Name: Final Recovered Recording Emburse
Uploaded: 2025-11-20T15:26:52.567Z
Duration: 1 h 1 min 44 s
Description: Final Recovered Recording Emburse

Transcript for "Final Recovered Recording Emburse": Alright. Hello, everybody. We'll give everyone a couple seconds to join us here. Hope everybody is having a good week so far. And as I see people joining us, give it a couple more seconds, and then we'll go get started. Perfect. Alright. We'll go ahead and jump into it. Hello, everyone. Thank you for joining us today for our webinar, Expense Fraud Red Flags and Prevention Tactics CFOs Must Know. My name is Taylor Decourt. I'm on the North American events team here at ImVerse and your host for today's event. Just a few housekeeping items before we get started. This webinar is being recorded. You'll receive a copy of the recording twenty four hours after the event ends. On the right hand side, you'll find the documents tab with some curated resources we highly recommend you check out. At Imbursed, we encourage you to ask questions throughout the webinar. You can do so under the q and a tab at any time. Chat is also available today, so feel free to engage with your fellow webinar attendees. And I'm pleased to say that this webinar is eligible for one CPE credit today. To qualify, you will need to stay for the full duration of the webinar and engage in at least three poll questions. Poll questions today will be scattered throughout the session, so keep an eye out for those and be sure to participate. And with that, we have a lot of great content to to go over today, so I'm gonna jump right into it. In today's webinar, our wonderful speakers are gonna go over, the state of expense fraud in 2025, re real world red flags CFOs must recognize, how to build a fraud prevention framework, and where CFOs should focus next. Speaking of our wonderful speakers, I'm gonna invite them to join me on stage here today. And as they join us, I'm gonna introduce, our lovely guest speaker for today's event is Kevin Perminter. Kevin is a research director at IDC who analyzes trends and technologies shaping the fintech ecosystem including accounting, payments, and financial applications. He leads both qualitative and quantitative research that guides technology buyers and suppliers on the evolving role of finance systems in modern business. And along with Kevin, some of you may be familiar with Mike Daley. Mike has joined us for several webinars recently, and it's always a pleasure to have him. For those that do not know, Mike is the global VP of presales at Imverse and has twenty years of experience in accounts payable automation, b two b payment digitalization, and financial analytics. He's a recognized thought leader, and APSC certified professional. Thank you both for joining us. Thank you. Thank you. Awesome. And before I hand it over to our lovely speakers, we're gonna go ahead and dive into that first poll question. So I'm gonna open this up for us, and we're gonna ask our audience here, how confident are you in your company's ability to to detect expense fraud today? So now I've gone ahead and I've opened that. So, again, in that engagement center, you where you sign the chat and the q and a, you should also see a tab for polls. Please go ahead and submit your poll responses there. As a reminder, you do need to submit a response for all of our polls today in order to get that CPA credit. And I do see we are getting, that those responses in. So I'll share those with everybody. Kevin, Mike, I think we're seeing some high rates or high responses for slightly confident to confident, which is really good, but definitely think that hopefully, the goal of today is to get those to very confident. Am I right? 100%. Absolutely. Perfect. Awesome. Well, we'll give our audience here just a couple more seconds, and then we'll close that out, and we'll dive into how we can get you to be that very confident. Alright. Alright. Five more seconds. Four, three, two well, alright. We'll close that out. Perfect. And, this time, I'm gonna go ahead and hand it over to Kevin, to get our webinars started. Absolutely. Thank you for inviting me. I really, really love this topic. It's a topic that, I tell you, at the end of the day, this is the one that is, both near and dear to me. So as you guys know, I wear, three major hats here at IDC. I I talk with vendors. I talk with buyers, and I talk with, you know, the investor community. So it means that I'm on the road a lot. So this is a topic that is not only a topic that I cover. It's a topic that I live, 25 trips a year, way too many out to Las Vegas to to be to be fair. Right? Way too many. But, anyway, so I I figured what we could do is let's get started with, you know, sort of the the big picture, zoom out a bit here, and and sort of talk to the idea of, let's talk to the the idea of expense fraud, and how it right now, it still remains one of the most persistent forms of of leakage. Look. Look. Everybody's chasing leakage around. Right? They're looking at, all sorts of places. Of course, they're looking at waste, fraud, and abuse. But this one is very, very persistent. It's been a problem for a lot of businesses for decades. Right? And as as and and we're gonna talk about this. But as as the the technology on the on the the the company side increases or improves, so too does the techniques, on the fraud side. Right? They really, are in a dance or race. And I think every so often, you'll see that the fraudsters sort of move ahead a little bit. So, what we're definitely seeing is that even as companies digitize their expenses, the detection, rates that they see are still flat. Right? So, again, you could see that push pull as organizations try to, move ahead, and get ahead of some of this fraud activity. You'll also see, the organization sort of, or the the the fraudsters sort of get more sophisticated too. Finance teams, right now, they they're embracing automation, to to try to, combat that. And and when it comes to, things like the oversight process, though, we still have seen that they haven't evolved quite yet. Right? So we see a lot of automation, when it comes to invoice ingestion, for example, tons of automation there, lots of automation on the collection side. Right? But when it comes to, some of these oversight positions, it still is, a bit less automated than some of these other places. And what that does is that creates an environment where, fraudsters, are able to, get ahead. They're able to get ahead of the the organizations, and do their fraud faster, in a more subtle, nuanced fashion, and also make it harder to identify. Right? So as you can see here, we have some