5 ways AI empowers finance leaders of the future
Learn how AI fits into your record to report processes
The Office of the CFO has a tough job. As financial data grows in size and complexity, finance departments must evolve to understand new types of patterns, identify deviations in the business, and prioritize team resources on the highest areas of risk.
Watch this webinar to learn how artificial intelligence is transforming enterprise risk management into a future-focused function that puts data first. Structured around the record to report process, you will see real examples of how AI-based adaptive anomaly detection accelerates risk identification, eliminates number crunching, and helps your finance team address the right risks.
Corporate finance teams spend 80% of their time manually gathering, verifying, and consolidating data, leaving only 20% for analysis and decision making – Adaptive Insights
In this webinar, you will learn:
- How AI and machine learning identify risk in financial transactions
- Why adaptive anomaly detection improves your operations
- The benefits of AI for controllers, internal auditors, and CFOs
You will walk away prepared to make a decision on whether AI is right for your enterprise financial risk management.
Welcome, everyone. Thank you for joining us for today's webinar. My name is Patrice Belmont. I'm product marketing manager for corporate finance at MindBridge. Today we're presenting five ways AI empowers finance leaders of the future presented by our very own, John Colthart, SVP sales. Just a little bit about myself. I come to this from very long heritage and business analytics, specifically within the Office of Finance and Accounting. I've spent over 20 years, either being an implementation specialist, you know, a technical salesperson, helping companies choose the right solutions, also designing net new solutions for the office of finance. And I'm very happy to be MindBridge really working through the next generation of how AI is going to bring some amazing capabilities to the Office of Finance.
So, AI in finance, what's it all about? Well, we talk about the five ways it's going to impact you, and we're going to talk about this throughout this webinar. Capacity, coverage, controls, continuous monitoring, and close, kind of five C's that we see as we've been spending a lot of time in this industry. Myself, here at Mindbridge for the last three years, as we've been developing a variety of technologies, and to really kind of walk through, well, how did we get here? How did we move from point A to point B, you know, we need to kind of sort of talk a little bit about history in that buzzword, and we'll be able to do that in just one second here, there we go, how did we get there?
So, we got here over six decades ago, with this concept of AI. But really, as I said, now, the last sort of 5 or 6 years, some will say maybe 12 years, we’ve really seen this huge advent in us investing in artificial intelligence. And the problem with AI as a concept for a lot of you in finance is, there's a little bit of, you, know, it's a technology looking for a problem. And, I think that that's a very good way for you to be thinking, because that's historically where it's been. We spent a lot of time and energy in the technology field, where I've spent my last decade or so between IBM and MindBridge, really trying to understand how this technology can be applied. But I think we're on a really good trajectory now, in the last couple of years, as to exactly how it's going to work, and how it's going to work together.
You look at the technologies that you've been hearing about from maybe the last decade or so, it’s going to kind of follow along the traditional innovation theory, you know, humps, right where there's an early innovator, early adopters, to an early majority stage, all the way too late majority and laggards. And, really, depending on where your organization is, you may be using these technologies already. My guess is that a vast majority of you, or at least your organizations, will have lots of cloud technology. That might be cloud storage for, you know, priority documents, and for your content management structures. It may be your sales organization using customer relationship management tool. There may be ordering and online systems that you're using that are all cloud based. And maybe your technologies and, your services to your clients are actually all cloud based as well. Cloud has really helped us a lot in artificial intelligence, because it's really allowed us to create this massive scale.
I remember some of my first early projects, as I just know, as leaving the industry from a practitioner in the Office of Finance, working for a CFO and moving towards being a consultant. And one of our first projects was you know to create this massive environment, this massive data infrastructure to do analytics. And we used everything on premise. You know, we had to go and buy servers from you know, the brands of the day you know, HP or Compack and Dell and we loaded up with all sorts of software. And then we'd have to configure it all to talk to each other. And then we build front end user interfaces, you know, and it became this really challenging thing to aggressively respond to the requirements of the changing business landscape. I think we can all agree that. That's a really big driving factor, right, of why cloud became so important, because we could share that workload. We could actually make it more of an operating expense versus capital expense, and the reality is, our business is kept changing, and we kept needing to change the technology, and our technologists or IT infrastructure team just didn't have the bandwidth to support us, and finance.
Another one here that's disruptive. And, you've probably heard, maybe a little less about, but it's really important to optimizing, increasing that capacity within your office, or finance team members is this optical character recognition. And, you might be sitting there saying, I've never heard the term, OCR the acronym, I've never heard that the, the phraseology of optical character recognition. But, if you look at technologies that are out there today that are using this, it's allowing us to drive automation within payables and receivables within, you know, expense reporting cycles within the office of finance. So, it's a really critical piece of the puzzle. And as you can see, it's been more widely adopted then before now. Interestingly enough, it's all been basically just to automate routine. Where we're going to talk about later on is how it's going to be embedded into artificial intelligence scenarios.
