best for a fantastic webinar.
Wonderful. Thanks Brian. I just wanted to double-check everybody. Brian, can everybody see my screen now?
They can indeed.
Well, good afternoon, everybody. Good evening, or good morning, wherever you're joining from today. I didn't type in the box.
But I'm actually in just outside of Atlanta Georgia myself, which was where one of the first people that Brian mentioned it was from. Just wanted to say a sincere thank you for joining us for this webinar. Again, it's around Solving Cash Flow with AI.
And as stated before, if you have any questions, you can go ahead and type them into the question and answer box, and we'll get to those at the end. And if there's any questions we don't get to, because we run out of time, we'll make sure that we follow up with you individually, as well.
So, with that, we'll take a look, quick look at the agenda. So we'll give a more of a proper introduction of who I am, and who lease is.
And then we'll start talking about some of the challenges faced by corporations.
This is something that Phi S takes a big interest in every year, we do a market survey, and I'll get into some of that in just a little bit. We also have just being able to delivering artificial intelligence in credit to cash. So, typically, when I talk about this topic, usually combine the practical application side, are kind of the day, today life of artificial intelligence, with the cash flow side, the impact that it has. Today, I'm going to try to do it a little bit different.
I'm gonna split those two apart, So we'll look at the practical application side first, and then we'll look at the impacts that it has on cash flow.
Then Lisa has been very gracious to join me today to give you some of her experience at Crowley Maritime how they're solving cash flow.
Lisa is always a wealth of knowledge so I'm really appreciative of having her onboard and then again, we'll wrap up with with Q and A at the end.
So by way of introductions, as I said, my name is Keith coward. I am a Senior Product Manager on for the receivables business with Phi S. Before joining us, though, I was a practitioner in the field running credit collection organizations and a shared service environment, and actually, a former customer of ... get paid products.
Um, hmm, excuse me, and before that, I formerly ran accounts payable organizations, other finance, and accounting leadership roles as well.
You may ask yourself, What does accounts payable have to do with this? We're talking about accounts receivable credit collections.
But really, it's my experience in running the AP Department of Understanding why invoices don't get paid, what kind of internal struggles to companies face and paying invoices that really help to drive improvements on the HR side.
And the AR and AP, where they meet is really what we're talking about in terms of cash flow.
So, it's certainly something that's helped me in my career, and hopefully I can impart some of that knowledge on to you.
And Lisa, you want to take a moment to introduce yourself?
Of course. So my name is Lisa Whitehead, and I'm the director of Credit and Collections Crassly Maritime.
I've been in credit and collections my whole entire life.
So, I started out, you know, in retail credit, when I was like 16, so I've worked with FIF, and since they took over Get paid, I used to get paid when Zen Garden and it.
So I remember the black and white screen, the way back in the day.
So I've been a user of Get paid for a very long time, but I started at crassly in 2013. I'm located in Jacksonville, Florida.
So, they hired me because of my user experience will get paid and to implement and strategize, So they could have a, you know, an extensive credit collection platform and get rid of some of their manual processes and optimize their cash flow, and working capital through making sure the cash flow and faster.
So, this is the first time I've actually been behind the scenes on get paid, and doing configuration and, and really knowing what the system does.
So, I think what enhanced my career at crassly and made us very successful is no, actually understanding all the technology that the tools come when actually using them and implementing them into your process. And so we'll talk about some of that later on today.
Excellent. Thank you, Lisa.
So, real quick, for those of you that don't know who Phi S is, we are actually one of the world's largest fintech providers to financial institutions, as well as as corporations, all the way from from small business, through enterprise size corporations.
I actually would the Acquisition of World Pay, we also became the world's largest or the goal, the largest global merchant acquire, as well.
And, really, the entire ecosystem of the financial systems and solutions that the ... provides supports over 20,000 clients for things like receivables, payables, Treasury insurance, and much more.
And the get paid solution itself is a major part of the success of ..., capital markets, and business to business division, with considerable investment that's driving the future of receivables.
So, as I said in the agenda, we'll start with looking at a few statistics, Everybody likes statistics, right?
So, every year, SIS does a market study to understand what's happening. What are the trends in the industry? What are the focus areas the companies have? What are the pain points, and what are the areas that we can help them solve those, those issues that they're facing?
So, with that, we'll jump into a few of the different statistics around it.
I promise we won't spend too much time on it, but it just kind of sets the stage of what companies are looking at today, what challenges they're facing, and then it'll lead right into our discussion later on.
So first, we always ask you, What are the top challenges that credit and collection or accounts receivable organizations are facing?
And the most common ones are, the top three that you can see here on your screen is 50% said that collections are in the collections process, that they're facing increasing DSO or day sales outstanding, and pass. Do AR, or accounts receivable 'em.
Saying the actual words for these, because I got feedback before that sometimes, because I've been in the industry for so long, I am, take for granted the fact that not everybody uses acronyms the same way.
So so yes, DSOs stands for de sales outstanding. And an AR stands for accounts receivable, and how we're using them today. And then another 49% save with Collections Management. one of their toughest challenges is around prioritization of accounts of focusing on the right customers at the right time.
And then another 49% say that they don't have visibility into the risk associated with the portfolio.
So, in that credit review process of understanding how much risk there is in doing business with certain customers, these challenges are all kind of tied together. And they actually compound each other by the fact that, if you don't have visibility into the risk in your portfolio, you're likely going to do business with more risky customers.
And then, if you're doing business with more risky customers, it becomes more and more difficult to prioritize the accounts, because everyone becomes a high priority.
And then, of course, if everyone's a high priority and you're not able to get through all of your accounts, then that's what's driving the increase in DSO and passed to AR.
So we're going to look at how artificial intelligence can help solve all these challenges the companies are facing.
So dabbing a little bit deeper and I promise this is my last slide of statistics from our survey. We asked, what areas are you investing in over the next 12 months to help you with those challenges?
So 95% said process automation, followed by 92% for API connections. And for those that don't know what APIs are, that's the layman's term is that it's a real-time connection between systems of transfer of data. And then 87% said AI and Machine learning.
And granted, that, this webinar today is focusing on artificial intelligence. And it's the number three priority that's listed here.
But really, I would argue that AI or machine Learning really provides biggest opportunity for cash flow improvement, because it leverages both API connections and process automation to help with these improvements. So, I would position this as, you know, if you're looking at the investments that you're making, to improve your cashflow, Why choose one?
Why not choose all of them?
So, again, we're going to talk about that.
So, if we look at artificial intelligence across all of credit to cash. And so, we're going to dive into kind of the practical applications. So what does that mean in the day-to-day life of someone who's using a solution to support their credit to cast process?
So, we'll look at it, in kind of a chronological order, somewhat of a chronological order.
So we'll start with credit, because that's usually one of the first pieces that that's involved, and in terms of protecting the company from risk.
So, a lot of people refer to, and when I say a lot of people, it's usually sales teams refer to credit, as a sale killer or revenue blockers. Or, I'm sure there's many other names that you've heard.