stats here. Some of these I really love. As you can see, 5% annual revenue, is, what we are are looking at here in terms of, measurable loss each year due to fraud. I mean, that's amazing. Right? That is billions and billions of dollars that businesses, on a global stage are are are are losing. And then to put that in further perspective, you can see here the median loss is about $50,000. These are all from, the ACFE, 2024 report. And then you can see that the loss, even for a small business, and especially for a small business, $50,000 a year could mean the difference between them making payroll, in q four or not. Right? It really does get down to that level. That's why it's so important. About one out of four employees are, admitted to submitting personal expenses. Heavens no. I wonder I wonder if we can if if we're really getting a a true measure on one out of four. I think it's probably closer to, two out of four, in my in my estimation, especially since I have a five person team, and I always have to look at the, you know, the the, expensive reports. And I always have to do, Sherlock Holmes y sort of stuff. So I I really think that number's a little low, but it's still indicative. Right? And the weak spots are pretty consistent. Too many systems, inconsistent policies, and brittle policies. Right? You'll have policies that, you know, haven't taken into account the new things that you can do. You know, airlines are now selling, amenities and upgrades. You can buy food at 37,000 feet, you know, those sorts of things. And some policies just haven't caught it caught up to that, and that that, you know, sort of changes or or turns into a brittle policy. So, again, consistent policies and brittle policies that can easily be broken because they haven't caught up to the latest way of doing things. And then, of course, reliance on manual checks. All of it just sort of really, builds into this place where, you know, fraud can run rampant. And then the other thing the last thing that I'll say here, really important, a lot of these organizations are stuck doing, sample based reviews. So they take a fraction of of of the, you know, spin flow, and then they look for fraudulent activities, or they take a look at some of the way outliers. All of a sudden, someone tried to expense a Ferrari or something like that. You know, rent renting a sports car in Vegas. Things in Vegas get weird. Right? For sure. So, you know, these are the kinds of things they they they'll look at that, but they they they don't have the tools to look at the entire sort of spin flow. But with AI, that's changing changing rapidly. So I do think that it it does come down to, not that it's a malicious intent. We're not chasing malicious actors most often. Most often, it's a result the biggest risk that you're gonna run into is complexity. Right? Especially when you're talking about fragmented data, that makes it easy for small issues to hide, amid the big ones. So that's a that's a huge problem, and I do think, that AI is the the sort of ticket forward, to kind of, to address that. So I think there are a handful of forces that, that are are converging to make expense fraud much more difficult to manage even for teams that consider themselves fairly mature. We'll see that, you know, most teams, they they they're fairly mature. But in actuality, they're probably not. We'll we'll have some data on that a little later. Mhmm. I think the first point is is definitely around hybrid work. That puts pressure, of course, on these businesses. It changes it it's changed, the day to day spending behavior in some really major ways. Most policy frameworks didn't anticipate some of those changes. So, instead of large predictable travel claims, finance teams are not dealing with, like, a small stream of, decentralized expenses. So coffee runs, lunch, you know, cell phone, sort of usage, home office purchases, chairs, those sort of things. These piecemeal sort of, expenses, decentralized expenses, they really trigger, that you know, these things, they rarely trigger, excuse me. They rarely trigger, any formal approvals, but they also are it's like sort of breeding ground for, bad actors. These they the transactions look harmless, but in volume, they can really add up. I think the second one, definitely deals with the explosion of mobile and digital payment tools, which have, you know, really reshaped how employees pay for work. So you've got virtual cards. You got Tap2Pay. You got mobile wallets. We see that as well. And a lot of these lightweight SaaS subscriptions, have made spending pretty frictionless. But the downside of that, of course, is that compliance becomes harder to track, and it it really does need to be down at the individual item level detail. And getting that detail can be pretty inconsistent. Merchant data, level three data is messy, or or nonexistent. Right? There was a really great example that I had. I ran into this as, as a manager. The person was was going and and and sort of using their their their, tap tap to pay at a, a local convenience store. And the the, you know, the the the amounts didn't look too too crazy. But as it turns out, it wasn't until, you know, the company started diving into the level three data that they were finding that, yeah, these were, like, you know, snack runs, which isn't terrible. There were also, like, prepaid gift cards in there. There were also cigarettes and and, you know, sort of lottery tickets and or, some other gaming sort of thing that they did. So it wasn't until the level three data made all that stuff, available that you actually saw that, yeah, this guy, it looked like, you know, $50, and it looked fairly harmless. As it turns out, he was getting $10 gift cards and cigarettes and all this stuff that you don't really pay for as a company. So these are the kinds of things that you definitely find. But I think there's, and then the last one I'll I'll go into here, that artificial intelligence, which is really speeding up both sides of the equation. Right? So it helps us as managers, really sort of find, and and helps us automate, you know, the process of reviewing, and and maybe even it helps automate reviews along with, you know, cluster anomalies or flagging unusual patterns. But it's also helped employees or bad actors. They have access to all those same tools, and now they can it's a pretty trivial matter now to generate a convincing receipt template, a cleaned up image, even adjusting metadata. Right? So it's an arms race like we talked about, that that that horse race where, you know, companies and bad actors are all sort of moving, and AI just sort of gives them both