How AI Could Have Saved Macy's $150M and Prevented Financial Disasters – Ahikam Kaufman
In this episode of Future Finance, hosts Paul Barnhurst (aka The FP&A Guy) and Glenn Hopper welcome Ahikam Kaufman, Co-Founder and CEO of Safebooks AI. They dig into what it really takes to run finance at a fast-growing global company. From building systems that can handle international payroll to staying on top of ever-changing rules and regulations, Ahikam shares a behind-the-scenes look at how finance can be a key driver of growth, not just a support function.
Ahikam Kaufman is the Co-Founder and CEO of Safebooks AI, a platform focused on ensuring the integrity and accuracy of financial data for organizations. With a strong track record in leadership roles across finance and technology, Ahikam brings deep insight into the challenges companies face in managing financial data at scale. Prior to Safebooks AI, he held key positions in companies like Buildup and ScanModul, giving him a well-rounded view of both startup growth and enterprise needs.
In this episode, you will discover:
Why paying people in different countries is way more complicated than it sounds
How finance teams can shape a company’s strategy-not just track expenses
What makes Papaya Global stand out in the crowded HR and payroll space
How to deal with the endless maze of international tax and labor laws
Advice for finance pros who want to grow their careers in a changing world
Ahikam Kaufman shared practical insights on how messy and unreliable financial data can hinder companies and what it really takes to fix that. Drawing from his experience as Co-Founder and CEO of Safebooks AI, he offered a grounded view on why visibility, structure, and accountability in financial operations are crucial for any organization. He emphasized that success starts with clean data and clear systems, not just better dashboards. At the heart of it all: discipline, attention to detail, and building tools that make finance teams stronger.
Follow Ahikam:
LinkedIn - https://www.linkedin.com/in/ahikam-kaufman-688310/
Website - https://safebooks.ai/
Join hosts Glenn and Paul as they unravel the complexities of AI in finance:
Follow Glenn:
LinkedIn: https://www.linkedin.com/in/gbhopperiii
Follow Paul:
LinkedIn - https://www.linkedin.com/in/thefpandaguy
Follow QFlow.AI:
Website - https://bit.ly/4fYK9vY
Future Finance is sponsored by QFlow.ai, the strategic finance platform solving the toughest part of planning and analysis: B2B revenue. Align sales, marketing, and finance, speed up decision-making, and lock in accountability with QFlow.ai.
Stay tuned for a deeper understanding of how AI is shaping the future of finance and what it means for businesses and individuals alike.
In Today’s Episode:
[01:53] - Welcome to the episode
[02:19] - SXSW and Startup Energy
[02:53] - Transitioning from Corporate to Startup
[03:46] - The Importance of a Strong Foundation
[05:40] - Replacing Manual Financial Controls
[08:03] - AI-Powered Financial Graphs at SafeBooks
[17:10] - Ensuring Compliance with SafeBooks
[22:49] - AI’s Impact on CFOs
[26:57] - AI-Powered Priority Alerting
[32:26] - Fintech’s Biggest Problem
Full Show Transcript:
[00:01:53] Host 2: Glenn Hopper: Welcome to future finance. Today we have Ahikam Kaufman, co-founder and CEO of safe Books I. The world's first AI powered financial data governance platform. With over 20 years in corporate finance, Ecom has played key roles at major firms like Intuit and successfully guided startups to acquisitions. Icon. Welcome to the show.
[00:02:16] Guest: Ahikam Kaufman: Hey, thank you so much for having me.
[00:02:19] Host 2: Glenn Hopper: You're at South by Southwest this week. How's that going?
[00:02:22] Guest: Ahikam Kaufman: It's an amazing festival. You know, sometimes you go, uh, you go to tech conferences, you hear, you see a variety of ages and a variety of people and personas. So I think the fact that they combine, uh, the music and the film festival makes it a very, very interesting and colorful event. So, uh, the streets are full and it feels like one big festival. So it's very exciting.