The credit that sales teams have said about the credit organization, because they are trying to protect the company from risk. So, they may have to say no to a certain deal.
With artificial intelligence, however, that can lead you to become more of a sales enablement operation.
So, we actually have a client who just recently won an award from the sales team at their company, that, that had the tag on it.
They were a part of the sales promotion department, because they've been able, been able to protect the company from risk, while also looking at the opportunity for increasing sales to those lower risk customers.
So, how does AI help in the credit process?
Well, the first piece is related to pulling credit data from external credit bureaus.
So being able to automatically pull that information, put it into a scorecard to provide that review of the risk assessment, as well as looking at the internal payment history you may have with a customer already.
So that's actually providing a very, an accurate view, and actually more of a predictive view of the risk where the customer is using your own internal payment history with them.
And it's going beyond just payment history. It's looking at order history and other information, as well, to determine that risk.
And again, so it's looking at that internal information you have. It can be married up with the external information, you may be pulling, so that it can determine the risk with each of the customers.
And then it can also alert you to anything that's changing in the risk status for those customers. So, they may be a low risk customer, but there's some event that's happened.
Or there's a trend of events of where maybe they're slowing down payments, and it's potential risk factor that that's growing into the business that you're doing with them.
So, it can alert you to those changes in the risk. And then it can also provide the automated approvals around credit lines.
So, basically, based on whatever your risk tolerance is, You know, with each individual company, it can help with, um, providing a balance of the workload, in terms of reviewing all of those credit lines. That's goes to, you know, the initial assessment of credit risk with customers, as well as the ongoing assessment.
Even for customers that you've been doing business with for a long time.
So it can automate the approval process if they meet certain criteria. It's under a certain value, those can be automatically approved and set certain limits, as well as being able to route those for approval for the things that go outside of those thresholds.
So if we switch gears a little bit and look at the collection side of things. So how does AI apply in collections? So there's really three main focus areas that we'll look at today. There's many different pieces of it. But we'll focus on these three areas. one is identification. So it's looking at multiple data points, and when I say multiple, that's really not the magnitude of it.
It's looking at a lot of information to be able to determine a customer's propensity to pay.
So that's very different from your credit risk, which is the customer's ability to pay.
This is looking at the customer's willingness to pay.
So that's two different risk profiles.
So you have your credit and your collections risk.
The second one that we'll look at is prioritization. So it's organizing the accounts to make sure that you're contacting the higher risk accounts more often with more of the personal touch, and so it's prioritizing the accounts and allowing automation to help with the lower risk accounts. And again, we'll get into more detail about how this actually works in just a second.
And then three is coverage.
So if I could see everybody, if I were to ask the question, Your raise your hand, if your teams are able to contact everyone in their portfolio each month. Not many of you would be raising your hand.
So, with artificial intelligence and being able to identify the risk and separate the accounts out into different risk classifications, the system can help you with automatically contacting the self caring and can also the low risk accounts while focusing your teams on the high risk accounts. And what that does is driving more business towards the lower risk accounts, more automation. It allows your teams to actually get through their entire portfolio, usually more than once in a month.
So I'm gonna take you through a little exercise. If any of you seen me present this before. No spoiler alerts. So I'm gonna give you a sequence of numbers, so you see these four numbers on the screen right now.
With these four numbers, can you predict what the 12th number will be after this sequence?
Not likely, right?
So if I give you the next number, do you think you could predict it?
So if I give you, all the way up to the 11th number, can you predict what the next number is going to be in the sequence?
Again, not likely.
But those of you that just have to know, that number is 1027.
And, what this is showing is that artificial intelligence looks at a lot of different information.
It's pulling in, you know, and the collections world is pulling in invoice information, the payment information, any of the credit risk side of it as well. And, it's looking at trends in the information, it's developing this probability of what's coming next and predicting into the future. So, in this example, we're looking at numbers, But you can think of it as, you know, payment history that you may have with a customer to where the AI engine is predicting that. Next number.
So that's where it really applies. And that information is used to help prioritize the accounts to make sure that you're collecting from the right people at the right time.
So again, it's looking at all this information, so it can go beyond that 12 number in this example. It can continue to predict out for many, many numbers into the future. And in the collections world, it can predict out into, say, six months into the future, what potential delinquency may appear with a customer so that you can proactively prevent it.
So give you a little more of a classic example of how it prioritizes accounts again.
This is a simplified example, but if we have two customers, customer A and customer be, we take a look at customer A, they owe $50,000. And they're greater than 30 days past due.
Customer B, it was $30,000, but they're less than 10 days past due.
So, just on the surface, looking at these, of course, in a perfect world, you want your collectors to collect from both of them to make sure that they don't go any further past due.
But in terms of a prioritization standpoint, on the surface, it looks like you should probably go after customer array.
But with artificial intelligence, and looking at the different risk factors associated with the customers, it's determined that customer is actually a low risk. What customer B is a higher risk.
So what does that mean, in terms of the risk associated with these balances, and with these customers. The probability of bad and bad is an acronym for Beyond Acceptable Delinquency, which is something that is different for every company. one company may say, one day, fast do, is beyond, acceptable that we need to make sure that everything comes in a certain field on the due date.
And the other companies see that you're providing a grace period of 10, 15 days past the invoice date is something they're comfortable with.
So whatever that threshold is for you, the probability of bad for customer A is 2%, while the probability of bad for customer B is 50%.
So simple calculation tells you the cash at risk for customer A is one thousand dollars with all the cash at risk for customer B is $15,000.
Now, again, this is a very simplistic example and with artificial intelligence, it's looking at a lot of different factors to determine the actual risk associated with a customer.
But this just demonstrates how when you just look at, you know, the balance and the age of an invoice, you may be leaning towards those customers that are actually a lower risk, the ones that will pay as well. But you're, you're leaving off those customers, or at least delaying the contact with customers that are at the higher risk.
And so, really, what we're trying to do here is change that mentality of looking at the risk associated with these customers to make sure you're focusing on those higher risk customers first, which allows you to automate, and that contact with some of the lower risk customers, and allows you to get through your entire portfolio.
So this is an example of that prioritization and really, how it helps from, from coverage.
So this is just using five different classifications of accounts. It can be any number of classifications of how you want to split out the accounts.
But if you look at the level of automation associated with self carrying accounts, that's the highest level of automation.
Those are the accounts that are going to pay regardless of whether you contact them or not.
So, you still want to provide reminders of invoices coming due just to make sure that, you know, there wasn't an invoice missed somewhere, or anything else that would prevent payment. But you really don't have to have that personal touch with those self caring accounts.
And the low risk accounts are pretty much in the same category. There, of You can use automation to contact those customers, and you may require some personal contact if there's an issue as to why they're not able to pay.
As you move up into the medium risk accounts, that's where you start using a little bit less automation associated with it.
So it's it's looking at, you know, providing those initial contacts, using automation to the customers, and then a little bit more personal contact as they start to get closer to their due date. To again, make sure you're preventing them from becoming delinquent.