You see artificial intelligence there, I put it in bold and you'll see that it's, it's really not even quite yet at early majority. And there's a lot of difference of opinion here. You know, how far down the curve, how far up the curve between early adopters and the early majority we are, specifically within the office of finance, and within finance practitioners and accountants around the world. I think that lot of smaller startup businesses are already using artificial intelligence capabilities without really knowing it. I think there has been a great advancement within our Enterprise Resource Planning tools or ERPs, the likes of Zero and QuickBooks Online and intact by Sage and even SAP's, you know, recently, got into this mix of embedding artificial intelligence to try to make your jobs easier.
Now, before that was, you know, type ahead and, that was like this idea of. Wow, it figured out that I wanted to put this in travel, but the reality is, is AI is becoming even bigger chatbots are coming into the mix. There are whole organizations building themselves around, around this, like, bot keeping, again. These are things that I'm going to talk about over the next little while, as we share our time together. Then there's Blockchain. And blockchain. I'm going to really leave at the end, really not talk a whole lot, though, We can, we can, you can go research that If you haven't heard the term, but a lot of what's going on in finance and blockchain is fairly limited at the moment. It's typically being used in more supply chain-oriented parts of your business. You know, folks like, like, Wal-Mart have built, and I'm very impressive blockchain, you know, use case. where they were having problems understanding where from farm to table of their products were, and if there was an issue that they might be potentially liable for, you, know, they didn't know how to connect with those customers. They didn't know who had bought you know, the lettuce or the cucumbers, or whatever it might be. And blockchain has been used to solve that problem. Which is really quite cool. Great use case. You know, definitely not going to go too much into details.
But let's really talk about why, you know, why are we in the state where AI is going to be so important to you as well? I think this is really important. Many people read or seen excerpts from the World Economic Forum, a foreign book, The Fourth Industrial Revolution. Now, C. Schwab wrote this book a little bit, you know, ahead of his time in some ways, and really talking about what's going to change between when they were writing the book in 2014 and 2015 through 2025. So, in 2012, sorry, a 10-year outlook. And if you think about it, we're halfway through that journey. So why is this so important? Well, it's because, you know, we're kind of at the tipping point, and this is just one example. It happens to be an example that's really important to MindBridge, but we'll kind of get to that a little bit later on, but really, what this is talking about is that our white collar jobs, you know, what traditionally has been chartered accountants certified public accountants, you know, ACCA members, ACAT members around the world, wherever you're from. You know, it's, it's been less used in technology as a whole has been less used in our professions. Now, we've always had technology, but we've never really empowered ourselves to really use it and the problem with the white-collar jobs is everyone fears that AI is going to, you know, totally disrupt and displace their world. In this book specifically, everyone that they surveyed said that 75% of them expected to be at the tipping point where there's a belief that, you know, more and more of that traditional internal audit, corporate finance function, control, type jobs are going to go away. If you look at this book that seven out of the top, sorry, 4 out at the top 11 jobs listed on redundant jobs by 2025 are things like payroll clerk, bookkeeping, clerk, accountants in their traditional form, inside of the business. And so, you know, we really need to understand what that means to us and we really need to dig in.
One of the biggest impacts that I can see AI helping us with is obviously the impact on capacity. And capacity is one of these things that, and as I said, I'm a bit of an older guy, I've got white hair. You know, I still feel I'm about 20 years old. But the reality is as I've been in finance and accounting organizations around the world and this capacity issue always comes up, and Patrice always reminds me that this is the common thread of, you know, do more with less. You know, as financial executives, as, as folks that are the stewards of fiscal responsibility in a business, we're always seem to be the first ones that ask ourselves, do we really need that? You know, do we need to spend our money here? How can we optimize it? And we also are the ones that typically don't, ask more and more for resources. Whether they be headcount resources and human capital resources, you know, technology or spend on other things, because we're always worried that we're going to be seen as not helping the business grow.
The reality is, I think, that we're starting to see is that these digital technologies are going to reshape the finance function. And we just asked you about this, right, where, what, what are you thinking of? And we've seen some pretty radical ways that AI is going to impact these businesses. A few of you will have heard that term around robotic process automation, at first little image, from the McKinsey study. The reality is, is RPA is, radically transforming how people do their job? Now, in some ways, this is the scary part, some people have actually been removed from the business, in the function they were doing, and deployed differently. In some cases, yes, we do have the issue where these could actually be impacting us more negatively, and people are actually losing our jobs with RPA. I believe there'll be a balancing. So, if you are worried about this, you know, my urge you to learn and grow some new skills. But these are companies like, Bot Keeper. These are companies like: UI path. These are companies that are doing, you know, solving business problems like payables. How do we more effectively drive, you know, our payables process, more and more electronically? Even QuickBooks today, I don't know if you've seen the recent ads, but there's QuickBooks, you scan a receipt, QuickBooks tries to figure out where it needs to go.