fantastic tools to keep that arms race going. So put those together, I think these dynamics, create a faster, more connected environment, which is a plus and a minus. And this is why, oversight, organizations or the or oversight functionality within these organizations really still struggle to keep up. Alright. So as we move to the next slide here, I would say what's what's important about this one here I wanted to give a few examples. As you can see here, there are tons of really great examples that we that we pull together. So, I'll I'll get started with a few that I I really like. The one that, that I'm looking at here is definitely around generated, receipts. So we we definitely saw, a good example of the generated receipts around, yeah. So we definitely saw a good example of a travel and a, a travel organization or a user out on travel, submitting the same hotel stay twice. And they just, you know, lightly edited the, the the receipt. We definitely see mileage double dipping. We see mileage double dipping on a lot of levels. We see mileage double dipping when it comes to, manual, mileage logs or auto capture, trips reimbursed, and they're the same drive. Right? You know, those are all examples of of duplicate double dipping, receipts. When it comes to, vendor collusion, this is one of my favorite ones, vendor collusion, because, not that I do it. I but I have a great store I have a great story here. Ran it I was at a conference, a travel and expense conference, and I got the the chance to sit and talk with a great, sort of, a travel manager, very large company, 10,000 travelers at any given time. And, there were a couple of really sort of, great examples. There was one where the the catering company was artificially boosting headcount for these roadshow, events that they were doing. And and then between the the vendor, the caterer, and the the organizer, the person that actually organized the events, the event manager within the company, they were skimming maybe $5,000 each for each roadshow. So it's 20 roadshows, and at the end, you you know, they had a 100 k that they that they split amongst each other. And as it as you can imagine, you know, you go to these roadshows, they're not necessarily counting. You know? No one's really counting. They're more about, you know, did the did the thing sort of go off well? So we saw that. And then another one here, and I know I'm getting close on time, so I wanna speed up. But another one was, this company did a lot of, like, corporate reward events or award awarding. So there was, like, you know, they do rewards for one year of service, five year service, whatever. So, anyway, they they would they would contract with the local jeweler to do it. So the person that was responsible for coordinating this event said, well, you know, it's a jeweler. I like watches. So they ended up, you know, sort of trying to ex they they ended up expensing a Rolex. And the way they did it was they they worked with the jeweler, and the jeweler would expense the company 5 you know, $2,000 at a time. Just enough to stay under the radar. And by the end of the year, the guy had a a free Rolex. He felt he deserved it. And I get it. Right? But, those are great examples of vendor collusion. So look, I don't wanna I don't wanna, you know, sort of belabor the point here. The point is there are all sorts of really, sort of terrible examples. Some of them are funny. I I there was someone that that, tried to expense, plastic surgery. I've I've I've ran ran into that one, legitimately tried to expense plastic surgery, and, you know you know, called it an expense, and and it got it done. But, again, they didn't have the the, you know, they didn't have the sophisticated, you know, tools that you do now. But I've seen a lot of lot of, malfeasance when it when you see it there. And some of it's funny, some of it's not so funny. But you definitely see a ton. So I'll leave it there. Moving on to the next one. This is an important one. And and and I wanna really, focus on this fraud perception gap. Human beings are are just, are are are built a certain way. You know, we we sort of have a little bit more confidence in ourselves than we probably should. I feel like I'm taller than I am. It's just the way the way it is. Right? And I think a lot of CFOs run into the same sort of thing. As you can see here, 81% have confidence in their expense oversight. Only 14% of actual, detection or actual fraud, you know, incidents are detected. So 14% detection rate, although the, CFO believes he's got a grip on it. Right? And it's that it's that perception gap that allows some of this malfeasance that allows some of these bad actors to thrive. So with that, I'll throw it over to, Mike to to go through a little bit more. Yeah. Thanks, Kevin. That was a heck of a tee up too. Right? And I think the the polling question that tailored to the top kinda added up to almost at 81% for very or kinda confident. Yeah. Sorry, guys. I love your I love your confidence, but unfortunately, the data is showing you might be a little bit of hubris, but we're trying to help you. So though we're gonna move from the broad picture now to specific warning signs. Right? So some research that Kevin alluded to earlier than most cases of expense misuse. Honestly, the the the data was already revealing the problems. It's just that the organizations didn't recognize it at all or didn't recognize it soon enough. And meaning on that same study that, Kevin talked about, the Association of Certified Fraud Examiners, that's a mouthful. They found that fraud on average, the schemes go for eighteen months without being detected. That's on average, guys. So it rarely hides completely. The clues are there, but it's buried in volumes of data or patterns or behaviors that are intentionally trying to mask it or hide that needle in the haystack. Right? So I'm gonna walk through a couple of examples now what this is gonna look like, and then I'm gonna pop it back to Kevin. He's gonna talk a little bit about some of the tools we're gonna have, like AI and analytics to start spotting them because it's an evolving landscape. Right? So let's go to the next slide. And, really, when I when I see this, I'm talking about the red flags. And we're gonna I'm gonna kinda put these in two buckets. Right? There's a behavioral bucket and a transaction level that you wanna look at. Now the transaction level is still behavior driven. It's just where you're looking for the problems that's raising the red flag. Right? So let's talk about behavior side first. One of the strongest predictors is policy gaming and anyone knows how to do it. Trying to trying