And then as you move into the high risk accounts, that's where you can still use automation to do those first contacts out to the customers again, making sure they have invoice copies and so on.
But that's really where you want to put most of your resources towards focusing with that, the high personal contact of making sure their own phone calls with them, they're holding meetings with them if there's any issues with invoices, and so on.
And then the final category here is the extreme risk accounts. This is where you want, your collectors have that continuous personal contact. You want to make sure that they know what's going on at all times with these customers.
Typically, you don't want many customers in this category.
However, there may be some strategic customers that you need to do business with that may be considered extreme risk, so you really want your collectors to stay on top of those.
So, the white line on this graph actually represents the kind of a bell curve, sort of associated with the number of accounts, that typically we see falling into these different categories for companies.
And that's where you've got your self care. It counts, the number of those, and your low risk accounts are typically the largest category, so that's a lot of automation, a lot of work that you can take off of your collectors by automating that contact. And then, as you get further on, you can see the number of accounts you tend to drop, and that's associated with the fact that you have visibility into the credit risk of those companies, because you're leveraging AI to take a look at the risk profile of those companies. So you're doing less and less business with them and opening up sales into those lower risk accounts.
So from disputed deduction standpoint, AI can help in a number of different ways. one being just identifying the deductions, so automatically identifying those deductions or disputes from short payments or even e-mails from customers.
So being able to read and interpret the information that customers are sending e-mails through, natural language processing, the AI engine can pull out that information and use that to go ahead and automatically create and identify the dispute.
And then it flows into smarter workflow. So being able to route those disputes and deductions to the appropriate people for resolution. So for an example, you may have a pricing dispute and that goes to the sales team to resolve whatever that pricing issue is.
The system can automatically identify that it's a pricing dispute and they can route that to the appropriate person.
So it reduces the resolution cycle time associated with these disputes and deductions, and it also provides visibility to the cash at risk.
So in the example we showed on the slide a couple of slides ago, it shows visibility into what's the actual cash at risk. It's not the full value of an invoice.
It's, you know, some piece of it associated with the risk of that customer. So that allows you to provide a more accurate cash forecast, which Treasury teams are very excited about, being able to have an accurate cash forecast so they can do better working capital calculations. And it also supports bad debt reserved calculations, also, because, I know in my past, a lot of the bad debt reserves were calculated based on people's assumptions. There's thinking while we may or may not get these invoices collected.
So, being able to use the risk associated with these customers and having accurate invoice level predictions of when those payments are coming in, allows you to, to make sure that you're appropriately reserved for bad debt.
Looking at it from a cash application standpoint.
So this is one that is near and dear to my heart that when you, in the past, you've had a lot of payments coming in through, through check payments. And typically, the remittance data associated with check payments, you know, goes along with the payment itself, it goes through a lockbox.
So the bank is able to provide a file that has all of the remittance information associated with that payment, so that that file can be loaded into an ERP system, which typically would get you probably 40 to 50% straight through processing on check payments. However, with the push for companies to move to electronic payment methods because we all want money faster.
It's actually created more manual work for companies from a cash application standpoint because the remittance details are typically set separate from the payment itself.
Usually they're e-mailed over sent to a server somewhere and then the cache supplier has to find that remittance e-mail.
Then they have to match it up to the incoming payment and then they have to match that to the open invoices. So it's a lot of manual work in there.
With with artificial intelligence, however, we can automatically retrieve the remittance information from an e-mail box or a server location.
We can scan that to digitize all the information so it all becomes usable in the matching process by the engine.
So, this helps to speed up the application process, regardless of what the payment method is, and it helps drive the straight through processing to 90% or greater because it goes across all payment methods and uses more information from a matching standpoint, Then a typical ERP system would.
Usually ERP is can match on invoice numbers, dollar amounts, maybe a date, and that's typically it.
With using artificial intelligence, you can match on any number of data points, like reference numbers, PO, numbers, whatever the case may be to speed up that, that processing.
It also supports automatic write offs of your deductions or short payments.
This saves you time and money by automatically riding off amounts that are under your defined threshold.
So if you have a deduction of $1 on a payment, it's not worth the time and effort of having someone spend hours trying to research it and figure out what the issue is, Where the system can automatically write those off again, whatever year defined thresholds are.
And then, really, a big piece of it here with the cash application side is that the artificial intelligence engine, it's built on machine learning, so it learns from exceptions.
So it's monitoring what a user does.
In the case where a payment couldn't be plied applied automatically, it's monitoring what that user does to clear the exception. So it's looking at the information they're using, how they applied it. So, the next time a payment comes in from that customer, it can be processed straight through automatically with no one having to touch it.
Then, just another kind of side piece, because I mentioned before about the investment areas for companies of process automation, APIs and AI, And this is really how all of this can work together.
So, with APIs, you have that real time transfer of data. So, whenever anything, changes or updates in the system.
So, does the solution that's supporting your credit to cash process.
And then it provides you with better visibility to information.
So you're making decisions with all the information in real time, instead of waiting for something to update, so that you know you're making the right decision.
And then there's the ability to collaborate between different systems. So you have, say, your sales team, maybe on a different CRM system.
They typically don't want to have another system to log into to work disputes.
So, with APIs, you can transfer that data back and forth between the systems, so, they're actually working in the CRM system to resolve the dispute.
And then, that information is transferred back to the Collection's team in real time, as the changes are made.
So, again, it allows all these teams to collaborate together.
Alright, so now, we'll look at the impact of artificial intelligence on cash flow. So, we looked at the practical application side, what does that look like on everyday life of a credit and collections organization.
Now, let's focus on what that actually means, from a cashflow standpoint.
So if we look at just the reduction of risk in your portfolio. So really focusing on that credit side.
So you're able to identify any unnecessary risks you may have with companies you're already doing business with today.
And so that helps you to reduce the number and value of those delinquent accounts that you have.
And, again, as I mentioned before, it helps you to increase revenue opportunities. So you can identify those lower risk customers, especially the ones that may have underutilized credit lines. So that you can help drive sales to those customers.
So that they can can grow revenue with those customers instead of focusing on those higher risk customers that they may be looking at today.
And then, when you do have those strategic, high risk customers, it really helps you to set up proper payment protection plans, so that you're mitigating the risk associated with those accounts.
And so, that helps you to focus on driving cash flow from your credit side.
So, from our aging compression standpoint, So being able to leverage AI in the collections process, it helps you to automatically prioritize the accounts again so that you can get through your entire portfolio.
And you can make sure that your collectors are spending their time focusing on those higher risk accounts, the ones that require more of that higher personal touch, and then automating the contact with those lower risk accounts.
And then, of course, if the risk profile changes on a customer, the system will automatically re prioritize the accounts.
And they will move the customer between the strategies, so that you're making sure that you're using the appropriate strategy on those accounts.