So, you know, the interesting part about this is AI is going to change us in this area. In terms of capacity, because what happens when, you know, you take an organization that maybe has 10 payables clerks and, all of a sudden, have artificial intelligence and robotics process automation actually delivering, you know, all of that, paper and electronic paper, through the system, into the GL or into the sub ledger, and then into the GL. What do we do with those people's time? And, I'd say, again, you know, there's this issue of capacity, the end of the month, you know, they sort of always joke about, you, know, watching people as they're closing the books. So, I'm talking about, you know, the fourth C in our list, but, you know, people are trying to close their books. And I think about those folks and payables and receivables and, you know, they're busy trying to figure out accruals and they're busy trying to figure out, you know, do we have everything? And the reality is, is that AI is going to help us here. AI is also going to help us, you know, gain new insights because what's happening in your current world is, is your mechanically doing these facts. These pieces, where do we redeployed people? If we implement an RPI tool? Or sorry, and or RPA tool, Robotic Process automation tool, or we deploy something like Amazon, or Expensify, or concur to manage our whole expense management process. And it just automatically posts to the GL. Well, what you're going to be able to do is now more effectively spot check or analyze if there is an anomaly in those expenses, in those vendor payables, in those customer buying behaviors, which may be a positive or negative impact to your business. If it’s positive, then you're going to be asking yourself, well, how can we do more of that? And if it's negative, how do you stop it and stem it, right? One of the things that's interesting on capacity that we talk about when we're out in the marketplace is also this whole issue of employee recruitment and retention.
Now, again, I think technology can be a great advancer of lots of things. You know, just taking out the mechanics of what we do and allow us to be the you know, the thought leader, thoughtful resources the knowledge workers that some of these industry analysts talk about. But I actually think it's also really important for us to get more people out of school into corporate finance type roles and I think we've been lacking that, in fact, The AICPA So, the obviously the association for chartered accountants in the US. has been seeing a drastic drop year over year of folks that leave their schooling actually go into the accountancy profession. The same holds true for corporate finance because a lot of those people are accountants, right?
A lot of the people that first start, including my first job as I was finishing school, was all about training and learning to be an accountant. And the reality is, we need new tools to excite, you know, the new generation of people coming out of school. You know, they don't want to go to the old Xerox machine. I know you're all laughing, because I said Xerox. But we don't want to take a ledger book to the Xerox machine and copy it and write a bunch of stuff anymore. And we've gotten way past that, but now, AI is going to actually drive us even further, which is really quite exciting to me. And I think that this is where, you know, we become part of that valued, trusted system within the business, where I believe the officers CFO has to set. So, a little bit on capacity. There are some very specific things that I mentioned on how we get there, and some tools that you might want to wait, might want to think about and look at, that can help you there. The other issue that is kind of a bit of a sister to this or something that as a result of not having capacity, is that you end up in this cycle of having limited coverage.
Now, what do I mean by that? And I'm going to use an audit example in my pictorial here but think about all the business processes. You haven't finance. Think about where you're at with them. What you do with them and imagined to yourself, how do I get a scope of my business? If I'm spending, you know, 80 to 90% of my time doing manual data entry or manual movement of data, what ends up happening in an audit process or you know, continuous controls, you know, review process is you end up having to do some very basic rules to and sampling to try to figure out if there's anything wrong that leads to a lower level of coverage that leads to not detecting errors.
The reality is most fraud cases, and I don't normally like using that big F word. But the fraud cases that we've heard of we've seen, and we've participated in, in terms of our technology being used to, you know, uncover some of the signature patterns from the employees that have perpetrated it. You know, the issue has almost always come down to limited human capacity, not able to see everything. And the real risk is, the moment you see something that is meaningful, it's highly possible that a million small things have already happened. So, case in point one of our public accounting firms, or one of our professional services firms out on the West Coast, was using our solution in a litigation case, and, you know, the scope basically went from, you know, about half a million dollars exposure, or the company thought there was a half million dollars exposure to actually being closer to one point eight million. Now, on $100 billion business annually, that's a significant amount of their revenue, now being no siphoned off and funneled out to private individuals in an incorrect way.
This is the type of issue that you have when you can't get to that level of coverage. The time you see it, you think you've got a handle on it. The reality is, there's a million things going on. Now, talking about the, you know, sort of the next piece of this, in terms of, where we are. It also comes to the controls and one of the challenges that we see, and we talk about in terms of where AI is going to help you here. And some of this is in reality, and some of this is ethereal. And some of it somewhere in between. Even organizations like ourselves are still working on these problems. But trying to understand the golden thread of that data, when you think about our third C here, being about controls and about making sure that you, you understand, how the business is actually being maintained, and monitored. The reality is, is controls get in our way because there's so many things that happen to a transaction, as it goes through the funnel, to, posting to your, quarterly or monthly reports, right. Lot of teams spend a lot of time, almost too much time, rolling up numbers from various sub systems, you know. And that might be an invoice subsystem. It might be a contractual subsystem. It might be the vendor payable system. It could be your payroll system. And you know, I kind of keep waving out into the into the expanse here of all the things and tools that are on that thread of something, hitting your GL, which you then use to manage your financials. And the question is how you appropriately track the controls.