to avoid the problems. Right? It's classic case. Employees always submitting an expense report for $45 because they know that the manager approval happens $50 and above. Right? So it's a gaming's a very common area. Another one is timing. Hey, organization a lot of times companies pick how they want their employees to be reimbursed in their timelines they have. I'm a big public company, we say everything for the last quarter has to be submitted by the end of the quarter because we're closing the books and it's the eleventh hour on the last day and maybe that employee is just a professional procrastinator or maybe they're trying to build that haystack around the needles of personal reimbursements that they're trying to hide. Right? And then the last one, our behavioral one is category alternating. It's like I'm putting the spend in the meals bucket or the office supplies with different GL codes to try to avoid hitting the budgets to a single line item. Right? So there's opportunities to hide a lot in those cases. Now pivoting over, let's zoom in on the transactional level. There's some hard data patterns out of this. You know, Kevin touched on duplicate submissions. They're a big common one. Sometimes it's like fraud's not always intentional. Like, there's there's intent behind it and whether it's malicious or not. But it still happens and this is fraud. I know that I'm gonna be getting my credit card in that I used to reconcile before I submit the expense but I forgot this time and I submit the expense report and then I submitted that same receipt again when I've got the credit card data in there and I got double paid. Right? Round numbers are a dead giveaway. Do they happen once a mile? Yeah. But if and a pattern emerges at an individual employee or department level that, wow, every time they submit an expense, it's always rounded to the closest $100. Like, that's a little bit of a red flag opportunity. And finally, on this page, you know, there's off hours expenses. Like, someone at Saturday evening at 8PM and all of a sudden they're putting some home office expenses online and you look into what they're buying. First, I'm sorry that person's doing work Saturday night, if it is even happening that way, but likely, it's gonna be that they're trying to push something through and you wanna be better aware of that. Okay? So we've got behavioral and transactional. Now let's go to the next one. And then there's areas we gotta think about, well, what categories am I gonna be worried about that really we wanna lean in harder on? So meals and entertainment really remains a top problem area. It's so easy to learn, to blur like the personal and business spend happens there. A lot of times, hey, I'm out for the evening. I thought I slipped it in my pocket. I lost the receipt. Easy to duplicate. Right? Second one down, Kevin hit on this hard before. Mileage continues to be a chronic issue and I don't need to replace the elements that he talked about there. And recurring vendor payments are they're kind of like you heard a lot about quiet quitting the last couple years. Well, this is like a a quiet frauding. It's the gift that keeps on giving. Maybe the PO ended, but I'm just gonna keep sending invoices in and see if the AP department stops paying me. Right? So you've got risks allocated like this. You've got behavioral, you got transactional, and the category they tend to fall in between yield and expense, mileage, and recurring payments. Now I'm gonna pop this back over to Kevin. He's gonna talk about some of the tools we're now having at our fingertips to help us spot these. So go ahead, Kevin. Yep. I was on mute there. Sorry about that. So yeah. Are we gonna pop a forward one for you, Kevin? Yeah. Please. There we go. No. I'm having problems with my technology. There we go. Alright. We're good. Alright. You're up, buddy. There we go. Alright. So, we definitely see, we definitely see AI and analytics, how they are they're exposing sort of the hidden fraud networks. I do think that here is where technology, changes the equation quite a bit. AI, analytics, and and I think even, the machine learning along with, Agintiq and GenAI together. Right? These advanced AI solutions really can help, humans see, or help help help organizations see patterns that humans couldn't see. So there's a lot of of of sort of misunderstanding about, AI. Will it replace jobs? Maybe, maybe a few. But one thing that it does do, it keeps humans doing human jobs, and then it it it sort of opens the door for computers and and sort of the AI to do what AI does best. Right? So, if you've if you've got 10,000 travelers in a year, going through 10,000 experience reports, that's just not human work. Right? It's just not. Right? And and it doesn't really matter how many humans you throw at it. That's just not how our brains work. But AI can do that. Right? So instead of reviewing transactions in isolation, AI really could, AI really can, evaluate, all of the behaviors across, different users for different time periods, in different categories. It can do all of that, simultaneously. Right? So even, even in real time, it can give you a lot of that that insight that you can use to guide spend. And there's another piece that I wanted to throw out here as well. It's not always about finding the malicious acts, that they're, you know, that that are there. Sometimes it's about, also being able to utilize these spin patterns to direct spin. If you are able to to to to, you know, get a hold of that leakage, plug that gap, now you could start directing spin and doing some really great things, even sort of, bringing in revenue into the, you know, t and e department, with some some choice, use of of, payment tools and those sorts of things. That's a that's a conversation for a different day, but the point is, AI opens the door to all of that stuff. So, again, the point is not just to catch the fraud, but to predict it. Right? You can you can use, your data. You can use even external data, historical data to teach these systems what normal looks like, and then you can surface those anomalies in real time, again. And you can even do that post audit, or or even, you know, sort of in situ. So lots of great opportunities as you can see. But the point is, you know, let's let computers do what they do, and let's let humans do what they do. And and then I think the entire department is able to to, to grow and and sort of move forward and and and be agile as it needs to be. I'd say, yeah. So why do so many organizations miss the red flags? Yeah. I'd say definitely there's two main reasons. One, the data lives in silos. Right? So you've got your