[00:02:48] Host 2: Glenn Hopper: That's great. Are you, um, speaking on any panels there or what's your agenda like?
[00:02:53] Guest: Ahikam Kaufman: We were on stage for a pitch. Uh, they selected a few companies to kind of pitch the audience, and that's what we did. So I came over here with our COO, Steve Selvig, and, uh, yeah, he actually did a pitch.
[00:03:10] Host 2: Glenn Hopper: Cool. Great. Well, you'll have to be sure and get by. Uh, what is it? Perla's. The famous seafood place in Austin. They're right across the, uh, right across the river. That'll be a good stop for you while you're there. If they're there. Probably so packed right now, I don't know if you'd even be able to get in.
[00:03:22] Guest: Ahikam Kaufman: Yeah, everything is very packed. Yeah, actually, traffic at midnight looks like, uh, midday. Yeah.
[00:03:29] Host: Paul Barnhurst: So it's like downtown LA or New York?
[00:03:32] Guest: Ahikam Kaufman: No. Totally. Yeah. Yeah, yeah.
[00:03:34] Host: Paul Barnhurst: I still remember the first time going through downtown LA at 1: 30. It's Friday or Saturday. I'm like, and it's still rush hour. What's wrong with this picture?
[00:03:43] Guest: Ahikam Kaufman: There's actually traffic at midnight. Yeah.
[00:03:46] Host: Paul Barnhurst: Well, enjoy the rest of the conference there. Hopefully you get to enjoy a little bit of music and have some fun. Not just the presentations, but want to jump in and ask you a question here. Safe books If you founded the company a little bit over two years ago. Maybe you start by just telling our audience, you know why? Why you founded the company, what you do. Just a little bit about it.
[00:04:07] Guest: Ahikam Kaufman: You know, I started my career as a finance guy. I had a privilege to work in the fallout companies. The first one was a company called Mercury Interactive, where it was a very super successful software company. Eventually we got acquired by Hewlett-Packard for $5 billion. But in the process, we got into a compliance issue. And I've seen the dark side of corporate life. Of course, I personally was not involved, but the company had several issues and worked its way out of these issues with the SEC. And, uh, then I became an entrepreneur and I did a bunch of things. We actually did a B2C company, which was fun. Uh, in the payments world. But I always dealt with compliance even when I was in fintech and payments. It's all about compliance. At some point, I thought, and we I continue to use it until today. So there was like an inherent gap between accountants and what they do on a daily basis and their ability to actually touch the foundation layer, the foundation of finance, which is the data. So if you think about that, companies today, they process a lot of data in across disparate systems, in many systems, whether it's like a, you know, billing, ERP, CRM, spend management, payroll, HR. So when you try to manage finance, you're dependent on a lot of systems which essentially process pieces of the same transactions even.
[00:05:40] Guest: Ahikam Kaufman: And it's your requirement as a finance organization, we call it internal controls to be able to monitor and govern the data. But there's a gap because you're only an accountant, right. So you can deal with transactions, but to deal with the data itself. So I thought there was walls, like this gap that always existed. And people used to solve it by pulling reports, doing a lot of manual work, putting it on spreadsheets. And basically you need to document how you check the data. And I thought that today using AI and a lot of recent technology, we can actually automate that work and allow people to focus on the really important things which are like, you know, how do you remediate the data if you have to? How do you make all kinds of decisions, whether they're accounting or operational decisions, and not just monitor the data? So monitoring the data, which is a concept who's been around for 20 years in DevOps, in cyber, in it didn't get to finance. So what we're doing is literally replacing manual work. Manual specific repetitive work around what we call is financial data governance.