And then that's where it also helps that you're providing a consistent strategy with accounts that are in different classifications. So, for all of your lower risk accounts, all of your collectors are following that same strategy for all those customers, and that's really what helps you to train the customers to have proper payment practices, and it helps to prevent delinquency.
And then just the accurate invoice level predictions. So being able to see when payments are expected to come in on each individual invoice, which in the past it was potential to have that prediction at the customer level. But then it really didn't give you the visibility at the individual invoice of, knowing that this invoice is likely to go more delinquent because of whatever information we have on the customer.
So being able to provide that information, that helps you with better cashflow forecasting and helps you to prioritize the accounts to make sure, again, that you're collecting on the right people at the right time.
And then, again, from a total portfolio coverage standpoint, it's automating those self caring and lower risk accounts so that your collectors aren't spending as much time on those.
And then, the ability for artificial intelligence to use that natural language processing. So the incoming e-mails from customers, it can read and interpret what those customers are asking for, stating in their e-mails, and then it can provide suggested actions.
And, to give you an example, if they're looking for invoice copies, that's something that takes a lot of time from a collector, because they have to read the e-mail. They have to understand which invoices they're looking for. They have to go find those invoices, and then e-mail them back to to a customer.
The AI engine can interpret, they're asking for this list of each of invoices, so that it can automatically provide those invoice copies back to the customer.
So, again, it saves time, as well as, if they're indicating a dispute, it can go ahead and create the dispute and code it and route it to the appropriate person for resolution.
And then, also, it's like payment promises, so if they're promising to pay something next week, it can automatically set that up as a promise to pay. And so, it's looking for the payment to come in. If it doesn't come in, that becomes a broken promise, that puts it as a higher priority for the collector.
Again, it's being able to automate all this work so that the collectors can just focus their time on contacting customers.
And then it's looking at the continuous monitoring of customer risk and the automatic reassignment of accounts.
So it's looking at a customer over time. So something that we all fall into the trap of, We've got so much work going on.
We look at a customer wants to assess the risk, and then we probably don't look at them again until we know they're asking for a higher credit line, or they've become severely delinquent.
So with artificial intelligence, it's continuously looking at the risk of these customers so that it can reassign them good or bad into a higher risk category or lower risk category based on what's changing in the profile of the customer.
So, again, it's it's saving time and allowing your collectors to cover the entire portfolio.
And then, lastly, I want to touch on just the side of processing, cost, or operational expense. Now, while this isn't really a direct impact on cash flow itself, it certainly has an impact on your bottom line.
So, it's looking at the operational expense side by being able to create capacity.
So, you require fewer resources to get through your entire portfolio, to train your customers, to make sure they're paying routinely and on time, and it grows, it has scalability. So it grows with your business. So you can be a small business today utilizing the system and training your customers to make sure they're paying on time.
But then as your business grows, this system can grow with you and artificial intelligence helped to set that up.
And even if you're already an enterprise size company, a very large company, then maybe you're looking at acquisitions. You say, you're bringing on another business.
The system can, can use all the data from the other company that you may be acquiring, can scale with it to make sure that you're being as efficient as possible.
It certainly provides just a lower total cost of ownership, that, again, it's, it's fewer resources and supporting you from a system side of automating as much of the process as possible.
And then it also offers the customer self-service options. So, it helps to remove some of that administrative work for your collectors, where you can allow a customer to go into a portal, to retrieve invoice copies, to create a dispute on an invoice, or even make payment directly on invoice, as well.
And that even goes so far as, when the collector sending out an e-mail, or the system, is automatically sent an e-mail to a customer.
They can provide a click to pay link, so, that, it makes it, as easy as possible. On a customer, To actually pay their invoices, They simply click on the link, it takes them to the portal to make payment.
So, again, it's removing a lot of the administrative work and making things much easier for companies.
So, we looked before at the corporate challenges. You can see them here again on your screen.
So, by leveraging artificial intelligence, it really helps to turn these challenges into answers. So, all of these things that they mentioned before, as challenges are now solved, and it really helps to solve cash flow.
But, don't just take my word for it.
I want to have Lisa talk to you a little bit to tell you her experience and what her company has been able to do.
And, Lisa, do you want to take it away?
Let me take myself off from you.
As I said, my name is Lisa Whitehead and I'm with Crowley Maritime corporation. Crassly recruited me in April of 2013 form I get paid experience. On the user side and it's been very successful.
So we're gonna go through some slides today to kind of show you where this company went from my very manual paper process, right from the Green bar. I don't know if anybody, how many of you ever came from the old school where they had Green bar paper and you do that you're aging on it. And you to handwrite your little notes then and that's pretty much for crassly was So they really had no automation. They had no spreadsheet in Excel with some notes written on it.
Their credit process was basically, an application came in in a folder roamed around an office for an approval. So I'm going to talk about, you know, kind of where Crassly today, because of the automation that Keith men talking about, and some of our upgrades that we've done to improve our process, as well.
Are you turning that, Keith? Or do I turn that? I've got it for you.
I'm not going to stay on the side too long. Crassly always have a cultural cultural moment, so whenever we have any kind of webinars or anything that we're talking about our company, we always have kind of either Thank you, my Merkel, to remember where we have some development skills. And so one of the things that I recognized really early on with Crowley is A, OK.
What does credit contribute to fail?
And what is sales contribute to credit and how and how can we help each other? So, these are just some things that are best practices that you can look at that you may be able to incorporate in your credit collection process with sales and have a more collaborative relationship. At Crowley we really take a different approach with our customers, and we want our collection and our credit collection experience to be a happy one. So, just as much as you have customer experience in terms of customer service, you also have, you know, your credit collection customer experience. So you don't want to go to no Sale Getting This Customer and going through all this work.
You know, and then, in the end, lose that customer, because they had a Bad Credit And Collection experience.
And this is the same thing. It's the sales department ... to contribute credit, and have a Pro Credit attitude.
No, I think, a typical sales, they want, they think everybody should get credit, and I always try to elaborate on that.
and say, you know, everybody's not gonna get credit. We have to look at the customer's credit worthiness. Can they pass?
And one thing that we've used several times is I tell them, I'm like, you know, if if ..., Lisa, a million dollar house, and I'm like, Yeah, I'm gonna buy it, but I work at crassly, So, I don't, I don't make enough money to buy a million dollars.
So, how has that relative lost if they all know?
So he's told me to health, I wouldn't have been able to pay for it. So we really try to do an approach with failed and credit to try to give realistic opportunities and making sure they understand that It's not just about the fail. If the ability to be able to collect the money to ensure our customer has the credit worthy enough to be able to pay that, because you're doing a customer huge dissatisfaction if you, if you don't do your due diligence upfront.
I'm not gonna go into our mission statement, because it's pretty self explanatory, but I always share it because it's just a powerful message and a credit collection group. You know, Crowley, of course, didn't have credit collection processes. So, we had to make credit collection policy.
I wanted our group to have a mission statement. We've really taken pride to have, like, one Crowley, you know, one credit and collections department at, you know, to where we all collaboratively work together, and we've been able to do that. We're having that artificial intelligent automation, the communication between sales and credit.