travel data in one place. You've got your HR data that could be useful. You've got your and that's in another place. You maybe have, sales data or, you know, customer data, depending on where you're traveling to. That all lives in a different place. You've got different card feeds, reimbursements, procurement. They're all in different databases. Right? So the the the the idea is those very simple questions. Right? Are we spending too much with this airline? Well, in order to do that, I've gotta I've gotta go and do data pulls from six different places, and it just means that you're too far away from the real insights that you need. Right? So that that siloed data really has some some powerful knock on effects, right, because it slows everything down. The second is that finance teams are still way too focused on exceptions because, again, that's how humans work. Right? There's you got four oranges and an apple. Everyone's gonna ask why is there an apple. Right? Instead of saying, you know so they're always gonna look at the exceptions and not the correlations. Correlations are more difficult. Correlations take more computational, you know, sort of, horsepower to kinda get done. Fraud rarely that's a problem. Fraud rarely sticks out with one glaring transaction. Like I mentioned, the person that would go to get cigarettes and scratchers and and, and, and and what else would oh, and prepaid gift cards. The guy that would do that, you know, twice a week. That wasn't something that, you know, stuck out in a pattern, but it was there once you were able to dive into the data. The patterns are only, obvious when they're viewed from across the entire system and over time. So, in every in almost every use case, data has been flagged, somehow, somewhere. It's just not connected. So the big takeaway here is the lack of connected analytics. That's what I'm gonna go with. The lack of connected analytics really keeps organizations blind. They really do. Oh, you're stealing my thunder, Calvin. That's coming off. Alright. It's on the tailwind. Great. Yep. We're gonna take this, time to open up our second poll here. So, you'll see that poll tab pop up again in that engagement center. As a reminder for everybody, you do need to submit responses for all of our poll questions to get that CPE credit. So, I'm gonna leave this open here for a little under thirty seconds, and then we'll close it out and we'll continue to move on. And I'm gonna share those responses as well. We're seeing, yeah, definitely a lot, with that in that meals and entertainment category. I know our chat has been sharing some of their stories, and they definitely seem to be around that trend too. So Love me. Keep it up, guys. I'm loving all the stories we're getting. We should have a competition for who has the best story. The best story. I love that. I just They really yeah. I'm going to go through all of them. I've seen quite a few. Awesome. I definitely think the wine as barbecue sauce was my favorite, though. That works. That was interesting. That was I'm reading that one too. That one's pretty good. There was a restaurant in Suburban Toronto before COVID hit that changed all the menu name items to office supplies. Interesting. Yeah. So it was a little bit of a collusion. Collusion. With a collusion. Yes. Well, I hate to bring the fun to it in, but we're gonna go ahead and close out that poll so we can keep moving here. And I'll go ahead and move on to our next slide and hand that back over to Mike. Doing on time? How are we doing on time, Taylor? So we're we're running a little behind here, guys. We got a lot of content to still go over, but, I'm gonna entrust Kevin and Mike here to move us forward so we'll dive back in. Okay. Well, good. Well, Kevin really did a great tee up on the tools we have and hopefully, I shared with you kind of where to hone in your efforts for your audits and fraud prevention even, not even having to look at it afterwards. So now that we covered what it looked like, now let's talk about how to stop it. Right? The most effective finance teams don't rely on a single control. They got a layered approach. So we're gonna talk about a layered approach. We're gonna talk about ingestion. We're gonna talk about where to prioritize. I'm gonna pop it back to Kevin for some other tools and we'll wrap towards the end. Right? So I'm gonna go to the next slide here, guys. But it's a layered approach. Think about back in, medieval days, if you watched a good castle movie lately or anything like the knights. You know, they build the castle on a hill because it was better to defend, and then they put tall walls made of stone because it's harder to get in and harder to destroy, and they put a moat around the castle. Right? You wanna think about the same way of the layers you're gonna have in place for your solution around preventing fraud. Right? So the first, the outermost layer, the top of the atmosphere is that automation stuff. It's leaning in on technology to validate and flag those anomalies, but in today's world, that has to happen in real time. Right? So automation is gonna filter out the noise. So either it doesn't have to get to your auditors at all because it was preventative and it happened at the end user before they submitted, but it is gonna make it easier for your auditors to be filtered in and focused in on the real risks. Okay? The second level down in our atmosphere here is the actual audit itself. You know, by checking the compliance boxes, that's not gonna be enough. It's gotta be risk based auditing where the high value, like the big dollar ones, in the high frequency categories get prioritized automatically. Now that poll you guys just all replied to, office supplies are only showing 14% in the second question. I think that goes back to, like, what Kevin was talking about earlier that we haven't caught up yet as people went remote in the early days of COVID. Now we've a lot of people stayed hybrid. There's a lot of stuff happening in that home office space that I don't think we're leaning in harder enough on. Right? And this is then where the third layer comes in. It's an accountability layer. This is where it gets away from technology and services and more the people in the field that you wanna empower with this. Like, the employees and managers need to understand that every submission represents the company's funds, not personal funds. Alright? So when you think about those three, now let's talk about a framework on how we help this happen. So the first layer in the automation, right, when you get to that, first, it's gonna be talking about ingesting those receipts or those vendor invoices. And