[00:06:51] Host 2: Glenn Hopper: And what you guys are doing. I mean, it's kind of the holy grail. I've been talking to the office of the CFO about AI and finance for several years now. And I mean, with the real time data monitoring, automated reconciliations, your fraud detection mechanisms, and then integrating directly, um, with ERPs, CRMs and all that. I mean, this is you're addressing problems I've been trying to solve as a consultant and as a former CFO for years. And I'm, you know, I think that right now, kind of in the run up and hype, the hype cycle around generative AI, a lot of people have kind of lost sight of the fact that AI has been around for a while. And I guess we'll call that classical AI machine learning. And a lot of what you're doing is classical AI. That's a lot. You know, it's deterministic, it's a lot more predictable, and we understand it better than generative AI that's generating new content. And I'm looking, you know, at the tools that you provide. And obviously those are sort of machine learning tested approaches that have been around for a while. But I'm also like it when I see how quickly you can get a customer up and running. I'm imagining you have some generative AI components as well in there. And I was wondering if you could talk about, you know, maybe a breakdown of how you're using the classical AI machine learning in the platform and maybe how you're applying generative AI if you are, or if you could kind of run us through the technology that you guys are using today.
[00:08:16] Guest: Ahikam Kaufman: Actually, that's a great segue to what we do. So thank you for the question. It's actually how you describe it very accurately. So a way to think about that, we developed several capabilities to be able to solve that problem. And I'll explain how we solve the problem. But essentially what we did is we developed a unique graph technology which brings all the financial data and creates a one big scheme or schema and one big graph out of your financial data. And the reason we want to do it, because we want to take that transaction from the CRM and connect it at the same transaction, in billing, at the same transaction in the ERP, at the same transaction in the payment gateway, and maybe the there's some transaction in the supporting documents that you need to monitor, because people today are literally flipping pages and comparing order forms to the data in the CRM for a lot of interesting reasons, but they do that. So. So we created that basic technology with machine learning, and then we run controls over that to be able to identify what looks okay, what looks wrong. And in the process of doing that, we're not just vetting or deciding ourselves. We're actually taking the checks, protocols, all the things that people are doing manually and we just automate them. But that's like the, the ML part, which you talked about what we're doing with AI, we are doing several things. So first of all, we don't just read figures, we read information or data. So think about entries, journal entries or description or expense reports where you have, you know, lingual data, you know, you have like descriptions or whatever.
[00:09:58] Guest: Ahikam Kaufman: And we identify that the description doesn't match something, it doesn't match, the date doesn't match. The amount talks about something else. Maybe an entry is misclassified. So you see like an R&D entry which is in marketing or whatever. So our ability to understand, uh, the description of transactions such as journal entries or expense reports or whatever. Uh, and we can talk about why we're checking expense report, but that's like that's like one thing. The other thing is the ability to ask data questions. So we developed this module where basically, you know, we figured if you're connected to all the data and I'm like the CFO, right. So the CFO doesn't get down to the engine room that often. Right. So it's not like he's not dealing with what we're doing. But if we're connected to all the data, we can answer business questions on your data. So the ability to create a human interface where you can in free text, you can ask a question and we will retrieve. The answer is interesting, by the way, whether you are a CFO or an auditor to that extent, imagine that you have like a door to the entire the ability to. It's like a vista point to the entire data lake of the office of the CFO. Another thing which we're doing, which is very interesting. So turns out we learned in the process of doing that, and that was a very interesting experience that people spend manual time or doing manual work comparing documents to data, which sounds very, very natural and obvious for finance, but they're still doing that.
[00:11:35] Guest: Ahikam Kaufman: So as a, as a mean of control. So for example, at some point when they close their books, they would compare all the as much as many older forms as they can review or MSAs or whatever to how the transaction or the revenue was booked in the books, in, in the CRM or in the books. We automate that. So we use language models to be able to read documents and marry them to controls on the data. So I'd like to think that, you know, it's essential today to use AI. I'll give you another example. When we scan your data or your books, we have like mini models to understand the structure of your transactions. And then we can identify anomalies. We can identify missing transactions. Let's say you get this lease charge every month. All of a sudden you're missing a charge. Obviously you need to accrue that. I know it's all kind of boring stuff, but that's kind of the front and center of finance work, right? We can identify missing data, not just the existing data, but missing data that should have been there. And it's not. Uh, so all these things we're doing, like with real AI and what we're doing with the traditional AI. Back to your question is more like the ability to run statistical queries across the data, find the anomalies, create a graph and all of that.