We've seen cases where in a traditional way, people are trying to manage them. You know, they made sure that an SAP, you know, person A was set up with the right level of controls for that operating entity. We've also seen them where that same person has also got controls at the, at the group level. You know, so, maybe, something where there's a consolidated set of entry entities. Well, it's very easy for things to move from the individual operating entity up to the group level, and then have it changed at the group level. And this particular case that I'm thinking of, and where AI would have been able to identify these issues much earlier, there was actually, again, frauds being perpetrated because at the at the entity level, right, at that individual operating unit level, all the controls were 100% right.
They know the person that had access to pay the vendors. You know, off they went, they did all their job, they put in all the right pieces. But the way this company was structured is all payments actually went out of the, out of the operating entity level, or, sorry, out of the group level, because it was all based on, you know, cash management, while the same person that was doing data entry for that individual operating entity. Also had the ability to access certain payment profiles, taking them side-by-side without, or, sorry, you know, in these two systems, in those silos, without seeing them, you may not have realized that, that posed a problem. You bring them together all of a sudden you do.
So now the interesting thing about, about controls is that, you know, they're all rules based today. Almost every single ERP uses some type of rules and constraint-based processes, but what happens when you get more and more new technology coming in new subsystems coming in? I don't believe that, without AI will ever be able to, going back to the first point around capacity, going to the second point, the second C around coverage. I don't think we'll ever get a really great picture of controls and be able to see this golden thread without artificial intelligence. And, as I said, some of its working today, you know, in our system as a great example. You have the ability to load in all the different user profiles and user lists on all different pieces of sub ledgers and ledger. You can then run a number of natural language queries that says, hey Sam, you know, has Sam posted any transaction over 10,000. You know, and if they did, what was the trail of that? You can have those types of conversations with your data with, with these upgraded tools. But, where we need to go and be able to pull all these pieces together and actually understand that, you know, there may have been a reason why that, why, that happens. Now, when we look at, you know, the next C, it's continuous monitoring, and I kind of have a funny image that I like to talk about, when we talk about this, and how AI is going to help, you know, the first, the first three, right? Capacity, coverage controls.
You're probably starting to understand, there's a, there's a bit of, a, these are all inter-connected, and they quite literally, are. But this whole idea of continuous monitoring has been almost impossible. You know, I've worked on business analytics solutions for quite some time, and the issue that I normally see is that we typically in finance, start thinking about, you know, actual to plan or actual to forecast, or do a 12 month trailing and see how we're doing. And that level of aggregation of data, you know, looking at it from, you know, quite literally a historical standpoint, kind of creates the side mirror, you know, sort of perspective, and the issue is, is that, you know, you're, basically just doing compliance internally, right? Your continuous monitoring is just about, hey, did we check these boxes? Did we take all the right things off? Yes or no?
And unfortunately, if you look at the world economy in the last 3 or 4 years, and you look at all these major entities that have gone under. Whether it be possessory, Valerie, just being extremely extended, because there were no controls. You look at Thomas Cook Travel, where there are external auditors, didn't give it a clean bill of health, but actually didn't really raise a red flag to the investment community, that there was something more at risk here, and then they didn't do any continuous monitoring. You know, you end up in that situation, where you're really just trying to get through compliance checklist. You're trying to submit those quarterly or annual reports, and the reality is you're looking at a kind of a hazy, you know, kind of rear view, mirror site, mirror concept, Whereas if you start thinking about artificial intelligence and using, so a subdiscipline within artificial intelligence, is statistical modeling. And, more importantly, looking at things like predictive modeling.
What this allows you to do, is, it allows you to actually see the trends and see where things are going to be at, allows you to very more effectively check and see those trends over time, and see sort of where we get to. And I think that, you know, from my perspective, I think that's one of those big things that, you know, is sort of really key to leveraging AI in its most effective way. Can we get to this right-hand side of the screen where we're, you know, we're using ways or maps or whatever model you use to get from point A to point B and we do that with our financials, and can we do that within our businesses. And, again, I always look at it as, can we become really trusted partners to the business in order to help make those changes in order to help drive different behaviors in, in where we're going to get to, by helping each department understand the impact they have to financials.
And, I go back to the first three right capacity coverage controls. And now continuous, you know, being the fourth of our c’s, can we actually, you know, be continuously updating? Can we be like ways in finance? And actually, understand that, you know, some sales throughout, out in the field has signed a deal for a great amount of money, but put payment terms that we didn't expect? And all of a sudden, we now have a cash flow issue, where the IT person, at the exact same moment in time just signed up for a new cloud service, where they needed to do all the payment upfront. I mean, these are the types of things that AI is going to be able to bring insights the forefront of, and start being very proactive in our solution. We basically give you the absolute freedom to create as many, you know, key performance indicators, or financial metrics that you want. And the system will naturally, every time you load data, whether it be monthly or quarterly, it will, literally, run through all of its predictive algorithms, and show you how it thinks the year is going to unfold. And it will start identifying those anomalies where there may be key ratios that are off.