We'll talk about this little on this next slide. But there's lots of things that we're doing at crassly to ensure that, that automation automation continues. ... has been a huge challenge for, I think, any company.
We're going to talk about some things that crassly did during coven with using Artificial Intelligence. That really helped us. And we were kind of surprised. I think you'll be surprised by the results as well, and how well we did during the coven. And we're still, you know, going through it. But when you see the numbers, You'll be surprised.
I'm gonna give you some numbers at the end.
one thing that I also want to talk about, we talked about the positive customer collection experience.
The communication skills, making sure that everybody's training get paid, has done several trainings for my group, and it's really been a very good training.
It helps you utilize the system efficiently, You know, Sometimes, when you're training a group of people, you know, shouldn't have their half listening when you have somebody actually come in and talk about, you know, the automation and what can they can do to make your job easier. as a collector, That's when, you know, the light bulb goes off. So, they also do training as well, and we've done the certification program. So, I, myself am a super user, through their certification program, managers that I have our super users. And, then, the collectors we put through, the collection certification, and then on the credit side, we had all the Analysts go through that certification. That really gives them detailed, more information, things. They may have known, maybe not you know, Maybe they can improve, maybe things that we needed to change in the system. I thought the certification program with Very good. I gave a very good ratings, Keith, because.
Know, there, I felt like I know everything. When I went, I was like, oh, I'm just gonna briefly this and know everything. But I mean, they actually taught me things that I didn't know. So we learned a lot. We Lawrence and best practices and get paid that we didn't know and things that have really helped us get to get to that next level, as well, and we still continue soaring bands. And, I mean, even my management team. Like, we don't know how you do at least it, but you do it. And I do it because I have this automation and get paid. So that there's times where I feel like I need to, like carry a presentation with me for our own people. Because sometimes, you know, credit collection. We are in that little back corner. you know, we don't get, you know, the recognition. Maybe sometimes though, that we are trying to collect in the bag. So there's sometimes people don't see what you are doing with less people. The cost saving the efficiencies that you have?
The metrics, I mean, in the cash flow. And so, this is where we've had to do several presentations and house as well.
So, we can communicate to our team, and they, look, look, look what we've done and make sure that you communicate what credit collections to tend to fail. Because if you don't speak up, they just think customers pay all by themselves.
I think it's an invoice, customers kind of pain and we all know that doesn't happen. but you know there's there's always that misconception that, you know, the AOR collect itself, and I tell them OK Next.
So, Tara ... credit limit approval profit Keith talked a little bit about the credit score the automation. That's huge. Crowley, you know, with their manual processes were running manual DNB. They were having paper credit applications. We now have a process through get paid through their automation where we have an automated credit application.
Customer goes online, built up a credit application, they're actually filling out the customer information and get paid.
So I don't really have to do anything. When I get the credit application, I push a button, I can send a reference.
You know if I want a reference?
The D&B is already imported so we get Dun and Bradstreet imported into our system every week. And so we always have scorecard so every customer regardless of whether they have credit or not, has a scorecard, so that way if we had we need to do a preliminary review on a customer. We have it already on hand.
So, if the customer has ever been a customer Crassly, whether they're a company, or a shipper, because we're in transportation and logistics, they have every customer has a score, which puts them into a credit risk class, and that, also, that carried it through the collection process, as well. one thing that we like about the automated credit applications is our customers don't like filling out PDFs.
They, they like going online and filling it out, and then get an approval back, because we're doing same day approval.
So, if it's US. Domestic, you know, where it, you asked Puerto Rico or our Virgin Islands, I mean, we can profit that credit application within five minutes of getting it. We have all the information we need, and customers like that. They get their approval process back.
They didn't like filling out PDFs and no telling where that PDF when, or where the credit application went.
And it also helped us with customers not changing our terms and conditions.
So when you're going online with a credit application, through, get paid, they can't change our terms and conditions. They can only fill out the box they're allowed to fill out. So we were having problems with people, like acting out things on our credit application. And that was a huge problem, And it got us into legal issues. If somebody didn't notice somebody cross something out, or they change the terms on the terms, and because you know it's written a real small. So it also incorporates financials.
We also use our matrix if it's an existing customer, have a lot of crassly failed. So we put our sales how long they've been a customer with Crassly, and then we also know how they pay Crowley.
So 40% of our matrix is based on our own internal experience and then we also use Dun and Bradstreet information for the US Domestic Customers Foreign. We use strictly our own information that we have well occasionally run a D&B but it's very limited on international customers.
We do have a platform and locations in Central America. So we do a lot of international trade and export import in Central America.
We also handle a lot of the islands. You know, so the DR, El Salvador, Honduras, all Central America Plus all other islands that were we're dealing with is international. So we handle those a little bit different, you know, because it's international trade.
But we also use agents as well that collect on behalf of proudly that are also. They have a portfolio and get paid so we allow our agents.
As customers come in we assign those to our agents and that way they're able to go in to get paid and see the agents on what they need to collect.
I'm in the countries that we don't have an office.
one thing that's also important about this piece of it on your credit risk management piece, is like Keith said, it does recognize and give you scores, and, and, you can, you can actually do your reviews based on that. So, we have one of our reviews we do with outside collections, like, so, if the customers are radial credit hold, and they're 100% path, do what's the collector doing with it. So, we're able to put in specific rules and to ensure that, we're going to end up getting the information that we need on the end for a review. So, we also use the Advanced Search method to The Advanced Search basically lets you take all the data and get paid, and you can really run some robust reports.
We've used it to automate a Dashboard, you know, for the company, and it's all automated. So, at any point in time, anybody can go on and look and see, how much does the company have an AR? How much is past due? How many customers do we have? I mean, there's all kinds of things that we have old friend, get paid, get paid on, an automated process, to make sure that we have that visibility to operation fail, you know, where our ERP is, Oracle and, one of the things that ... was challenged with was, we have many different point of sale system.
that, we didn't have one place to keep all the customers.
And, so, what we did get paid, we were able to take all of that.
We have five different in this particular instance that we're working on.
We have five different instances of different systems that are pushing information into get paid, and we're able to look at the customer on a global purpose perspective in terms of how much they owe us across the business.
So, you don't have, we can, we can track credit limit. So, you're gonna see a number later that I'm really proud of in terms of, you know, how do you keep up with a customer's credit limit? None of our point of sale system to actually put a customer on credit, hold you, unless you do it manually in that system. So, we had to find ways where we could manage 5 or 6 different systems in one place, and manage that credit line.
Because you can't have a credit line of $10000 in five different systems, or you'd have a $50,000 exposure. So, now, we have a $10000 limit. We have all these system spending in there, you know, we can address those, and then immediately go and find out what we need to do. We need to increase the credit limit. Do we need to put them on credit?