[00:12:56] Host 2: Glenn Hopper: Okay, Paul, before you're going to need to step in here because I'm fighting the temptation to really geek out here and get into Neo4j's database or whatever with the nodes and edges. Yeah, yeah. So I'm going to I'm going to step back and let Paul ask a normal person a question.
[00:13:12] Guest: Ahikam Kaufman: And I want to give another example. I want to provoke you guys. And to me it's a humbling experience. It's really a humbling experience. But it's like an amazing one. Now, the reason we want to do that is because the amount of data is enormous, right? And human beings, we can't expect them to be able to monitor every single transaction. Now, why do you want to monitor every single transaction? Because for many reasons, one of them is that you want to trust your data, right? So you want everything to be in the right place. If you have a just for example, if you have a transaction which is classified to marketing instead of R&D, you may violate the tax code because Cause is an R&D expense. Maybe you need to amortize something that is a marketing expense you just expensed, right? That's like one time example. The other thing you want to monitor financial data because financial data is money. And if you have leakage we actually can talk about leakage examples that we've identified. You leave money on the table. The staggering one staggering story, which I think is a very interesting one. It's been three months actually, three and a half months ago. Macy's are about to publish, you know, their earnings release.
[00:14:25] Guest: Ahikam Kaufman: That's like the week of Thanksgiving. And what they announced was staggering. They announced that in their books, in the numbers they released already or they were about to release, they identified that they've under accrued for freight accruals. Right. The reason they have a freight cool is because I think the online business they ship from the nearest store. So imagine that all your store basically warehouses, and you have to be able to manage the inventory from that too when you ship online. So you have to manage these costs. And what was amazing. So so they announced that they've under-reacted for like $150 million. And as a result, they had to delay their earnings release. And they opened a special investigation and all of that. I'm not sure they even closed the matter until today, but I'm sure it was a but and I've actually heard that the CFO is an amazing guy. But that's not what I think. Mind blowing. What's mind blowing to me is what they announced to the SEC that it was a result of a single point of failure. So think about an organization like Macy's. You have like 500 people in finance, maybe a thousand people in finance.
[00:15:40] Guest: Ahikam Kaufman: And as a result of a single person who actually did wrongdoing, he didn't take the money. Right. But he didn't. He was trying to hide the problems that was growing. And eventually it blew up. But because of that, Now you think about you as the CFO. How can you know about that? So in today's world, in cyber, in, you know, you have all these controls using automation where you can't ignore issues, right? People see those issues on dashboard, and then they either take care of them or they escalate them. But in finance, it doesn't happen because we're all playing with spreadsheets. So the ability to monitor the data can actually save your career, can actually save the company a lot of money, uh, from, you know, having to deal with compliance issues. And again, it's like you can see that a single point of failure caused the entire company to delay its earnings release on the week of Thanksgiving before the Macy's parade and all of that. And to me, that's like, uh, you know, an eye opening experience and another reason why we need to use more automation in internal controls, which is what we're doing.
[00:16:50] Host: Paul Barnhurst: Yeah. No, it's amazing the mistakes that were made. Sometimes the dollar amounts, the challenges. So, you know, obviously you're helping companies, you know, manage, see all their data, understand that flow. But what particularly you're doing to help make sure they meet kind of the regulatory and compliance demands. Are you loading all those rules in. Because different companies have different things they may have to meet for compliance or regulation. So how do you manage that in the tool with, you know, doing different industries and things to make sure companies are compliant?