Don't think AI is quite yet at the place, where we can actually be prescriptive to tell you exactly how to fix it. But I think we're getting closer and closer to identifying a little bit of the how or the why. So, I think that's kind of like, you know, a great place in a position to be at, if we can get there. And I think we're a lot closer than anyone really things. Now we'll get to my last slide on the five C's and this one is all about the close process. And you know we were having a really hard time trying to give you a visual to this. and so, I'm going to share it and talk about what this really means. It's a little bit of what we've just all been talking about. What I've been sharing about, my thoughts on, on where I think we are. That the reality as I see it, is, we're in a position where the data that we're trying to use to close our books is becoming further and further, you know, away from the nucleus of what's this 10% structured data is.
So, you know, I kind of mentioned this as the, you know, as the nucleus of chaos. You know, we're sort of, in this world, we have today with our current ledger systems. And ERP is whether big or small, right The, you know, the sort of mid-market here, at Microsoft Dynamics of the world, the sages of the world, all the way up to the You know, what? What's always been classified as the Enterprise class, Oracle's SAP's? The reality is, is that, we only get about 10% structured around seeing that data. So that's everything that's hitting the ERP and the various sub ledgers. But we don't see is all those pieces that come around it. So, what ends up happening in the closed process for a lot of you is, you spend an awful lot of time sort of imagining where you think you're going to be. As I mentioned, I think a little bit earlier on, on the webinar, you know, the idea of accrual based, you know, accounting and trying to get it right, based on what you think, where you think you're going to be. Without AI. You're sort of kind of stabbing in the dark, because you really don't see what's happening out in the tendrils of your or tentacles of your business.
And what we need to get to be we need to get to a close process that is less frenetic, and chaotic, around just trying to get data in and really be thoughtful about, is that data going in the right way? And I think that there's a huge opportunity for AI to really support you. In moving us past this, this current, you know, mindset of, I'm just going to roll everything up. I'm going to see how we're doing and you know, I'll plug a couple of accruals if I think we need to be higher and then we'll, you know we'll provide new guidance out to our stakeholders, management, you know, whoever that might be. And I think we need to use AI to really start getting us back on a path of being able to gather all that unstructured data. We have, over here at MindBridge, we've been working on programs and processes to really extrapolate down to the individual documents. And you go back to one of those first slides I shared with the different types of technology, right.
Where cloud based, we use OCR in this particular case, when we're looking at the close process and all the documents that we need to be thinking about. And, you know, how do you tie that altogether to the structured data and identify the anomalies? Because really, what we're talking about from our lens, you know, in terms of, of AI and helping finances, is to really let the data tell the story. Right? It's giving you the capacity to really look at those insights. Which has been, you know, able to cover, you know, more and more of your data. Which identifies potential gaps and controls, which allows you to be more continuous. And now, with this last piece, you know, also thinking about in terms of enhancing the close, and I think these are all inter-connected pieces, but it all comes down to, you know, the slide that I shared a lot earlier, which is, you know, we really are at this this moment, you know, in the middle of a point in time, where we're going to be at the tipping point, where AI is going to impact every single industry. If it's not already, AI will impact every single profession. But there is a prevailing thought that unless as finance and accounting individuals, if we don't get on this train and start using tools and products to solve the problems that we're experiencing right, in those five C's, it's going to be harder and harder for us to move forward.
So, I think that there's, there's a lot of really interesting concepts going on there, and I wanted to share a very specific use case around MindBridge, because I think this will start putting it a little bit more into focus for you. So, what did this organization look like? Well, let's give you a bit of, of background to that. We work with a lot of, of multinationals, we work with a lot of, you know, sole proprietor type businesses and in our ecosystem. So, I'm going to give you something that's a bit gnarly a bit complex, maybe bigger than some of you are, or maybe very, very representative. But the reality is, as an organization that has a lot of different footprints, they've got think of it like retail versus commercial. They have unbelievable amounts of transactional data going day after day through their system they have multiple different backbone systems rolling into their multiple ERP’s, it's the type of industry that is impacted based on all sorts of things. So, when the you know, when people are confident in their in their futures, they spend more, you know, they do more, right? This is the type of business that's impact of this. And, you know, of course, because they're in this, you know, both sort of like a retail orientation, or a consumer base orientation, as well as a business to business type orientation. It just adds a whole bunch of complexity to their, to their organization.