it's utilizing tools like optical character recognition to take that picture of a word or an invoice number or a receipt dollar amount on that page and turning into someone's computer readable. Right? So embedding artificial intelligence, AI, and OCR, you got validation directly from the expense workflows, which means the issues are getting caught before they even reach review or before they're paid. Right? So that optical character recognition tool is gonna extract the data, maybe verify the merchant data details, and you're gonna check for mismatching. Then you lean in harder on the AI side, and this is where you get to lean in the most historical patterns and behaviors and look for, oh, wait. This this spend is ending in around a number of a $100. Right? The system can flag or auto reject out a policy early on and make it easy for that end user. You've got compliance with convenience. Right? So it's a win win. The the company is winning because it's getting better compliance. The end user is still winning because they got better convenience. Okay? So on the next page, we're gonna hit on the risks and how you prioritize, guys. This is gonna be key. You gotta think about it this way. Not all expense is created equal from a risk perspective. Right? For example, here at Enverse, we help companies, like in finance teams, define their audit rules in system that signs different level of scrutiny maybe based on a dollar amount or category or the specific department or behavioral patterns that we can impact on that side. A great for instance here that I was just hitting on is those office supplies. Like a low value office supply claim, you know, it's gonna maybe get an automated check, but a recurring entertainment or vendor claims that are recurring may trigger that manual review or a deeper look. Escalation protocols then are gonna ensure that once something, excuse me, once something crosses a defined threshold, whether amounts, repetition, etcetera, it's automatically gonna be routed to to the right reviewer. Right? Now if you think about it to the next step, and Kevin hit on this a lot about the silos that you have out there, the disparate systems, Fraud is gonna find those gaps between systems. Like, people are gonna try to exploit those areas by integrating your expense data with your ERP, your accounting system, right, with your travel spend, with your with your payment data, all into a single unified analytics tool. You're gonna be able to suss out duplicate submissions more quickly out of policy transactions, and it's gonna make unusual vendors or unusual vendor spend a lot easier to detect. So this real time synchronization is a big key. So integration transforms the oversights from being this reactive. We hope the 10% we're looking at might have some problem and then we figure it out to a proactive evaluation and and continuous monitoring. But again, remember, technology's half. It's getting the people involved as well. Employees have to know what to expect. Managers should feel accountable. There's training in this as well. Okay? So from here am I on the right slide? I think the next one up is the case study. Right? How are we doing on time? Did I catch this up at all there, my friend? I think we're okay. You're doing good, Mike. Plums are right? Okay. We're fine. Yep. Yes. Good. So this is an amazing conversation. So, Jotun is a global leader in painting and coatings. Right? They have been managing 13 disconnected expense systems globally across a 100 different countries and multiple languages with limited visibility and a lot of manual processes. And it really made it impossible to to spot those dupes and out of policy spend. And because they didn't have the technology to support them, they didn't have a way to hold their teams and leaders accountable back to it. Right? So Stiles approved without full visibility. It may it may for an onslaught of challenges. So once they were able to fully deploy Enverse's enterprise expense product along with our analytics tool, Jotun's unified that. Get rid of those styles. Right? The global expense management, and they gained that real time transparency that we're saying is really today's hallmark of be having a successful fraud reduction or prevention program. They needed this because they they process over 80,000 expense reports a year. So not only did they have the win with this, but they also found a pretty fast ROI out of it inside of a one year investment time frame. So that was helpful to them. But like we talked about the top of this, guys, is that this is an evolving marketplace, and you've gotta keep up. Like, yesterday's solutions for stopping risk and fraud is not today. So we are in the midst of beta testing today and rolling out early in 2026 a product that we're gonna be, labeling as em, Enverse Assurance. Right? So the Assurance is gonna automate continuous auditing for every transaction from the time it hits the system, and it's gonna more rapidly flag high risk, spending across employees, departments, etcetera. Right? It's gonna use a combination of artificial intelligence and some predefined business rules that we have a need in the system really embedded in it. Again, not as a bolt on. So we evaluate those real time transactions, gonna give alerts of nothing's gonna slip through the cracks or virtually nothing. Right? The value in this is not just catching that fraud, but it's the other side, the human element. It's it's about building the confidence in the process so your employees feel this is doing stuff right by the company, and I'm still making it easier for my users. Provide the compliance, reduce audit fatigue, make it better spending decisions, all while it's still convenient for your end users. Right? So that's where we tee off from there. Kevin, I'm gonna pop it back to you for getting close. Alright. So let me see here. That is the slide future outlook? Alright. Great. So yeah. So I would say first first, I should say, that's super exciting. Imbursed Assurance really, is a step forward, I think. But I like I said, let's continue looking forward. This is an important piece, right, that future outlook, of what, CFOs, should be focused on in the in the short term and the long term. Of of course, the goal is not simply to catch fraud faster, but it's to build systems that are smart enough to anticipate it. Right? Mhmm. So that's that's the main thing. So, as we go to the next slide yeah. Here we go. So I we definitely think as we look ahead, I would say, one thing that I wanna bring out, is the finance function is really stepping into a different era. I think this era is probably shaped by, intelligence, integration, and