[00:17:24] Host: Paul Barnhurst: Ever feel like you go to market teams and finance speak different languages? This misalignment is a breeding ground for failure in pairing the predictive power of forecasts and delaying decisions that drive efficient growth. It's not for lack of trying, but getting all the data in one place doesn't mean you've gotten everyone on the same page. Meet QFlow.ai, the strategic finance platform purpose built to solve the toughest part of planning and analysis B2B revenue. Q flow quickly integrates key data from your go to market stack and accounting platform, then handles all the data prep and normalization. Under the hood, it automatically assembles your go to market stats, makes segmented scenario planning a breeze, and closes the planning loop. Create air tight alignment, improve decision latency, and ensure accountability across the team.
[00:18:32] Guest: Ahikam Kaufman: Right. So I have this great examples that I'm using if that's okay. So um, many years ago I had the chance to, uh, at a large company I worked for. We did a contract with Palantir, and I had the chance to work with the platform. So I'd like to think about what we're doing. We have this platform which is geared towards being able to automate and monitor your financial transactions and automate the monitoring end to end. So what we do, basically we take your existing wallpapers, we take your existing controls and all the checks you're doing today with some of them you do daily, some of them you do weekly, some of them you do monthly, some of them the it takes a lot of work and maybe you do a few times a year, and we just automate that in the system. So a way to think about it. We have the ability to ask the data in question, and we can run it on a daily basis. And as our CEO Steve says all the time I never sleeps. So it works 24 by seven. And this way we monitor every transaction. So we actually created it for the customer. And uh, another principle that we have is we don't like deployments and our customers don't like deployments. So we deploy it for them. But we take their existing protocols, their existing tasks, and we just create these use cases. Because of our technology, we can create a use case in like a matter of hours. So actually we're finding ourselves and I think the other interesting experience is that when the customer understand and realize what we can do, they add more and more because there's always stuff you never get to.
[00:20:11] Guest: Ahikam Kaufman: And now you get to that. So the way we help them is by automating these checks and balances that they would do manually and automate the work papers. So you end up having all your data monitored way before close happens. Right? So if you had an issue and the way the ERP capture the invoice was different than the way it was captured in the ERP, you're not going to find out about it like a half a day before you need to close the books on a Friday afternoon. You're going to find out about it in real time. That's one way we're doing it. The other way is that all the documentation you need to produce, which is like the work papers, right? Essentially half of what? What happens when we say finance? Close the books, they produce wallpapers, they produce all kinds of analytics and reviews and all of that. We automate that. So think about Machine Create for you like these spreadsheets that you're used to. We literally create these spreadsheets and they flag just like in the application. They flag all the transactions you need to pay attention to. And you just get the same spreadsheet you used to produce manually. It's now, you know, it's now automated. So that's how we uh, so at the end of the day, it's all about manual work. It's all about replacing manual work.
[00:21:29] Host: Paul Barnhurst: We like that. As much as I love manual work, I like it being replaced.
[00:21:32] Host 2: Glenn Hopper: Glenn so I come I this is why you're so much smarter than I am. I'm. I've been just trying to explain all of this to the CFO office as if let us go implement this under, you know, under your office, whereas you've gone and created a product that does this because it's I mean, it's not you're basically you're creating your own data pipeline, building your own data lake, setting all this up, whereas I'm trying to help them do that in their office. But to a CFO, this is all we might as well be speaking Greek right now when we start talking about every, you know, what's going on under the hood here. So the fact that you've built this platform and you're able to implement it so quickly, I mean, you're, you know, as much as I do, and I guess what I'm trying to sell is the end product of what their internal sort of, uh, data pipeline and data lake and how they're accessing the data and what they're reporting on. Whereas you can come in with a product and just say, look, give, give me access to your ERP, your CRM, EPM, whatever tools you're getting them in, you're going to build your own data lake, be able to report on it and that, you know, so you are, you know, clearly far ahead. Your capabilities are clearly far beyond what CFOs even understand right now. And I'm wondering, as you talk to CFOs, like, I imagine the sale is not that hard because if they can quickly see the results and the implementation is that easy. But as you talk to CFOs, what's your sense of? Outside of using your product, how AI and automation are going to change the office of the CFO moving moving forward so that they do become more technical and do start using this.