Hold, are they, PHD, So, those are all things that this artificial intelligence and this automation do, where the collector come then, and I know exactly what they need. The credit people come in, and they have credit applications. They know what they do, they need to do, they have the credit reviews. They know what to do. A lot of this stuff is automated, all of our Dunning letters that path. Do you notice is all of those are automated. So we don't have to send out any.
Go to the next slide, Keith, and I'll talk about some more stuff that we're going to get.
So, we're going to talk about the collection steps about, you know, prior to credit hope because we're in, know, logistics and transportation. So we deal with bill of lading. So if I know how, what do we have to do? So part of our strategic plan, and our collection, and get paid.
And if we finally get to a point where the customer's not responding to a customer's not paying us, then we have a strategy that says, go to the salesperson and you go to the salesperson, Hey, can you help me out? So, there's never, there's never a time where nobody's informed. So, you have documentation of that, because everything going in and out of the system, and it's documented, and, and we'll talk about that in just a minute. I have the e-mail exchange that out, that we'll, talk about. Which is, which, is really where crassly solve a turnaround. We upgraded in 20 18 to 87, and that's when we got the online credit application, we got the e-mail exchange, and we saw a huge efficiencies with an e-mail exchange, before we were cutting and pasting e-mails under the fifth them now, all of our e-mails go in and out of the system. So the collector send the e-mail, right? from the customer's account. We have already pre form templates. They can modify them, if they want, they can add, If they want, and they send it to the customer. When the customer response, It comes back into the customer name.
So, you're building a process around a collection process, versus trying to build it around a person. What happens if that person goes away, are you going to lose that e-mail, you're gonna have to get an e-mail and try to paste those. And so, that proved a lot of efficiencies for us, and that really want to where we saw, because we were so freed up from not doing the manual processes. In the AWS was a huge thing.
Bad, know, you e-mail out of the system backend, and you don't ever lose any of your notes. And that's very important on a legal point, because any of you that I've dealt with an attorney and taken an account to an attorney, they want all your communication to that customer. And so, we've had legal cases, and we have one, because of our documentation, and all of our efficiencies, and having all of our approvals and everything in one place.
Um, it's been there encode. one of the reasons why they asked me to talk today, is, I was talking to one of the girls that get pain, and I was telling her how successful we were Gearing coven.
Do you want to go to the next slide, Keith?
And so, we work together with fail.
And so, these are just some things that we then, but what I want to share with you from the covert experience was in March, we were probably 50%. Maybe almost 60% passed a number that I had never seen in my lifetime and collection. And.
and when you work so hard, you know, Crowley, with name 2019, best in class Collections because of our automation and efficiencies and the result that we have that you'll see later, on the next slide, you know, we were very proud of that award. So, here we go, thinking, yeah, we're fast and collections. And then I want to thank overhead and are our number, so are more alike.
What do we do?
Well, we immediately implemented, and we developed and implemented an Action Plan through Get Pane, and we put in Strategy.
Stricter guidelines, we on the scorecards reroute. We tightened up the scorecards, you know, to look at things that were more high risk, like notifying us of bankruptcies, notifying customers, you know, past do more than what they normally would be.
So we had a huge, very regimented, credit whole process that we implemented would get paid. And if a customer, odious, any kind of money, we put them on a credit hold. And basically, until we communicated them and determine, you know, what customers are out of, you know, out of business permanently, what customers couldn't pay. Because they were. They were not operable.
Know, what customers were essential customers. You know, Crassly with an official, an essential customer.
So, we were able to code those customers and be able to provide metrics on a daily basis to our senior management right out and get paid. We were telling them a cash projection in terms of the promises to pay and how much money was coming in that day and was forecasted. We were able to give them all the customers that weren't paying us.
Because we're able to code them and get paid as like. Yeah, we, we, we have a dispute management process, as well. So we're able to code that as a coven. And so anybody that had like a pivot. We had like little code. So this is how many accounts are out of business. These are accounts that are bankrupt. These are accounts that are not operable right now. And we had a strategy.
So I'm very pleased to tell you that by the end of the year, in December, our numbers for collections in terms of our delinquency percentage, we're right at what we were in 2019 when we won best in class election.
So, we went from all the way, almost to 60, down to 30.
By December, Are, bad debt has been very little, but I think that credit risk upfront is very important and making sure that you have a proactive approach about your credit risk, opposed to just fill in everything and putting it on the books and hoping that collectors kinda collected.
It's like, why don't you know for a fact your, your collectors kinda collected. I mean, we, since we've been in on get paid, we've had very little bad debt bankruptcies that we have. We've been named as critical vendor in most cases and collect and collected our money. We had to have had a few hit, but we've been very successful.
And part of the success has been very good for my career. I came in here is just a Manager Credit for Crowley and kinda set up the credit process. And and two years, they promoted me and gave me all of collections. And recently I got promoted, and to where they're given me the whole entire company and all all the Business Unit Crowley. And so it's been a very successful career for me. And I would say that I have that because I get paid. There's no way I can.
I can deal with thousands and thousands of hundreds of thousands of transactions and customers and, and be able to collect that without having and holding that credit collection group accountable. So, I know at all point in time, what my analysts in credit. I know how many approvals they're doing. I know how many reviews they need. I know how many calls my collectors are making. I know how long they're on the calls, I, I know their numbers on a regular basis, so, get, paid is really provided the metrics that we needed to hold people accountable and that's very important. Specifically during Kobe, When you have people working from home, you need to make sure that you're holding them accountable and that they're doing their work.
And we were able to do that on an analyst position, and all of our collectors working from home so that that was another thing that's very successful, is our business continuity plan.
We had it all laid out, so when our collectors went home, we had no issues because they have everything they need and get paid.
Hopefully, I didn't miss anything. Don't miss anything. I'm going to go into the numbers now. I can't add anything to that. I mean, it's fantastic. So, now, you're gonna find out why, why we won best in class and collection. I went to London, and it was so much fun. It was. The first time I went to London. So, in January, I went to London and wrote a bath and ready to train and ready to satellite.
And so, um, yeah, our DFO since we implemented, get paid, is down 15 days, our overall path. Do you went down, 25%? And this is what I mean, they had no control. Remember, when I tell you that our customer has credit and they just have credit, right? So, at a 47% where customers were over their credit limit.
And of course, audit, Deloitte, with like, that's way too high.
So, one of our, my KPIs, when you need to get credit limits in line with what they truly are, so we would have a $10000 credit limit.
The customer does $500,000, and get paid, you're not gonna be able to do that because as soon as it even if it is coming from different POS system, it's going to alert the collector that, this is a $10000 limit, and somebody just dropped 100,000, And then, we're on the phone, immediately calling them, and, and either, collecting the money.
Because they should have paid it upfront, But, I mean, we've been very robust, and, in our collection processes, through this automation, And I don't, I definitely wouldn't be as successful as I am, without having get paid, and I know, I'm kind of a get paid baby, because I grew up with.
I'm not really talking bias, but, you know, this is really the first company that I've been able to, you know, from the beginning to end and see how successful it with, and if the first company that I've worked for, that's privately owned. And so, it's true, it's really rewarding to see these kind of numbers and have the automation.