And what they were, what they were worried about, is compliance, of course. You know, they're in a regulated industry, they, know, they have to do certain levels of reporting, they to make sure those are all, they also were struggling, and this, this happens to be within the, office finance, within their internal audit functions that support the office of finance. They were really struggling with trying to build value, right? They were really just ticking and tying different data elements. Trying to give the appearance that all risk has been mitigated and, you know, it came back to the capacity and coverage issue where they just didn't have the resources.
So, they came to us with a problem of, hey, we're in this regulated industry, we need to get better and sharper it, you know, our products and offerings being, you know, well, use, do we have any gaps in our finances? What can you do to help us? We have almost no resources, and we're being seen a little bit, like a cost center, think most of you can probably know, attest to understanding what I'm talking about. And what we wanted to do for them was really identify a bunch of things. Like, what are the things they really wanted to know? Well, in their retail side of their, their business, that business consumer side, they want us to know, were there any branches of their organization that were higher risk. If they did have higher risk branches, could they dig in and find out why, you know, what was causing that level of risk? They wanted to make sure they use their resources well.
You know, I was working with a bunch of other firms, but one of which was, you know, kind of a funny situation. They would plan their audit, you know, literally 12 to 18 months before it happened. And I kind of asked them. I said, well, you know, how do you analyze the data and inform yourself as to where you're going to go and spend those resources? While we, we just pick it based on whether we've done it in the last little while, you know, like, think of how crazy this might sound, but, you know, pick country X, entity. Why? We haven't seen them in 12 months. We're going to put them in next year's plan, you know, in the diving world. If any of you are divers, you know, there's this concept of; plan the dive, dive, the plan. And the reality is, is when you're diving, you're doing anything underwater. That's really critical, right? To plan the dive. Well, to make sure that you dive the plan, right, so that, you're making sure that at all points in time. All the safeguards and all the checks are being done so that you can come back to the surface and still be alive. The reality is, I think, you know, in this particular business's case, they just really had no idea. Like, what a good use of their time was. They didn't really know how to plan. So, in the absence of that, they were no kind of just making choices, right, X, or Y, factor, then we're going to go audit without any real intelligence, and they also weren't able to get the insights.
So, what we provide, and there's a couple of screenshots of similar type of information that we're providing to them. But we showed them which businesses were at higher risk, which areas, you know, needed better resourcing, etc. So, we were able to really kind of get them into a great position of being able to now see all their branches, see all, their internal resources, how they were being deployed, whether there was potential risk. If there is risk, what did that risk will look like? And now, they're able to, you know, generate these reports are more repeatable basis, which is really important to them.
So, they mitigated their overall risk exposure and risk of compliance. They were able to reduce the overall cost of doing unnecessary, you know, audits or processes and they were able to really drive guidance into the business of, here’s how things are going and here's how they are.
And I realized that for some of you know, there's been a lot of words. A lot of screens come through, but I actually want to kind of bring it home to something that's really important for you as your business. And, to me, this is about the fact that it's a cultural change, as much as anything. And everyone continues to, you know, write these very wonderful studies. Some, not so wonderful, But, most of them. wonderful studies around, where the capabilities could take us, where these use cases may be, but, again, I try to remind everyone that, AI, you know, you're just saying, I'm going to go and hire five data scientists, which I saw an article earlier this week. You know, and I'm going to go and give them things to do and they're going to go find things you know, I think you're going to find that you'll have different levels of success, and, I think it's because it's cultural.
I think there's a, there's a lot of evidence to that effect, and obviously, Harvard Business Review is quite well respected in this regard in terms of their research and their data. But when we read through this and we hear our customers, it really is kind of important to think about the fact that your culture is a business is going to naturally change. Change is always hard. Any technological changes made harder by people fearing for current livelihood, fear that it's not actually going to impact them the way they want to know. It's funny, just a little sidebar, I took a few years at IBM to work in the design organization. And one of the best tools that I found out of that, and the design thinking methodologies that I took away was, you know, you could almost start every project with a bit of a hopes and fears exercise to guide your organization. Because what's happening is anytime you embark on something new, whether it be selling new products and all the products or services. Whether it's, you know, thinking about, you know, introducing yourself into a new market where these, whether it's about just fundamentally, you know, checking in and having technology help with something. There's always this, this pent-up anxiety because it's change. And AI, therefore, is, is squarely in this cultural change element. And so, going from siloed work to interdisciplinary collaborations is really hard for a lot of organizations initially, until they start getting some successes. And because AI is really necessary to have multiple points of input. It's really hard to make this a siloed you know, organization, and I saw a great win, unfortunately, the name of the firm escapes me. But what they did is they had a number of, of data scientists come in and they had a problem. And they said to the data scientists, Yes. So, can you help us go figure that out? And these two individuals, So, they had already these two data scientists. The two data scientists, when they were writing in Python and R, and my guess, is some of you on the phone are actually already exploring, you know, learning how to write your own data science. Because that's one of the, you know, the prevailing studies is that, anybody that's in finance and accounting should learn how to program. I'm not saying yes or no to that, but it was interesting. This organization over six months was able to get to, you know, you know, a valid way to assess that data and move it through. But what was interesting in the study is it actually talked about this first point, it's interdisciplinary.