[00:23:10] Guest: Ahikam Kaufman: I want to say a few things about that, but please feel free to weigh in. When you think I'm hitting a point or I'm missing a point, so don't be shy about that. So we would make it interesting. So I'll say a few things. First of all, I'll start from the end. We're definitely seeing more interest in finance teams to adopt AI and innovation. I think it's like after the Deep Sea. So if you like, if you think about after Dopesick and after like January and February, where you're busy closing the month and they're like in the trenches once they came out of it, you see. I would even say that we've heard from a few companies that the finance teams are compensated as part of their 2025 goals to deploy AI and innovation. So it almost feels like overnight. Define the office of the CFO. Doesn't want to stay behind and they compensate people for that. So I think that's like, uh, a unique unique behavior we are seeing. Overall, I would say CFOs are almost divided into two, if I may if I may, I want to say it very humbly, I think you have a lot of CFO which are very strategic. They're coming from investment banking. They're coming from like I've even seen a couple of weeks ago, a CFO of a public company was a partner in a law firm for 20, 20 years.
[00:24:32] Guest: Ahikam Kaufman: So they're very strategic. For whatever reason, these kind of people, they're less aware of internal controls and the importance of internal controls. And then you have CFOs who came from accounting, corporate controllership and all of that, and they get it right away The analogy I'm using with people, I'm telling them, listen, let's say in your house you have, let's say, between 5 to 10 credit cards for whatever, whatever mileage plan and other rewards you have. You have all these credit cards. It's obvious in your house all these credit cards are operated by the right people for the right reasons. But if you don't read the statement to make sure you don't have a false charge or like a fee, or like a double charge or like a fraud, then you. That's the difference between transacting and internal controls. And most people, most of us don't read those credit card statements, because if you do, then. So that but a company has to do that and compliance is not optional. So I'd like to think that those who come from the accounting world, the more and those who come from the investment banking world, it's a more of a learning experience. Or a lot of times they cascade it down to their teams.
[00:25:46] Guest: Ahikam Kaufman: But this is why I talk about manual work, because to me, the way to penetrate. It's not because of, it's just because of the sheer work that we can replace with automation. Right? But definitely now we're sensing more and more in finance team. They need to be a little bit more innovative and deploy AI tools. Now you asked another question: where do you want to deploy these AI tools? I'd like to think that because of hallucinations and other things, the office of the CFO is a very sensitive, dangerous place. I'd like to think that what we do, where we monitor your data, we don't inject any data. We only have a read, only access to the system. We help you actually identify the issues as they are being as they happen. It's a much more convenient way to deploy AI as opposed to because we are not making any decision. We automate your work, we show you where to look for stuff, but then it's your data. You can always investigate that and resolve that. But we don't inject any data. You're not going to wake up and find a bunch of wrong data in your system. And I think that's like a very nice, sweet spot to be in right now for us.
[00:26:57] Host: Paul Barnhurst: So it's not like you're processing any journal entries, doing any write back. You're strictly just kind of doing that, monitoring and alerting.
[00:27:04] Guest: Ahikam Kaufman: If I may use that analogy in its high windows, but you have a lot of AI tools in the, in the medical field, for example, that help you scan MRI or cities, if I may, those systems defined today as decision support systems, by the way, what's their main advantage even? It's an interesting case. But what people realized that even before helping you to understand the specific image you look at, it helps prioritize. So think about a night where a hospital processed 500 people in various whatever devices, and now someone, a radiologist has to review 500 which one he reviews first so the machine can help you just rearrange the line or the queue just to make sure you review those who are more urgent faster. And I think we're just like that, right? We help you focus on what we say. We scanned everything. We are Soc1 certified so we can rely on our controls. But basically you may want to look at these first and just make sure these are okay. And those are like the suspicious things that yeah if it makes sense.
[00:28:13] Host: Paul Barnhurst: Sure. Look at these ten first because we've done everything else looks normal from running it through. These other ones look like they have an issue. So let's start here I triage.