And, by the way, we have this automation with, you know, I had to kind of work around it Because, you know, every company has its own technology issues, through, get paid. They have solutions for everything. So if there is everything that I ever needed, I put in a ticket, and I asked for it, and and they find a way to do it. So I've had technology obstacles within my own company that get paid and helped me solve within the system.
So that's where, you know, Crowley looks at me like we don't know how you do it.
I do it because I have a good relationship with get paid, You know, I I've Seen the result that that this company has provided us through through these metrics and automation and I'm like.
I said I'm I'm just very proud Crassly has been very successful for me and and if I can help anybody Eric, do you have any questions? You're more than welcome to call me. I do it all the time. Forget paid, and I don't really I don't work for that.
But I just I have I have to give credit where credit is due and I said they've made my career very successful across the board. You know, I've been in credit collections for 30 years.
So, um, And I would say probably out of that 30. I've been on get paid 10 997.
I was with Siemens, Siemens was on get paid HD supply or some other companies to get paid, I think I've been at least 4 or 5 implementations on get paid, but this was the first company where it was no credit. No, it was the beginning to the end, and seeing those results, and getting an award.
I've worked for publicly traded companies all my life, and I've never gotten an award, though, it was a big deal, I felt like I wasn't getting an Oscar in credit collection and I've been very recognized by Crowley and and, and with with all these achievements and with the team, I will say that training is very important utilizing the system to its fullest efficiency.
Want to, one of the things that I talk about the most, when I do have the user groups, when I talk to you, or somebody calls me for a reference, or something, is, to ensure that they're utilizing all the efficiencies. Because, if you don't, you're not going to get those kind of result.
So, I'm one of those people where, if I see something, and I'm not using it, I'm on the family to get paid, what does it do, what does it, you know, I want that?
And, they've been able to help me achieve that, and we're getting ready this year to go to their latest version. So, we're excited about that. It's going to give us some automation that we don't have today, and we're excited about it.
They're constantly asking us what they can do to help us through those user groups. So if you have get paid and, and you you want something that you don't have, are. You have a question about something that you want to do better in your system.
And speak up, because I found that the company in and Keith and his staff have been just just very good about communicating with us and helping us and helping us to optimize, no, but if you stay silent and you want a system doesn't work.
Because you're not asking, Assistive does work and and it's proven years after year, ..., If I hit the reasoning and pulled up a graph where you, every single year, my delinquency percentages have always been left. And that includes 20 20. So it was high, and then it, and then it went, and then it went back down after, after we got everything.
But our controls and putting that in place and reacting very quickly, and implementing that and get paid, really helped us to cope it.
And I think that was one of the things that, when I talk to get paid and they found out how well we did during the coven, because they're like everybody's having a really hard time. Actually, we did it first.
But we quickly implemented a process, put it in place. And we were dialing for dollars. And I did lose five people.
So, you know, just like the other companies, we had to furlough people and lay people off and, you know, I lost 5 5 te 5 te during that time, and I haven't gotten them back yet. But we were still even, with less, people were still able to do it and that was much more work, so think about it. Whenever covert hit, I have five people, and I have more work than I ever had, were. Dialing for dollars. Try, you know, tried to do whatever. we had to do just, or a company, and, and try it with the economic conditions that were going on to try to make sure that we were doing the right thing and that we're making our customers happy. But we're also holding our customers and our employees accountable for their work.
Well, thank you so much, Lisa. And I think, next time, I'm not going to talk at all, I'm just gonna let go.
Know, I think it's because you're providing just great examples, practical application, of exactly how a solution can, can help, you know, leveraging artificial intelligence and automation, regardless of what circumstances or basin.
And we're currently the API, so we, we, we bought Salesforce last year, and now we're looking at the API, so we can communicate back and forth with Salesforce, because not everybody has a license, or get paid. So, you know, that way. Everybody in customer care, whoever's in Salesforce, have access to all of our data, so, you know, and then they can now, you guys have also enhanced. We just went through a demo, You communicate between Salesforce and get paid, so you can talk back and forth to each other, You can, you can talk about dispute.
We kind of have that set up, not to Salesforce, but we were doing it kind of a different way before Salesforce, but we were still communicating through, Get paid, where, know, we would send it straight out of get paid, and when the, and when they would send it back, it would come back in, you know, depending on what customer we were talking about, So. Yeah, I think it's good.
Well, I know, we did have some questions come in.
And, Brian, I want to check with you from a timing standpoint, if we have time to address any questions, or if we just need to follow up individually after the fact, to answer those? I think there's a couple of good questions, Nikes, which I'm happy to extend on, I'm sure the wider audiences, including the on demand audience. So, why don't you go ahead and answer those couple of questions that have come in, and let's see where we go from there.
Alright. So, I'll take a look at some of the questions.
So, it looks like the first one is, mentioned leveraging AI across an ecosystem.
I know I use that term liberally here of the financial system. So, can you expand on what you mean by that?
So from a financial ecosystem, the way I view it in the way FIOS views, it is really looking outside of just cut credit and collections, looking outside, just receivables. It's it's looking at anything that's impacting really the financials or cash flow.
So that's being able to connect and use AI across your receivable solution, your payment system, Treasury, anything that really has an impact from a cash flow standpoint. It's being able to connect those things together.
And in this instance, what, I mean, is being able to leverage the artificial intelligence that we're using within receivables to determine the risk of a customer, to determine the likelihood of delinquency with invoices, and being able to provide that data to a Treasury system, so that they know exactly what to expect from a cash flow standpoint. So that their calculations, I'm working capital, or that much more accurate.
They can determine whether they need to potentially look at, you know, extending some potential credit to help sustain the business over a certain period of time. Or perhaps they can start paying down some of that credit as well. So it really just all depends on what information's there, and it's really about being able to leverage what AI is doing. And, again, in this case, in the receivables world, across that entire ecosystem, so having all that inter-connected.
And so the next question is related to what is more predictive and the collections process is an internal payment history or external credit bureau data. And Lisa, I'll let you speak to that one a little bit from, from your perspective. And I'll add my 2%, because I always do.
So I think your internal data is more relevant.
I've always looked at myself is that I can collect from anybody. So I may be able to collect something. You can't have good relationships with our customers as well. So I definitely think internal information. I said earlier our matrix is 40% of our internal information. and that's how long you've been a customer with Crowley.
What are your sales with crassly because we have levels and then how have you paid crassly?
And so for existing customers, that's going to be 40%. And then for our foreign customers, it's gonna be 100% in terms of what the scorecards kinda look at.
We do we do look at the D&B information, especially for new customers, so that's all we have.
But we do use, some of, you know, their metrics in there. But especially with ..., we were definitely looking at the financial stress score because we wanted to see, you know, the likelihood of, you know, a customer going out of business. I found that financial stress or has been very relevant. So we were bringing that into get paid.