There's almost no organization that can hire an outside firm that does AI, from a consultancy perspective, you know, brings a toolkit and says, OK, what do you want to do? No one that will hire, you know, high-end data scientists who won't have to learn to fail fast. Because what ends up happening is, we get stuck in this, you know, Well, my work is this, and I got to go do this. You know, we actually need to move yourself out of that mindset and start working on inter disciplinary collaboration, actually bringing in those data scientists who typically will have no high degree of maths and sciences, no qualifications, but have may never been in your industry before. You have to pair them with people inside your business, inside, inside those knowledge workers that you already have, and through that partnership, you're going to make AI a much stronger output.
The other thing is you need to go from experience based, you know, the leader decision driven decision making. They used to call this gut feel. You need to go away from the, you know, the gut feel, Well, I think we need to target over there and actually find ways to become data driven. And this is really, really hard because you've actually got to push it all the way down into the teams. And, so, when you think about your organization, I think one of the areas, I think it was 67% in the last poll, you know, said that they were looking at AI for internal audit. It's fantastic to see that, partially because it's, you know, things that I think we can help you with from a MindBridge perspective, but it's also frontline people, right?
Most internal audit functions have a lot of frontline people trying to figure out what's going on, being asked a whole bunch of questions. Well, what if you could target those questions, right? What if your internal audit team could say, Okay, right? We just looked at all the customers. We just looked at all, the contracts with those customers and we've identified these 10 gap areas in these four customers, in these four regions. That's a way more productive way of letting your team go and work on those issues. Maybe find good things, maybe find bad things, but they can be out there in the frontline versus, you know, having, what we've typically had is all the analysis being rolled up and everyone's saying, OK, right. We've got a problem with this product. You know. Maybe this product in this country or this region, but are you really getting down into actually, it's the type of deal we did with that customer that cause that problem? The answer is probably not. So, trying to help your teams get data driven down at the lower end, AI can help with that, because a lot of your staff and talent are not going to be those data scientists that have, you know, computer engineering degrees, you know, PHD math degrees, et cetera. They're just not that type of person, right there, business folks, who have, know, done really great at what they've done, they need some tooling to help them get past there. And I think AI tools, you know, that solve problems, are going to be one thing to go out. And then, finally, sorry, I'm spending so much time on the slide that I get really excited about cultural changes, As you can probably tell by the animation of my voice, but is also to go from these the sort of risk averse, rigid into agile, experimental, and adapt, and adaptable.
And I think, you know, given the day we're on and the current climate we have around the world, I think there's this common gut reaction for people to retrench. Right, for people to go from, you know, investing and, you know, innovating and changing into, OK, I just want to make sure that I can pay my employees next week and that I totally get. I think all of us should be thinking about that. But I think this is this last point is really around how can you actually help your business also take technology that is becoming more and more mainstream that is solving certain problems. And actually, deploy it to help make yourselves a little bit more agile and a little bit more adoptable by giving them more and more tools. So, all these three things come together to really say that it's a, you know, it really is a cultural change in a shift.
And, you know, I'd be remiss if I didn't say about the fact that, you know, part of the problems that we're experiencing with artificial intelligence as a concept being used in the finance organizations and around the world, in businesses In general is, we're actually, you know, we're actually challenged with a bunch of things all at the same time, and there's a very strong belief that this year, or early into next year, is going to be where we actually reached that inflection point. And I think, you know, we would say that in certain types of problems that we solve. Right, finding the anomalous behavior in your vendors, your customers, you know, looking at the financial audit side of your internal audit, I think we do a really great job of identifying where we see the, you know, potential insights are.
Again, both positive and negative. But it takes a lot to get there, and it needs a tool and solution that is likely to be artificial intelligence based. That is born on the cloud, and that is updated to the most current, you know, standards, and, in fact, pushing past those standards. Of what people used to do, because those barriers are, people don't know how to do it, right. You need ease of use, you need to be able to match the technology with business professionals and potentially, your own data scientists all the same time, right?
That cultural business that, you know, moving out of silos of everyone, doing, spending for themselves, and then, you know, the other barriers that we've really kind of gone past, right? The whole, you know, computing costs bit mean, if you're not thinking about cloud or private cloud, I think your kind of missing and create opportunity to drive your business. And, that's one of those things. So, when we are in this wave of replacement, we will be moving past old. You know, even when I started in MindBridge, what it was the first thing I did, I built a spreadsheet, I built a spreadsheet to give me some clarification of where we thought we were, based on the data that we had available.