[00:28:23] Host 2: Glenn Hopper: Yeah yeah yeah.
[00:28:25] Host: Paul Barnhurst: No makes sense I mean that's a great analogy right. Because if you look at things you can see if an accrual is three times what it was last month that should flag something. Or if there's an extra zero like it was 10,000, all of a sudden it's 100,000 or.
[00:28:36] Host 2: Glenn Hopper: Something.
[00:28:37] Guest: Ahikam Kaufman: Even more. I'm saying even more than that. I think when you're a public company, again, we can talk about 404 A and 4 or 4 B. I'm sure you're aware of it or you heard the term, but you have a legal obligation to deploy internal controls over financial data. Right. When you now you know. So if before you had like three people doing spreadsheets and someone may have been sick. Right. And as we say, compliance is not optional, right. Even if people are sick, you still need to do that, right? But if the AI runs 24 by seven and you deploy all those controls and you configure the system, you can actually rely on that and you can say, okay, I trusted the system to do this. It flagged that even if we missed something, you still you did your best effort and you made a reasonable effort as required by Sarbanes-Oxley or by the audit standards to monitor your data.
[00:29:32] Host: Paul Barnhurst: Yeah, it makes sense.
[00:29:34] Guest: Ahikam Kaufman: But as we said again, it's a new concept, if you will. Actually, Google financial data governance I'd like to think we're the first UNsponsored result. Now Gartner started to talk about it, uh, recently, just recently before the holidays, and they talk about DNA, data and analytics, governance. And Gartner actually determined that the number one, number one priority for CFOs in 2025. You can Google it up. It's what they call financial data governance or financial DNA. They call it DNA data and analytics governance.
[00:30:10] Host: Paul Barnhurst: Yeah. No it's definitely super important. And yeah I love what you're doing because it's needed. Nobody wants to do it manually. And we're much more likely to make mistakes versus let's let the machines, what they're good at help help us and make our lives easier.
[00:30:29] Guest: Ahikam Kaufman: Does anyone wants to go back to the days before the calculator used the mascot or whatever? No one. Right? People say, hey, but maybe there are less. No one wants to do it anymore. So that's the same. I think after people would had the experience of using us or maybe in the future other platforms, you don't want to go back.
[00:30:46] Host: Paul Barnhurst: Agreed. All right. So we're going to move into this fun section we like to do where we get to know you a little bit. So how it works is we fed your information into AI in this case use cloud. And it came up with 25 fun and unique questions. So here's how I ask them. You get two options. You can either.
[00:31:07] Host: Paul Barnhurst: Have.
[00:31:07] Guest: Ahikam Kaufman: By the way for you it's the fun part. For me it's the scary part I guess.
[00:31:11] Host: Paul Barnhurst: Yes, exactly. So you can use the random number generator and I can generate a number between 1 and 25. Or you can pick a number and we'll ask that question okay 13. 13. All right. Let's see what we have. Give me one second here.
[00:31:27] Host 2: Glenn Hopper: Oh that's a good one.
[00:31:28] Host: Paul Barnhurst: That's why I like this one. This isn't scary. Don't worry if Safe Books I had a company mascot, what would it be? And why.
[00:31:38] Guest: Ahikam Kaufman: Us making life easier with that vogue.
[00:31:42] Host: Paul Barnhurst: That. Yeah, that will give you that one that will work. What do you think, Glenn?
[00:31:45] Host 2: Glenn Hopper: Yep, yep. So, uh, I tell you, Paul and I, we've been doing these questions. We wanted to mix them up, um, every week and not just ask the same questions. So we really used to do bake offs between the different, uh, you know, Claude versus ChatGPT versus Gemini and all that, and now we just kind of mix it up. But I think Claude 3.7 has gotten better. Gemini is still the worst by far. Google. You know, they got to catch up. But so Paul does the random number generator or gives you an option. And what I do on mine is I just, um, since we had them created by a, um, uh, an LLM, I just have it pick one. And so we're going to let no humans in the loop here. Okay. Let's see. So the question that I've got is if you could solve one persistent problem in financial technology with a magic wand, what would it be? And, uh, everybody's talking about how how AI washes companies, and they pretend AI is a magic wand. So I think you maybe already are solving it, but is there one particular problem that, uh, that stands out for you that if you could snap your fingers and fix it, you would.