And so, we did create, you know, we took some of the elements out during Kobe because we wanted to know are you delinquent with Crassly? So we took some of the elements out there and covert to make the scores Go up to a higher risk account. So, we can determine, you know, how are you pain? Are you pasty?
So we were leveraging that high Leon on our own internal information and that.
And like I said, sometimes the Dun and Bradstreet information, you're you're going to have limited data on there, too, so you may have more experience, you may be, you know.
Critical vendor. And that's why I was talking about we've gotten paid on so many bankruptcies, because like the Sears, you know, we renamed a critical vendor and the fears and we were able to collect our money. So, it's, like, I think it's very important that you have internal information, that you use it, because those signs, especially, if they're going past due, are going to be there upfront, and you need to address them.
one of the things that I will say is that sometimes you have some habits and behaviors within a within a company that you have to change with the customer, And that, when you do, to say, I'm new to your company, and you've got this customer, that always pays 90 days, but he doesn't have an ID. They turn She has 30 day terms, but it always makes its way. No decision has to be made. I hold Crowley accountable just as much as I the customer. So, if I have a customer pay me 90 days and he's paid that way for three years, slow crassly you need to give them 90 days, or you need to work it back to 30 days.
Know, There's not like lifetime terms, you know, So, I think that your own internal information is more relevant.
But, we do use that external information as well, because there's, there's times where they have information sooner than we do. And we get those alerts. So, if there's a bankrupt or there's some kind of significant event, we get that alert right in the system and we're on it, and we're doing a review on it And we're addressing it. And trying to find out we were doing that specifically. We've kind of it, because there was a lot of retailers, you know, during kind of it that we had to, we had to watch. I mean, you know, we get a lot of apparel or in the apparel business we get a lot of, you know, close, Kevin, if you know the companies are closed down. And they're not felon. any clothes, you know.
Know, Those are a lot of things that we're able to do through the system with our own internal information, that I wouldn't.
I wouldn't say that the third party information is not relevant because I think it is, I think, combined, is a best practice.
So, I said, I was gonna add my 2%. But I think we're in agreement there. That the internal payment history seems to be much more predictive of what's going to happen with your relationship with that customer. But, certainly, having external data is helpful, as well.
But it does seem that the internal payment history seems to be a bit more predictive.
So, couple of last questions here, and I'm actually going to combine two of them together, because they're somewhat related.
And it's around, you do the AI cash flow improvements to the tools, work with companies of different sizes?
And the second question was around, you know, how does the onboarding of AI work?
Is it simply taking policy and applying those rules, or does the does it have, you know, this concept of, using, example, or using information when, potentially, the question says that there isn't a bad rate with the now available data.
So, I'll address those, kind of together that. The cash flow improvements improve provided by AI, absolutely is available for companies of all sizes. So, It doesn't matter if you, if you're the small business, with few transactions. Or, if you're all the way up to the full Enterprise. Large Corporation, AI, is using your internal history, your payment data. It's looking at your relationship with these customers to help with providing these risk assessments: the automation, the approvals, the automatic routing of information.
All of that. And being able to assess the risk to help you prioritize your accounts regardless of your size.
And then, from the onboarding standpoint, I can speak to, you know, kind of what we go through in the process of developing the artificial intelligence for, for each client.
And that's taking a look at your historical data.
And we have you hold back certain information, you know, of the most recent, you know, 4 to 6 months worth of data, so that that can be used as validation.
So we'll look at that historical data that you can provide, and we'll run it through our models, and we'll look to see where there's potential correlations for your business that may not apply to other businesses.
And then that's where we'll provide predictions of where we see the risks associated with customers in the portfolio.
And then we'll compare that to the information that you held back, the most recent information.
So that you can get a good view of your standard process that you're using today, versus what the AI prediction would have been for those customers, so that you can see where that was able to be identified, how it would have been re prioritized, and help you to focus your resources in the right place.
And with that, I want to say a very big thank you to Lisa, Thank you for joining us, joining me on this webinar.
Always lovely to talk to you. And I do want to say thank you to everyone who joined today, that. I hope you got some information about how AI can really help you to improve cashflow really solve, for cashflow.
And if you do have any other questions, if there's anything we didn't get to, or if you have follow up questions. I believe we posted a way to connect with me through LinkedIn. You can always contact me. Be happy to answer any questions. You have anything that I'm not smart enough to answer, I will certainly get you in touch with the right people. Right, yes, I will send them to Lisa.
But, again, thank you so much for joining us, and with that, I think we'll conclude the webinar.
Thank you guys so much. Have a great day.
Thank you, guys. Want to what a fairly engaging presentation, Lisa, you have a second career somewhere, very. Kitty, you're absolutely right. Lisa should, should, should stop.
You did a great job, that fantastic.
Keith, can I just ask you to stop sharing your presentation map that, ladies and gentlemen, ends the presentation today? As Keith has outlined, please do feel free to catch up with him. Lisa, isn't there, or anybody that wants to put you in touch with on LinkedIn?
And I hope that you enjoyed the Soviet cashflow in our webinar, as much as I did. And, once again, a massive thank you, to .... Thank you very much for this, and just to let you know, the webinar will be available on demand, over the next year, We'll be sending out an e-mail with the information about that shortly. So, once again, please do join us for the rest of the series. And thank you very much to Finance and the team for providing such useful insight to you.
Have a fantastic rest of the week. Fantastic Weekend, guys.
Thank you. Thanks. Bye.
Senior Product Manager, Receivables, Corporate Liquidity,
Keith Cowart is a senior product manager in FIS’ Corporate Liquidity - Receivables group which features the award-winning credit-to-cash solution, GETPAID and Integrated Receivables. Keith has over 20 years of experience in various accounting and finance leadership roles, including accounts payable, G/L accounting, as well as credit and collections in large global companies with shared service centers. Keith’s focus has always been in continuous improvement and leveraging technology to automate processes to achieve extraordinary results. Keith resides in Suwanee, Georgia and holds a Bachelor of Business Administration degree from Piedmont College and a Master of Business Administration degree in Management from Georgia State University.
Director of Credit and Collections,
Crowley Maritime Corporation.
Lisa Whitehead, director of Credit and Collections for Crowley Maritime Corporation and 2019 Award winner, TMI Best in Class Collections. As director, Lisa is responsible for leading the Credit and Collections team and managing the processes of credit establishment, credit risk management, dispute management, and collections for Crowley. Her primary goals are to minimize financial risk while enabling business growth, improve cash flow and reduce bad debt. Lisa Whitehead is an innovative, multifaceted, and solutions focused Credit and Collections professional. She has over 25 years of credit service responsibilities in collections, credit risk management, cash application, special financing, international trade, third party collections and litigation. Lisa is an articulate communicator with a direct and decisive management style. Lisa has highly developed qualifications in personal development and oral negotiations. Lisa holds a Credit Business Associate certified designation from the National Association of Credit Management, a bachelor's degree in Business Management, and is a certified FIS GETPAID Super User.
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