insight. Right? Those are the the the the big I the big three i's. And, I think we're moving on from, you know, moving into that paradigm as opposed to manual checks and historical reporting. So the idea is to stop looking backward, even when it comes to, you know, expense management or any of the office or the CFO functions. They all sort of have this tendency to look backward, and now looking forward. I tell, everyone that the CFO and everybody in the office of the CFO now, that their their job has become fortune teller, chief fortune teller. It's what I like to call the CFO, and and everybody's sort of gotten that that job as well. I'd say the next frontier of fraud prevention is definitely gonna be about predictive analytics. So instead of reacting to anomalies, after they show up, you'll be able to, you know, identify those shuttle subtle shifts ahead of time. And I think this changes the job pretty much entirely. Right? Financial teams will be able to, move on from, like, fact finding and clean up after the fire happened to anticipating, what what what's gonna burn down or what might be burning down or or what could burn down and really sort of act in a a preventative manner. So, again, the work is about, you know, becoming less about catching people in act and more about understanding those early warning signs. So, again, automation will also evolve. We definitely will see that. And, today, a lot of of of systems are still focused on validating transactions. I think in that coming phase, those same systems will be able to advise on policy and budget controls and even process changes. So AI really is is changing, every everything. So the the big takeaway here is pretty straightforward. The organizations, we really feel that the time is really now to invest, in sort of building that predictive intelligence, that that automation that, you know, there's ethical automation as well. Right? And, that tightly integrated system build investing and building in those things, the time is now, and in order to build a a more resilient, sort of financial function and a more, you know, sort of proactive financial function. I'd say as you as we just talked about here, the future of fraud, definitely lies in prediction, not necessarily, you know, being able to look backward. Finance teams will be able to use AI to detect shifts in behavior, not just single, you know, single transactions or single violations. But the I wanna say that the key, though, is that it has to be embedded. The way that the folks here at Imbursed are building it, that it's embedded. You don't wanna have to punch out and go somewhere else to find this, bring that data back, then merge it. That's that slows everything down. So, again, you know, as the technology evolve, I think the role of the office or the CFO will change as well. You'll see that, the job of the financial leader will become more about, being stewards of data, and not just managing spend, not just managing spend, but being able to anticipate risk, maybe even guide, strategy spend strategy when it comes to it. It all is gonna come down to trust and transparency and embedding those kinds of things into into the system, imbuing AI with the capability, to to to get to all that information. And then that will close the the trust gap that that lives with, lives inside the office of the CFO. So, again, fraud prevention is becoming a strategic advantage. I really believe that, and the CFOs that embrace that now will lead to, a whole new generation, I think, of, intelligent, and resilient financial organizations. And with that Awesome. I'll give it over to Mike, and he's gonna Yeah. Give us our takeaways. Yeah. Kevin, I I I I wish we had this for a full day session. Like, your stories are amazing, all this stuff you're hearing, and I wouldn't doubt that our attendees would love to dive into every kind of use case to help them figure this out. But we have to make these a certain length of time in general. Right? So Right. The key takeaways, my friends, you know, just by the way you responded to the first poll question and then the data we put up. Like, this is an evolving this is an evolving pattern going on here, guys. Fraud is really moving fast. Like, Kevin highlighted a couple of AI, like, making fake receipts is all over everywhere. Right? It's digital. It's more automated. It's increasingly hard to detect manually. So if you're not continuing to evolve your audit process or just your processes before audit to make the things stop and, you know, have a stopgap or preventative, you're gonna be behind. So the pace of your visibility control has to match with businesses today and AI is making business move faster just like thirty years ago with the Internet did. Right? Guys, the second one, the red flags. We hit on a lot. Yes. The deck will be available. I saw a lot of those questions come in. But don't leave it just to that. Feel free to reach out to myself or Kevin outside of this too, but the warning signs are in your data. Connect all of your data. Have have a seamless integration. Get rid of the silos and lean in on AI and advancements going on in your workflows in the systems like what Enverse offers. Most fraud isn't hidden. It's just overlooked because it's hard to know where to look. And employees know that. Like I said, they're gonna build the haystack around the needles they're trying to hide, so you gotta lean in harder. And finally, the prevention is amazing. And I I did do a webinar over the summer months that was about compliance through convenience, about how we need to continue to drive compliance, but there's more and more demands from employees to make this easier today. So if you have the tools and utilizing AI and the OCR tools and and developing an accountability situation among your employees, you're gonna have the win out of this. Right? And it's gonna find it earlier and earlier in the process saving AP time and really setting frustration from those employees that didn't, you know, accidental fraudsters, right, when things don't happen intentionally. So, ultimately, you got the bodies, you got the technologies. You're gonna have a win out of this, and you're gonna see fraud being reduced to nothing until the next big wave comes. So from that, I'll pass it back to Taylor for the final poll question. Yeah. Thank you, to our wonderful speakers here. We're gonna go ahead and before we open it up to, some of our poll quest or some of our questions from the audience, we wanna end on one last poll question here in order to get those CPE credits for you guys. So, I'm gonna open that up. Go ahead and submit, your responses for which