[00:32:50] Guest: Ahikam Kaufman: I think it's a data reconciliation. Uh, but, you know, it could also be like, you know, all the accounting resolutions, which is something we plan to do in the near future. But, you know, once you capture all the data, how do you make sure that you're following the rules? But that's like you need to know a lot of rules for that. And yeah, the reconciliation is like the low hanging fruit.
[00:33:13] Host 2: Glenn Hopper: Perfect, perfect. So all right, well, this you know, Paul, what this made me think is I need another podcast that's like for the super geeks only like the engineering podcast. And then I feel like we could have a whole other hour conversation right there just talking about the, uh, deep dive.
[00:33:30] Host: Paul Barnhurst: See, I would always glaze over, Glenn, because you'd lose me.
[00:33:35] Host: Paul Barnhurst: That's, you know, that's why you need your.
[00:33:36] Guest: Ahikam Kaufman: I tell you, it's, uh, it's very technical. It's very mechanical. But as we saw in the Macy's case, again, it can be scary. And, uh, I'd like to think that, you know, remote work doesn't help compliance because new members of teams can be less educated. They didn't absorb. So there's a lot of you know, there's we didn't talk about the accountant shortage, if you will, just Google accountant shortage in Bloomberg. You will see staggering numbers. So I'd like to think that, you know, I even have an assertion and it's not politics that with Dodge for example, even if they would cut the workforce, they would double the penalties because they would say, if you get caught, you're going to pay a lot of money. So I'd like to think that, you know, there are a lot, a lot of trends now that require automation. You know, the accountant shortage and remote work are some of the things, maybe even not being able a lot of companies to outsource that work to India and other places they would like to. I think it's better to use automation.
[00:34:42] Host: Paul Barnhurst: So yeah.
[00:34:43] Guest: Ahikam Kaufman: We can deep dive if you want.
[00:34:47] Host: Paul Barnhurst: We'll have to do that next time. What do you think, Glenn? Deep dive next time.
[00:34:50] Host 2: Glenn Hopper: Perfect, perfect. We'll have it for the. And we'll put a warning on it for the nerds only. We'll just.
[00:34:55] Host: Paul Barnhurst: Yeah, yeah yeah, yeah.
[00:34:57] Guest: Ahikam Kaufman: Exercise discretion before.
[00:34:59] Host: Paul Barnhurst: Uh.
[00:34:59] Host 2: Glenn Hopper: That's right.
[00:35:01] Host: Paul Barnhurst: Yeah.
[00:35:01] Host: Paul Barnhurst: We could have, you know, future finance for the regular finance people.
[00:35:05] Host: Paul Barnhurst: But we can talk about it.
[00:35:06] Guest: Ahikam Kaufman: It will still be interesting.
[00:35:08] Host: Paul Barnhurst: Don't worry. Yeah, yeah. No.
[00:35:11] Guest: Ahikam Kaufman: Hey, guys, thank you so much. I can't appreciate that enough. It's been fun.
[00:35:15] Host 2: Glenn Hopper: Yeah. Thank you very much.
[00:35:16] Host: Paul Barnhurst: Thank you for joining us.
[00:35:17] Host: Paul Barnhurst: We've really.
[00:35:18] Host: Paul Barnhurst: Enjoyed it. Thank you so much.
[00:35:20] Host: Paul Barnhurst:Thanks for listening to the Future Finance Show. And thanks to our sponsor, QFlow.ai. If you enjoyed this episode, please leave a rating and review on your podcast platform of choice and may your robot overlords be with you.