Now, we use AI based technologies inside even my sales organization, to drive us to better outcomes, but the reality is, we got to move past that. So how do I see us supporting you, and how do I see yourself supporting each other, in terms of advancing the whole concept of AI is, you know, think about these types of, of challenges and business problems, and then know that there are tools and techniques out there that will help you get there. I always firmly believe that the CFOs office should be the first and foremost point for investment, for maintaining that trusted advisor status. You know, you may also use an external accounting professional to help you with that, who are also trusted advisors. But the reality is, the CFOs office should always have a heartbeat on everything that's happening in a business. And preferably, you're able to go out and work with those business professionals to help them understand, you know, how they're impacting the overall company performance, which naturally always comes down to dollars and cents. I hate to say it, but even if you're a not for-profit, it's all about the dollars and cents at the end of the day. and that financial health.
One of the big concerns in the industry right now, based on the IAEA's studies. So, the, the Institute of Internal Auditors Study and the Association of Certified Fraud Examiner Studies is that it's taking longer and longer to identify fraud over the last decade. And it's longer and longer to resolve it. And there's bigger and bigger dollars being extracted from your businesses. I mean, I explained the one out in California where there was litigation, we were helping a professional services firm really identified the signature, and we found it much, almost 3.5 times bigger scope than the company was originally worried about.
But fraud is a really big problem, and in challenging times it manifests differently. Lots of people will potentially make mistakes, because they might be new to this. But the reality of all the studies is, actually, the really big problem is, you know, highly educated long serving employees who perpetrate that type of thing. Anyways not to belabor this, there's also a bunch of company events that, that may end up happening right over the, over the course of the next little while. As we see how the world economic changes and I just think that, you know, getting insights to your business today is probably better than not. And I think that the way to get there is to use, you know, tools that that hopefully do actually get you there a little bit more effectively.
So, if I wrap up and then Patrice, maybe you've got a couple of questions. I'd say that it really comes down to, you know, AI is right now, best suited for helping to identify different things, insight's positive and negative. And it's really designed to support you and augment you, but it's really not designed to, you know, be the, you know, the all-knowing all, seeing crystal ball that's going to help you manage your business. So, you have to kind of embrace that, as it's a piece of the puzzle. And your people are another piece of that puzzle, and coming together and being able to use these tools, You know, you'll be able to, to drive further and further enhancements in your capacity, your coverage, You know, increase your level of controls through continuous monitoring and hopefully close your books a lot faster with a lot more confidence. Patrice anything come up. Yes, we do have questions, but to be respectful of everyone's time, I think we'll go with one, and the others will be answered individually. So, we have Richard asking, you know, he's an accountant in an SME. He’s telling us that his team is spending way too much time on monthly and quarterly close procedures, and you'd like to know, you know, about the time savings and maybe other benefits that a solution like ours could bring to his organization.
Yeah, it's a really a great one, I think. There's a couple of things. I mean there's a lot of pieces of the puzzle that go into a closing the books, right? So, there's reconciliations there's making sure all the data's actually process. You know, invoices, customer orders, have been sent out, payments, all of these things. So I think that when I look at how MindBridge works and the things that we're able to do is we're able to spot Early on challenges with where things may be posted with types of transactions that may be you know of concern and you really want to hit those before the books close if you can.
So, what we've seen is customers have gone from there’s an owner operator of hotels that we work with. You know they've seen really drastic reductions in the ability to the amount of data. they've been able to process before. Saw an increase in capacity because they were, you know, if they had a few hundred hotels, that, they wanted to do a month you know, pre close audit on, you know, just to make sure everything was fine. I mean, it would literally take them days, and now they can run data in hours. So, just having that visibility. They've actually found some really interesting things that have helped their business be more profitable, but I think in the closed process in small and medium enterprises, it's, interesting, It doesn't need, you don't need to spend, you know, hundreds of thousands or millions of dollars to get AI to do some of these health checks. I think you can kind of just think about thinking about what we do, Patrice. You know, you could run your entire vendor, you know, payables, all of your GL probably in the matter of an hour or two. And you'll be seeing some new insights, which will help you guide, know where you spend your time to make sure you're right on your close, and then as you get to report, you know, one of the things that we automatically do, is we rebuild your, your income statement and balance sheet based on the transactions that are created.
So, you actually get to validate that what you're reporting to your stakeholders is accurate. And there's been a tool that's, you know, kind of re validated, if you will, that all those natural roll ups of data are, are occurring in your business. So, I think it comes down to increased capacity and coverage, and making sure that, you know, you can interrogate the data beforehand.
John, anything else you wanted to add before we wrap up. I don't think there's anything other than, you know, kind of, be brave out there, As I talked about, you know, it being cultural. I think, you know, the, the ability for you to change your business, and to enhance it with AI, will come down to identifying some problems you're having. And then asking questions of vendors like ourselves. You know, can we solve those problems? Is better than just saying, I'm going to go off and build a data science program internally. Look for the problems, and then see if there are solutions on the market that can help you. Because in today's climate, we're probably going to have to be a lot more agile and brave with how we spend our money. Great. Well, thank you, everyone. We appreciate all of you sharing this hour with us, do not hesitate to contact us to continue this discussion. Thanks again, and we hope to see you next time.