initiative would have the biggest impact on reducing fraud at your organization. Again, that's in that poll tab in the engagement, center, same place where you find the chat and the q and a and all that good stuff. So we'll give you guys some time to answer that here. Reminder, you do need to submit responses for all three polls here in order to get that CPE credit, and then we'll go ahead and we'll queue up some poll questions here. I think we can take one or two before we have to close today's session out. But I am gonna go ahead and share those results as they're coming in here. We'll give you guys a couple more seconds. I think about twenty more seconds here and then we'll close this one out too. But looks like strengthening policy and training Yes. Embedding AI or automation. Yep. I think those are Forever move. Definitely expected responses. Yep. Yeah. I mean, it it it's always a lot of that today. Yeah. So much of this is just it comes down to a people problem. Right? And strengthening policies and training helps a ton. Yeah. I I I like that that came out first. Absolutely. Yeah. That seems to be the the leader for sure, in our responses here. So with that, guys, we are gonna close out this poll Yeah. So that we can take a question or two. We had some really good ones coming through, so I appreciate everybody's engagement in today's session as well. Let's see if I can queue up, a question here. Lots about if we will get the if they will get the presentation, after this call. Yes. We will make sure to send out a copy of this presentation deck for you all, regards to I tried to keep my I tried to keep my stories PG, but I got some got better ones. Sure. I love that you shared your stories with us, Kevin. That's why we were so excited to have you today. Sure. Awesome. I wanna make sure, I'm answering these questions here. And let me comb through these. We did get a lot, so bear with me. We have one around, some issues for possible a lot, some around saying that the issues that they see with fraud, are not easily detected through AI. How can you continue to build upon, programs or use AI to better catch those? Can you guys share with us talk a little bit more about that? Because AI definitely isn't the end all solution here. So maybe reiterate some of the other elements they can they can put in place to help build that successful fraud prevention strategy. Yeah. I think I I think I sort of talked to it a little bit, but a good fraud prevention strategy, like you mentioned, AI just throwing AI on top isn't quite enough. You really have to I I find that the data layer is oftentimes where a lot of the problems lie. I talked a little bit about how you we're we're dealing with siloed data, disconnected data. I have one organization that I work with, and in their in their customer, billing area, there are 47 different applications. Right? And in in their in their, AP, area, their spend management area, there's 37 different applications. So you can imagine bouncing between all of those just to try to find the information you need. So the the long story short, a lot of times, you can start to invest in that data, layer. You can start doing some data hygiene, some data, cleansing, and and then you'll find that AI would be a lot more, effective. And even the people using the solutions would be happy. Yeah. I'm I'm gonna I totally agree with you, Kevin, on that. Look, guys. AI is is a tool. It's not the panacea. Right? It's not solving world hunger. But it is it is the tool you need to start deploying because, honestly, the fraudsters are starting to use it as well. Right? Talk about that. I think Kevin and I both touched on, you know, fake receipts that make from AI that can make it look really well. So look, it's it's a combination of constantly evolving that program, reinforcing it with enablement and training, and and accountability of your leaders, and making sure you're not too far behind where the technology is, where your policies are. Look, the same thing happened, what, fifteen, twenty years ago when touchscreen cell phones first came up and consumer technology, all the apps started coming up and everyone was way ahead and then I go to work. I'm like, well, I can order this on Amazon, like, Zippity's app, and I get it in two hours now. Like, why does it take so long to do this in my business? Right? So part of it is you can't just rest on what's how we've done it before. You gotta keep it moving and evolving your your plan. So go back to the deck like we outlined. It wasn't just about AI. It was, you know, the other elements. It's about connecting across the systems like Kevin was talking about. It's it's it's creating reports or custom reports that are fingerprint to your company out of, like, an analytics tool that, like, Ambers offers, but it services those larger volume or larger dollar volume or the high high quantity stuff that the behavioral fraud hides in and leaning in on that. That has nothing to do with AI. Right? So it's it's about building your case from the ground up and always reevaluating it. Like, we had, a session at our Emerson motion back in May that people asked, like, one of the questions we pull for is like, how often do you revisit your expense, you know, your program to make sure fraud isn't happening and your policy is tight? We had a third of the people say, like, only when an incident arises. Like, you gotta be more intentional on that too. Right? So, like I said, we can make this a full day session. I think Kevin and I can probably whip up a complete business plan for everybody. Yeah. Yeah. We're gonna we might have to do this again, guys. You guy, you guys are fabulous. I appreciate. You definitely delivered some amazing content here for our audience, and I hope they feel the same, way that I do. But, I apologize, guys, if we didn't get to your questions today. We will reach out, and do our best to answer any questions directly. I'll also be sure to share those, CPE certificates and a copy of this presentation with you all as well. So be do keep an eye out on your emails over the next couple days. We'll get everybody taken care of. But appreciate everyone joining today. Thank you so much, and do keep an eye out for our twenty twenty six webinars. We have some awesome things planned for next year. So thank you all. Enjoy your holidays, and we look forward to having you join us again here soon. Bye. Thanks. See if Gavin was phone, buddy. Yeah. That was good. Let's see. Do I leave the stage? Or will I Yeah. We just we leave leave, and we're done with the day, bud. Have a good rest of your day. Alright. Take take care. See you, Mike. Goodbye. Cheers.